"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show(plot(x,y))"
]
},
{
"cell_type": "markdown",
"id": "2ffd85a1",
"metadata": {},
"source": [
"There is much to learn about matplotlib in order to make the plots even prettier and more useful, but that's a story for another tutorial. Meanwhile..."
]
},
{
"cell_type": "markdown",
"id": "9c3cb5df",
"metadata": {},
"source": [
"### Scikit-learn"
]
},
{
"cell_type": "markdown",
"id": "911b8eb6",
"metadata": {},
"source": [
"Now we can import scikit-learn library. \n",
"\n",
"Scikit-learn is a library that helps creating machine learning algorithms. And that's what we'll be doing here.\n",
"\n",
"This example is a bit longer than previous ones, but still, in 12 lines of code we create a machine learning algorithm.\n",
"\n",
"So, we use the scikit-learn in our first more advanced program. We import several things from the library in first 3 lines of code: data helper, machine learning algorithm, and a helper in dividing data).\n",
"\n",
"We use here machine learning (decision trees to be exact) to create a program that can differentiate between different types of Iris flowers. This is very common task to do when you are learning machine learning. And as you can see, it's quite simple. When you run the cell, you can see that you got almost 100% success rate in the task, and you can see how the tree looks like. We'll circle back to that and I'll tell you what exactly you can see here, but for now, you can just enjoy the fact, that you've learned how to create a machine learning program in Python - and how to use machine learning for something useful (kind of). It's like, after this cell, you've just created your first Artificial Intelligence. I'm proud of you. Congratulations."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "de6183b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training result: 1.000\n",
"Test result: 0.974\n"
]
},
{
"data": {
"text/plain": [
"[Text(0.4, 0.9, 'x[3] <= 0.8\\ngini = 0.665\\nsamples = 112\\nvalue = [37, 34, 41]'),\n",
" Text(0.3, 0.7, 'gini = 0.0\\nsamples = 37\\nvalue = [37, 0, 0]'),\n",
" Text(0.5, 0.7, 'x[2] <= 4.95\\ngini = 0.496\\nsamples = 75\\nvalue = [0, 34, 41]'),\n",
" Text(0.2, 0.5, 'x[3] <= 1.65\\ngini = 0.153\\nsamples = 36\\nvalue = [0, 33, 3]'),\n",
" Text(0.1, 0.3, 'gini = 0.0\\nsamples = 32\\nvalue = [0, 32, 0]'),\n",
" Text(0.3, 0.3, 'x[1] <= 3.1\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
" Text(0.2, 0.1, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
" Text(0.4, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
" Text(0.8, 0.5, 'x[3] <= 1.75\\ngini = 0.05\\nsamples = 39\\nvalue = [0, 1, 38]'),\n",
" Text(0.7, 0.3, 'x[3] <= 1.65\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
" Text(0.6, 0.1, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
" Text(0.8, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
" Text(0.9, 0.3, 'gini = 0.0\\nsamples = 35\\nvalue = [0, 0, 35]')]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"iris = load_iris()\n",
"X, y = iris.data, iris.target\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
"\n",
"clf = tree.DecisionTreeClassifier(random_state=0)\n",
"clf = clf.fit(X_train, y_train)\n",
"\n",
"#Print results\n",
"print(\"Training result: {:.3f}\".format(clf.score(X_train, y_train)))\n",
"print(\"Test result: {:.3f}\".format(clf.score(X_test, y_test)))\n",
"\n",
"tree.plot_tree(clf)"
]
},
{
"cell_type": "markdown",
"id": "11adb585",
"metadata": {},
"source": [
"There are other important machine learning libraries, like TensorFlow, PyTorch, Fast AI, Open AI, etc. We won't be installing them here, but you can find here the links to them (as they are at the moment of creating this tutorial) and install them on your own.\n",
"\n",
"Oh, and sometimes it might be better to install them in different environments, because most of the higher level libraries use various libraries under the hood, like numpy, matplotlib, pandas, etc. and they might be using different versions of these libraries, so during installing them you may encounter some problems because of that. Thus, if you don't need two libraries in the same project, you might create different environments for them and choose the one you need when you need it.\n",
"\n",
"And here are the links to these libraries and some more tutorials regarding installing packages, etc.:"
]
},
{
"cell_type": "markdown",
"id": "24bb367e",
"metadata": {},
"source": [
"https://packaging.python.org/en/latest/tutorials/installing-packages/"
]
},
{
"cell_type": "markdown",
"id": "3ed8f066",
"metadata": {},
"source": [
"### TENSORFLOW"
]
},
{
"cell_type": "markdown",
"id": "9ec1929d",
"metadata": {},
"source": [
"https://www.tensorflow.org/install"
]
},
{
"cell_type": "markdown",
"id": "e6c42e10",
"metadata": {},
"source": [
"### PYTORCH"
]
},
{
"cell_type": "markdown",
"id": "e1c28d1b",
"metadata": {},
"source": [
"https://pytorch.org/"
]
},
{
"cell_type": "markdown",
"id": "894a19ee",
"metadata": {},
"source": [
"### FASTAI"
]
},
{
"cell_type": "markdown",
"id": "1fc6d187",
"metadata": {},
"source": [
"https://www.fast.ai/"
]
},
{
"cell_type": "markdown",
"id": "a53fcf78",
"metadata": {},
"source": [
"### OPENAI"
]
},
{
"cell_type": "markdown",
"id": "ffce19fe",
"metadata": {},
"source": [
"https://openai.com/product"
]
},
{
"cell_type": "markdown",
"id": "ff88db45",
"metadata": {},
"source": [
"https://openai.com/research/openai-gym-beta"
]
},
{
"cell_type": "markdown",
"id": "6fe82e4e",
"metadata": {},
"source": [
"## 10. BASICS: Using, understanding and changing types (numeric (int, float, complex), sequence (list, tuple, range), text (str), binary (bytes, bytearray, memoryview), set (set, frozenset), mapping (dict), boolean (bool))"
]
},
{
"cell_type": "markdown",
"id": "92455fee",
"metadata": {},
"source": [
"https://docs.python.org/3/library/stdtypes.html"
]
},
{
"cell_type": "markdown",
"id": "167257d9",
"metadata": {},
"source": [
"And now let's talk about data types in Python."
]
},
{
"cell_type": "markdown",
"id": "248d50c0",
"metadata": {},
"source": [
"According to https://docs.python.org/3/library/stdtypes.html Python has several built-in data types that are used to represent different kinds of data. These data types can be divided into such categories as: numeric, sequence, text, binary, set, mapping, and boolean.\n",
"\n",
"### Types:\n",
"- numeric (int, float, complex), \n",
"- sequence (list, tuple, range),\n",
"- text (str),\n",
"- binary (bytes, bytearray, memoryview),\n",
"- set (set, frozenset), \n",
"- mapping (dict),\n",
"- boolean (bool).\n",
"\n",
"So as you can see these data types are just the types of data you can store and use in your program, these can be letters, numbers, etc.\n",
"\n",
"Sometimes you'll see or hear that there are six basic data types: int, float, complex, bool, str, and bytes. Nevertheless, here we'll follow the docs from Python.org and talk about the types from the documentation.\n",
"\n",
"**Numeric types** include integers, floating-point numbers, and complex numbers. Integers are whole numbers (for example 1, 2, 3) and floating-point numbers have decimal points (for example 1.5, 2.7). Complex numbers are a bit more complex, because they have a real and imaginary part.\n",
"\n",
"**Sequence types** include lists, tuples, and ranges. Lists and tuples are ordered collections of items, the difference is that lists are mutable and tuples are immutable (you can't change them). Ranges are used to represent a sequence of numbers.\n",
"\n",
"**Text type** is used to store Unicode strings and it is represented by the str class.\n",
"\n",
"**Binary types** include bytes, bytearray, and memoryview. The first two: bytes and bytearray are used to store binary data, while memoryview is used to access the memory of other objects.\n",
"\n",
"**Set types** include set and frozenset. The first one: set is an unordered collection of unique items and is mutable, and the second one: frozenset is similar to set but is immutable.\n",
"\n",
"**Mapping types** include dict, which is an unordered collection of key-value pairs.\n",
"\n",
"**Boolean type** can have one of two values: True or False and is represented by the bool class.\n",
"\n",
"So, as you can see there are different types that can be use with different data and tasks. I won't tell you too much about it, but if you want, there is a lot info about them in the Internet. For now, here you can see how they work (some of them, those you might see as a beginner). You may try figuring what will be the ouptut of each cell to better understand them. Have fun."
]
},
{
"cell_type": "markdown",
"id": "374dce6a",
"metadata": {},
"source": [
"### Numeric (int, float, complex)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b7538d4d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = 5 is of the type: .\n",
"b = 5.0 is of the type: .\n"
]
}
],
"source": [
"a = 5\n",
"b = 5.0\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))"
]
},
{
"cell_type": "markdown",
"id": "b70a3058",
"metadata": {},
"source": [
"### Sequence (list, tuple, range)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ea3f9f83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = [1, 2, 3] is of the type: .\n",
"b = (1, 2, 3) is of the type: .\n",
"c = range(0, 3) is of the type: .\n"
]
}
],
"source": [
"a = [1, 2, 3]\n",
"b = (1, 2, 3)\n",
"c = range(3)\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))\n",
"print(\"c = {} is of the type: {}.\".format(c, type(c)))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c4f625f9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a_list: [1, 2, 3]\n",
"a_tupple: (1, 2, 3)\n"
]
}
],
"source": [
"a_list = [1, 2, 3]\n",
"a_tupple = (1, 2, 3)\n",
"print(\"a_list: \", a_list)\n",
"print(\"a_tupple: \", a_tupple)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a498f604",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0th item of the a_list is: 1\n",
"0th item of the a_tupple is: 1\n"
]
}
],
"source": [
"print(\"0th item of the a_list is: \", a_list[0])\n",
"print(\"0th item of the a_tupple is: \", a_tupple[0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2608c13f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 2, 3]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_list[0] = 0\n",
"a_list"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c2dfa7a4",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a_tupple[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 2\u001b[0m a_tupple\n",
"\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"source": [
"a_tupple[0] = 0\n",
"a_tupple"
]
},
{
"cell_type": "markdown",
"id": "bf80749e",
"metadata": {},
"source": [
"### Text (str)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8cb077ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = Jane is of the type: .\n",
"b = Jane is of the type: .\n",
"c = Jane\n",
"and John is of the type: .\n"
]
}
],
"source": [
"a = \"Jane\"\n",
"b = 'Jane'\n",
"c = \"\"\"Jane\n",
"and John\"\"\"\n",
"\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))\n",
"print(\"c = {} is of the type: {}.\".format(c, type(c)))"
]
},
{
"cell_type": "markdown",
"id": "16f9aca7",
"metadata": {},
"source": [
"### Binary"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "00b7595a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = b'Jane' is of the type: .\n",
"b = b'John' is of the type: .\n",
"c = b'Jane \\nand John' is of the type: .\n"
]
}
],
"source": [
"a = b\"Jane\"\n",
"b = b'John'\n",
"c = b\"\"\"Jane \n",
"and John\"\"\"\n",
"\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))\n",
"print(\"c = {} is of the type: {}.\".format(c, type(c)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d71dbf00",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = b'\\x00\\x00\\x00\\x00\\x00' is of the type: .\n",
"b = b'\\x00\\x01\\x02\\x03\\x04\\x05\\x06\\x07\\x08\\t' is of the type: .\n",
"c = b'\\x00\\x00\\x00\\x00\\x00' is of the type: .\n"
]
}
],
"source": [
"a = bytes(5)\n",
"b = bytes(range(10))\n",
"c = bytes(a)\n",
"\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))\n",
"print(\"c = {} is of the type: {}.\".format(c, type(c)))"
]
},
{
"cell_type": "markdown",
"id": "adb8bf9f",
"metadata": {},
"source": [
"### Set (set, frozenset)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9faa7235",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a_set = {'Jane', 'John'} is of the type: .\n"
]
}
],
"source": [
"a_set = {'Jane', 'John', 'Jane', 'John', 'Jane'}\n",
"print(\"a_set = {} is of the type: {}.\".format(a_set, type(a_set)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ae4b6dfc",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "set expected at most 1 argument, got 5",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m first_other \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJane\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJohn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJane\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJohn\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJane\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfirst_other = \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m is of the type: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(first_other, \u001b[38;5;28mtype\u001b[39m(first_other)))\n",
"\u001b[1;31mTypeError\u001b[0m: set expected at most 1 argument, got 5"
]
}
],
"source": [
"first_other = set('Jane', 'John', 'Jane', 'John', 'Jane')\n",
"\n",
"print(\"first_other = {} is of the type: {}.\".format(first_other, type(first_other)))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "cd37b18c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"first_other = {'Jane', 'John'} is of the type: .\n"
]
}
],
"source": [
"first_other = set(('Jane', 'John', 'Jane', 'John', 'Jane'))\n",
"\n",
"print(\"first_other = {} is of the type: {}.\".format(first_other, type(first_other)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ea3e320d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = ('Jane', 'John', 'Jane', 'John', 'Jane') is of the type: .\n",
"another = {'Jane', 'John'} is of the type: .\n"
]
}
],
"source": [
"a = ('Jane', 'John', 'Jane', 'John', 'Jane')\n",
"another = set(a)\n",
"\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"another = {} is of the type: {}.\".format(another, type(another)))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ee210197",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b = ['Jane', 'John', 'Jane', 'John', 'Jane'] is of the type: .\n",
"c = {'Jane', 'John'} is of the type: .\n"
]
}
],
"source": [
"b = ['Jane', 'John', 'Jane', 'John', 'Jane']\n",
"c = set(b)\n",
"\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))\n",
"print(\"c = {} is of the type: {}.\".format(c, type(c)))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9a1ada46",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"d = {' ', \"'\", 'a', 'n', ',', 'J', 'o', 'e', 'h'} is of the type: .\n"
]
}
],
"source": [
"d = set(\"'Jane', 'John', 'Jane', 'John', 'Jane'\")\n",
"\n",
"print(\"d = {} is of the type: {}.\".format(d, type(d)))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f109ea00",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = {'Jane', 'John'} is of the type: .\n",
"e = {'Jane', 'John'} is of the type: .\n"
]
}
],
"source": [
"a = {'Jane', 'John', 'Jane', 'John', 'Jane'}\n",
"e = set(a)\n",
"\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"e = {} is of the type: {}.\".format(e, type(e)))"
]
},
{
"cell_type": "markdown",
"id": "4b37c535",
"metadata": {},
"source": [
"### Mapping (dict)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ee5e45e8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jane': 10, 'John': 9}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = {'Jane': 10, 'John': 9}\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0281614b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: 0, 1: 1, 2: 8}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = {x: x ** 3 for x in range(3)}\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6184733e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jane': 10, 'John': 9}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = dict([('Jane', 10), ('John', 9)])\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "086cb2bb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jane': 10, 'John': 9}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = dict(Jane=10, John=9)\n",
"d"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1d659cfd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: '', 1: 'Jane ', 2: 'Jane Jane '}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e = {x: 'Jane ' * x for x in range(3)}\n",
"e"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "adef4a66",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: '',\n",
" 1: 'Jane ',\n",
" 2: 'Jane Jane ',\n",
" 3: 'Jane Jane Jane ',\n",
" 4: 'Jane Jane Jane Jane '}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e = {x: 'Jane ' * x for x in range(5)}\n",
"e"
]
},
{
"cell_type": "markdown",
"id": "7e391a29",
"metadata": {},
"source": [
"### Boolean (bool)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ac5f05cc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a = True is of the type: .\n",
"b = False is of the type: .\n"
]
}
],
"source": [
"a = True\n",
"b = False\n",
"print(\"a = {} is of the type: {}.\".format(a, type(a)))\n",
"print(\"b = {} is of the type: {}.\".format(b, type(b)))"
]
},
{
"cell_type": "markdown",
"id": "2c77099e",
"metadata": {},
"source": [
"And that's it for now. If you want, you can check other types on your own and meanwhile I would like to tell you about another important thing regarding types that we did in a couple of previous examples."
]
},
{
"cell_type": "markdown",
"id": "7192c700",
"metadata": {},
"source": [
"## Changing types"
]
},
{
"cell_type": "markdown",
"id": "c9292b14",
"metadata": {},
"source": [
"Sometime we might want to or need to change the type, like in our first example.\n",
"\n",
"In our multiplier we were taking input from the user. That input is always of a string type, but in order to multiply the number we needed an int or a float type, so we had to change the type of our recieved input.\n",
"\n",
"This changing with step by step explanation could look like this:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ff00a52d",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"You typed 1 and its type is . \n",
"\n",
"And when we use 'int(your_number)' we get 1 that is .\n"
]
}
],
"source": [
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"real_number = int(your_number)\n",
"print(\"\\nYou typed {} and its type is {}. \\n\\nAnd when we use 'int(your_number)' we get {} that is {}.\".format(\n",
"your_number, type(your_number), real_number, type(real_number)))"
]
},
{
"cell_type": "markdown",
"id": "fc7d4d21",
"metadata": {},
"source": [
"## 11. BASICS: Using Data structures (lists, tuples, dictionaries, sets, etc.)"
]
},
{
"cell_type": "markdown",
"id": "4d765ad1",
"metadata": {},
"source": [
"https://docs.python.org/3/tutorial/datastructures.html"
]
},
{
"cell_type": "markdown",
"id": "b6657858",
"metadata": {},
"source": [
"Now that we know a bit about Python types, let's talk about data structures.\n",
"\n",
"Some of the things you find here might seem familiar to you, because we've covered some of that in the data types part.\n",
"\n",
"Anyway, according to https://docs.python.org/3/tutorial/datastructures.html, Python provides a variety of built-in data structures that can be used to store and manipulate data. \n",
"\n",
"These data structures include lists, tuples, dictionaries, sets, and more. Each of them can be used for different purposes.\n",
"\n",
"Lists are used to store an ordered collection of items and are mutable.\n",
"\n",
"Tuples are similar to lists but are immutable. \n",
"\n",
"Dictionaries are used to store key-value pairs. \n",
"\n",
"Sets are used to store an unordered collection of unique items. \n",
"\n",
"In here you can see how you can work with these different data structres, how you can pick something from them, cut something out, put something in them, etc., but it would be great if you knew more about data structures, so after learning what you have here, try finding more info in the Internet.\n",
"\n",
"You may try figuring out what will happen when you (or I) run the code."
]
},
{
"cell_type": "markdown",
"id": "ac4ccf3a",
"metadata": {},
"source": [
"### LISTS"
]
},
{
"cell_type": "markdown",
"id": "664d01b8",
"metadata": {},
"source": [
"We start with lists."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b98e7077",
"metadata": {},
"outputs": [],
"source": [
"a = [1, 2, 3]"
]
},
{
"cell_type": "markdown",
"id": "72f1ae11",
"metadata": {},
"source": [
"And print parts of it..."
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f0953166",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3]\n",
"1\n",
"3\n",
"[1]\n",
"[1]\n",
"[2, 3]\n",
"[2, 3]\n",
"[1, 2, 3]\n",
"[]\n"
]
}
],
"source": [
"print(a[:])\n",
"print(a[0])\n",
"print(a[-1])\n",
"print(a[0:1])\n",
"print(a[:1])\n",
"print(a[1:])\n",
"print(a[-2:])\n",
"print(a[-5:10])\n",
"print(a[-5:-10])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "efed2a0a",
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[27], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(a[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5\u001b[39m])\n",
"\u001b[1;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"print(a[-5])"
]
},
{
"cell_type": "markdown",
"id": "8b549ad5",
"metadata": {},
"source": [
"We pop one item from the list."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e3f31927",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "f7ec430f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "markdown",
"id": "479470e4",
"metadata": {},
"source": [
"And we change and add something to our list."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "d6d5370f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 2, 4]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0] = 0\n",
"a.append(4)\n",
"a"
]
},
{
"cell_type": "markdown",
"id": "3348534f",
"metadata": {},
"source": [
"### TUPLES"
]
},
{
"cell_type": "markdown",
"id": "0d569fc4",
"metadata": {},
"source": [
"Now, let's see how the tuples act."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2daf26d5",
"metadata": {},
"outputs": [],
"source": [
"a = (1, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "5058dc40",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 2, 3)\n",
"1\n",
"3\n",
"(1,)\n",
"(1,)\n",
"(2, 3)\n",
"(2, 3)\n",
"(1, 2, 3)\n",
"()\n"
]
}
],
"source": [
"print(a[:])\n",
"print(a[0])\n",
"print(a[-1])\n",
"print(a[0:1])\n",
"print(a[:1])\n",
"print(a[1:])\n",
"print(a[-2:])\n",
"print(a[-5:10])\n",
"print(a[-5:-10])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "719f6e9d",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[33], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 2\u001b[0m a\n",
"\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"source": [
"a[0] = 0\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "42606be0",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'tuple' object has no attribute 'append'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[34], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;241m4\u001b[39m)\n\u001b[0;32m 2\u001b[0m a\n",
"\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
]
}
],
"source": [
"a.append(4)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "efc70911",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.count(2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "f12e980d",
"metadata": {},
"outputs": [],
"source": [
"a = (1, 2, 2, 3)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "b6159b3f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.count(2)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "050e86ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.index(2)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "9599e82b",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "tuple.index(x): x not in tuple",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[39], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mindex(\u001b[38;5;241m4\u001b[39m)\n",
"\u001b[1;31mValueError\u001b[0m: tuple.index(x): x not in tuple"
]
}
],
"source": [
"a.index(4)"
]
},
{
"cell_type": "markdown",
"id": "afc80064",
"metadata": {},
"source": [
"As you can see, there are some methods you could have used with lists, but you can't use them with tuples, because they are immutable. There are, however, other methods which you can use with tuples."
]
},
{
"cell_type": "markdown",
"id": "bc3c6f10",
"metadata": {},
"source": [
"### DICTIONARIES"
]
},
{
"cell_type": "markdown",
"id": "7248e432",
"metadata": {},
"source": [
"Now let's check the dictionaries."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "8e945101",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jane': 8, 'John': 9}"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = {'Jane': 10, 'John': 9, 'Jane': 8}\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "a68dafe7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(a)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "2298713b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jane': 10, 'John': 9, 'Jenny': 8}"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = {'Jane': 10, 'John': 9, 'Jenny': 8}\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "5ddb9007",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "unhashable type: 'slice'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[43], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(a[:])\n",
"\u001b[1;31mTypeError\u001b[0m: unhashable type: 'slice'"
]
}
],
"source": [
"print(a[:])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "9f194f75",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"print(a['Jane'])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "898b5db5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['Jane', 'John', 'Jenny'])\n"
]
}
],
"source": [
"print(a.keys())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "0f1e0d58",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_values([10, 9, 8])\n"
]
}
],
"source": [
"print(a.values())"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "6b9fa0b0",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "pop expected at least 1 argument, got 0",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[47], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mpop()\n",
"\u001b[1;31mTypeError\u001b[0m: pop expected at least 1 argument, got 0"
]
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "f88dbd76",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop(\"Jane\")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "3654c2d2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop(\"John\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "f381958e",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'John'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[50], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJohn\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mKeyError\u001b[0m: 'John'"
]
}
],
"source": [
"a.pop(\"John\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "c16316f7",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'Jane'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[51], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJane\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mKeyError\u001b[0m: 'Jane'"
]
}
],
"source": [
"a.pop(\"Jane\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "0b412516",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jenny': 8}"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "1f3aa7d2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jenny': 8, 'Jane': 10}"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a['Jane']= 10\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "4441580d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jenny': 8, 'Jane': 10, ('John', 'Jane'): (9, 7)}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a['John', 'Jane'] = (9, 7)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "917c9e61",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jenny': 8, 'Jane': 10, ('John', 'Jane'): (7, 9)}"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[('John'), ('Jane')] = (7, 9)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "3d962447",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jenny': 8, 'Jane': 10, ('John', 'Jane'): (7, 9), 'Johnny': 6}"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.update({'Johnny': 6})\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "d71c3154",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jenny': 8, 'Jane': 10, ('John', 'Jane'): (7, 9), 'Johnny': 5}"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.update({'Johnny': 5})\n",
"a"
]
},
{
"cell_type": "markdown",
"id": "86ae036d",
"metadata": {},
"source": [
"And once again, there are some things we could have done with other data structures that we can't do with dictionaries, but there are also some new methods that we can use only with dictionaries."
]
},
{
"cell_type": "markdown",
"id": "7daba816",
"metadata": {},
"source": [
"### SETS"
]
},
{
"cell_type": "markdown",
"id": "f28afc1d",
"metadata": {},
"source": [
"And now the sets."
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "0cda1fdf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Jane', 'John'} \n"
]
}
],
"source": [
"a = {'Jane', 'John', 'Jane', 'John', 'Jane'}\n",
"print(a, type(a))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "8f4872de",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'set' object is not subscriptable",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[59], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(a[:])\n",
"\u001b[1;31mTypeError\u001b[0m: 'set' object is not subscriptable"
]
}
],
"source": [
"print(a[:])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "cf8895c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Jane'"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "c72f9767",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'John'"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "575b9bd0",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'pop from an empty set'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[62], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m a\u001b[38;5;241m.\u001b[39mpop()\n",
"\u001b[1;31mKeyError\u001b[0m: 'pop from an empty set'"
]
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "ca571017",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'J', 'h', 'n', 'o'}"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.update(\"John\")\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "101d1584",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"set()"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.clear()\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "21713afd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Jane', 'John'}"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.update([\"John\", \"Jane\"])\n",
"a"
]
},
{
"cell_type": "markdown",
"id": "0111b2e3",
"metadata": {},
"source": [
"And that's it about these basic data structures. I hope you liked these simple examples. There is of course much more to learn, but that's for the future. Remember that you can always type help(something) (for example help(set) and you will see what else you can do with that something (for example set)."
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "2daddd80",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class set in module builtins:\n",
"\n",
"class set(object)\n",
" | set() -> new empty set object\n",
" | set(iterable) -> new set object\n",
" | \n",
" | Build an unordered collection of unique elements.\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __and__(self, value, /)\n",
" | Return self&value.\n",
" | \n",
" | __contains__(...)\n",
" | x.__contains__(y) <==> y in x.\n",
" | \n",
" | __eq__(self, value, /)\n",
" | Return self==value.\n",
" | \n",
" | __ge__(self, value, /)\n",
" | Return self>=value.\n",
" | \n",
" | __getattribute__(self, name, /)\n",
" | Return getattr(self, name).\n",
" | \n",
" | __gt__(self, value, /)\n",
" | Return self>value.\n",
" | \n",
" | __iand__(self, value, /)\n",
" | Return self&=value.\n",
" | \n",
" | __init__(self, /, *args, **kwargs)\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | __ior__(self, value, /)\n",
" | Return self|=value.\n",
" | \n",
" | __isub__(self, value, /)\n",
" | Return self-=value.\n",
" | \n",
" | __iter__(self, /)\n",
" | Implement iter(self).\n",
" | \n",
" | __ixor__(self, value, /)\n",
" | Return self^=value.\n",
" | \n",
" | __le__(self, value, /)\n",
" | Return self<=value.\n",
" | \n",
" | __len__(self, /)\n",
" | Return len(self).\n",
" | \n",
" | __lt__(self, value, /)\n",
" | Return self size of S in memory, in bytes\n",
" | \n",
" | __sub__(self, value, /)\n",
" | Return self-value.\n",
" | \n",
" | __xor__(self, value, /)\n",
" | Return self^value.\n",
" | \n",
" | add(...)\n",
" | Add an element to a set.\n",
" | \n",
" | This has no effect if the element is already present.\n",
" | \n",
" | clear(...)\n",
" | Remove all elements from this set.\n",
" | \n",
" | copy(...)\n",
" | Return a shallow copy of a set.\n",
" | \n",
" | difference(...)\n",
" | Return the difference of two or more sets as a new set.\n",
" | \n",
" | (i.e. all elements that are in this set but not the others.)\n",
" | \n",
" | difference_update(...)\n",
" | Remove all elements of another set from this set.\n",
" | \n",
" | discard(...)\n",
" | Remove an element from a set if it is a member.\n",
" | \n",
" | Unlike set.remove(), the discard() method does not raise\n",
" | an exception when an element is missing from the set.\n",
" | \n",
" | intersection(...)\n",
" | Return the intersection of two sets as a new set.\n",
" | \n",
" | (i.e. all elements that are in both sets.)\n",
" | \n",
" | intersection_update(...)\n",
" | Update a set with the intersection of itself and another.\n",
" | \n",
" | isdisjoint(...)\n",
" | Return True if two sets have a null intersection.\n",
" | \n",
" | issubset(self, other, /)\n",
" | Test whether every element in the set is in other.\n",
" | \n",
" | issuperset(self, other, /)\n",
" | Test whether every element in other is in the set.\n",
" | \n",
" | pop(...)\n",
" | Remove and return an arbitrary set element.\n",
" | Raises KeyError if the set is empty.\n",
" | \n",
" | remove(...)\n",
" | Remove an element from a set; it must be a member.\n",
" | \n",
" | If the element is not a member, raise a KeyError.\n",
" | \n",
" | symmetric_difference(...)\n",
" | Return the symmetric difference of two sets as a new set.\n",
" | \n",
" | (i.e. all elements that are in exactly one of the sets.)\n",
" | \n",
" | symmetric_difference_update(...)\n",
" | Update a set with the symmetric difference of itself and another.\n",
" | \n",
" | union(...)\n",
" | Return the union of sets as a new set.\n",
" | \n",
" | (i.e. all elements that are in either set.)\n",
" | \n",
" | update(...)\n",
" | Update a set with the union of itself and others.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Class methods defined here:\n",
" | \n",
" | __class_getitem__(...) from builtins.type\n",
" | See PEP 585\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Static methods defined here:\n",
" | \n",
" | __new__(*args, **kwargs) from builtins.type\n",
" | Create and return a new object. See help(type) for accurate signature.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data and other attributes defined here:\n",
" | \n",
" | __hash__ = None\n",
"\n"
]
}
],
"source": [
"help(set)"
]
},
{
"cell_type": "markdown",
"id": "4bbab277",
"metadata": {},
"source": [
"Meanwhile, we can also check more advanced data structres from numpy and pandas. \n",
"\n",
"And again, try figuring out the output before you see it."
]
},
{
"cell_type": "markdown",
"id": "c54f0a33",
"metadata": {},
"source": [
"### Numpy Array"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "eb74206e",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "1bf27476",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0.],\n",
" [0., 0., 0.]])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.zeros([2,3])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "b2abfc75",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0.])"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "1c493e81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0.]])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:1]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "827d5f49",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0.])"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:, 0]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "aa4eaee6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0,0]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "300c409c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.1, 0. , 0. ],\n",
" [0. , 0. , 0. ]])"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0,0] = 1.1\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "873f55be",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.1, 2. , 0. ],\n",
" [0. , 2. , 0. ]])"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:, 1] = 2\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "05cbd280",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.1, 2. , 0. ],\n",
" [3. , 3. , 3. ]])"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[-1, :] = 3\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "1d8f8c16",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1.1, 2. , 3. ])"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = a[0, :]\n",
"a[2] = 3\n",
"a"
]
},
{
"cell_type": "markdown",
"id": "3473a04f",
"metadata": {},
"source": [
"### Pandas DataFrame"
]
},
{
"cell_type": "markdown",
"id": "f51da351",
"metadata": {},
"source": [
"And now let's check pandas DataFrame, very important in machine learning.\n",
"\n",
"By the way, here you can see how you can import something from pandas library."
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "2af85589",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "b1bafacd",
"metadata": {},
"outputs": [],
"source": [
"data = pd.DataFrame([[1,2,3], [4,5,6]])"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "2320774e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39melements\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\io\\formats\\html.py:94\u001b[0m, in \u001b[0;36mHTMLFormatter.render\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrender\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m---> 94\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write_table()\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshould_show_dimensions:\n\u001b[0;32m 97\u001b[0m by \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mchr\u001b[39m(\u001b[38;5;241m215\u001b[39m) \u001b[38;5;66;03m# × # noqa: RUF003\u001b[39;00m\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\io\\formats\\html.py:269\u001b[0m, in \u001b[0;36mHTMLFormatter._write_table\u001b[1;34m(self, indent)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mheader \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_row_idx_names:\n\u001b[0;32m 267\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write_header(indent \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindent_delta)\n\u001b[1;32m--> 269\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write_body(indent \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindent_delta)\n\u001b[0;32m 271\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\u001b[39m\u001b[38;5;124m\"\u001b[39m, indent)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\io\\formats\\html.py:417\u001b[0m, in \u001b[0;36mHTMLFormatter._write_body\u001b[1;34m(self, indent)\u001b[0m\n\u001b[0;32m 415\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_write_body\u001b[39m(\u001b[38;5;28mself\u001b[39m, indent: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 416\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\u001b[39m\u001b[38;5;124m\"\u001b[39m, indent)\n\u001b[1;32m--> 417\u001b[0m fmt_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_formatted_values()\n\u001b[0;32m 419\u001b[0m \u001b[38;5;66;03m# write values\u001b[39;00m\n\u001b[0;32m 420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframe\u001b[38;5;241m.\u001b[39mindex, MultiIndex):\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\io\\formats\\html.py:607\u001b[0m, in \u001b[0;36mNotebookFormatter._get_formatted_values\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 606\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_formatted_values\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]]:\n\u001b[1;32m--> 607\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {i: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mformat_col(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mncols)}\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\io\\formats\\html.py:607\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 606\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_formatted_values\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]]:\n\u001b[1;32m--> 607\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {i: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mformat_col(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mncols)}\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\io\\formats\\format.py:755\u001b[0m, in \u001b[0;36mDataFrameFormatter.format_col\u001b[1;34m(self, i)\u001b[0m\n\u001b[0;32m 752\u001b[0m frame \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtr_frame\n\u001b[0;32m 753\u001b[0m formatter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_formatter(i)\n\u001b[0;32m 754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m format_array(\n\u001b[1;32m--> 755\u001b[0m frame\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39m_values,\n\u001b[0;32m 756\u001b[0m formatter,\n\u001b[0;32m 757\u001b[0m float_format\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfloat_format,\n\u001b[0;32m 758\u001b[0m na_rep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mna_rep,\n\u001b[0;32m 759\u001b[0m space\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcol_space\u001b[38;5;241m.\u001b[39mget(frame\u001b[38;5;241m.\u001b[39mcolumns[i]),\n\u001b[0;32m 760\u001b[0m decimal\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecimal,\n\u001b[0;32m 761\u001b[0m leading_space\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex,\n\u001b[0;32m 762\u001b[0m )\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:1184\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[0;32m 1183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[1;32m-> 1184\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[0;32m 1185\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1186\u001b[0m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[0;32m 1187\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:1692\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 1690\u001b[0m tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tuple_indexer(tup)\n\u001b[0;32m 1691\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[1;32m-> 1692\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n\u001b[0;32m 1694\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple_same_dim(tup)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:1065\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_lowerdim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 1061\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tup):\n\u001b[0;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_label_like(key):\n\u001b[0;32m 1063\u001b[0m \u001b[38;5;66;03m# We don't need to check for tuples here because those are\u001b[39;00m\n\u001b[0;32m 1064\u001b[0m \u001b[38;5;66;03m# caught by the _is_nested_tuple_indexer check above.\u001b[39;00m\n\u001b[1;32m-> 1065\u001b[0m section \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(key, axis\u001b[38;5;241m=\u001b[39mi)\n\u001b[0;32m 1067\u001b[0m \u001b[38;5;66;03m# We should never have a scalar section here, because\u001b[39;00m\n\u001b[0;32m 1068\u001b[0m \u001b[38;5;66;03m# _getitem_lowerdim is only called after a check for\u001b[39;00m\n\u001b[0;32m 1069\u001b[0m \u001b[38;5;66;03m# is_scalar_access, which that would be.\u001b[39;00m\n\u001b[0;32m 1070\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m section\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim:\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;66;03m# we're in the middle of slicing through a MultiIndex\u001b[39;00m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;66;03m# revise the key wrt to `section` by inserting an _NS\u001b[39;00m\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:1754\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1751\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[0;32m 1752\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_integer(key, axis)\n\u001b[1;32m-> 1754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\frame.py:3998\u001b[0m, in \u001b[0;36mDataFrame._ixs\u001b[1;34m(self, i, axis)\u001b[0m\n\u001b[0;32m 3994\u001b[0m \u001b[38;5;66;03m# icol\u001b[39;00m\n\u001b[0;32m 3995\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 3996\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns[i]\n\u001b[1;32m-> 3998\u001b[0m col_mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39miget(i)\n\u001b[0;32m 3999\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_box_col_values(col_mgr, i)\n\u001b[0;32m 4001\u001b[0m \u001b[38;5;66;03m# this is a cached value, mark it so\u001b[39;00m\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1016\u001b[0m, in \u001b[0;36mBlockManager.iget\u001b[1;34m(self, i, track_ref)\u001b[0m\n\u001b[0;32m 1012\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miget\u001b[39m(\u001b[38;5;28mself\u001b[39m, i: \u001b[38;5;28mint\u001b[39m, track_ref: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m SingleBlockManager:\n\u001b[0;32m 1013\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1014\u001b[0m \u001b[38;5;124;03m Return the data as a SingleBlockManager.\u001b[39;00m\n\u001b[0;32m 1015\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1016\u001b[0m block \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblknos[i]]\n\u001b[0;32m 1017\u001b[0m values \u001b[38;5;241m=\u001b[39m block\u001b[38;5;241m.\u001b[39miget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblklocs[i])\n\u001b[0;32m 1019\u001b[0m \u001b[38;5;66;03m# shortcut for select a single-dim from a 2-dim BM\u001b[39;00m\n",
"\u001b[1;31mIndexError\u001b[0m: index 4 is out of bounds for axis 0 with size 4"
]
}
],
"source": [
"data.iloc[:, 1] = 7\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "0bab748c",
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Length of values (4) does not match length of index (5)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[90], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, :] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8\u001b[39m\n\u001b[0;32m 2\u001b[0m data\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:911\u001b[0m, in \u001b[0;36m_LocationIndexer.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 908\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_valid_setitem_indexer(key)\n\u001b[0;32m 910\u001b[0m iloc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miloc\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39miloc\n\u001b[1;32m--> 911\u001b[0m iloc\u001b[38;5;241m.\u001b[39m_setitem_with_indexer(indexer, value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:1942\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m 1939\u001b[0m \u001b[38;5;66;03m# align and set the values\u001b[39;00m\n\u001b[0;32m 1940\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m take_split_path:\n\u001b[0;32m 1941\u001b[0m \u001b[38;5;66;03m# We have to operate column-wise\u001b[39;00m\n\u001b[1;32m-> 1942\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_with_indexer_split_path(indexer, value, name)\n\u001b[0;32m 1943\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1944\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_single_block(indexer, value, name)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:2035\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer_split_path\u001b[1;34m(self, indexer, value, name)\u001b[0m\n\u001b[0;32m 2032\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 2033\u001b[0m \u001b[38;5;66;03m# scalar value\u001b[39;00m\n\u001b[0;32m 2034\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m loc \u001b[38;5;129;01min\u001b[39;00m ilocs:\n\u001b[1;32m-> 2035\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_single_column(loc, value, pi)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\indexing.py:2164\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_single_column\u001b[1;34m(self, loc, value, plane_indexer)\u001b[0m\n\u001b[0;32m 2160\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39misetitem(loc, value)\n\u001b[0;32m 2161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 2162\u001b[0m \u001b[38;5;66;03m# set value into the column (first attempting to operate inplace, then\u001b[39;00m\n\u001b[0;32m 2163\u001b[0m \u001b[38;5;66;03m# falling back to casting if necessary)\u001b[39;00m\n\u001b[1;32m-> 2164\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mdtypes\u001b[38;5;241m.\u001b[39miloc[loc]\n\u001b[0;32m 2165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mvoid:\n\u001b[0;32m 2166\u001b[0m \u001b[38;5;66;03m# This means we're expanding, with multiple columns, e.g.\u001b[39;00m\n\u001b[0;32m 2167\u001b[0m \u001b[38;5;66;03m# df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2170\u001b[0m \u001b[38;5;66;03m# Here, we replace those temporary `np.void` columns with\u001b[39;00m\n\u001b[0;32m 2171\u001b[0m \u001b[38;5;66;03m# columns of the appropriate dtype, based on `value`.\u001b[39;00m\n\u001b[0;32m 2172\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39miloc[:, loc] \u001b[38;5;241m=\u001b[39m construct_1d_array_from_inferred_fill_value(\n\u001b[0;32m 2173\u001b[0m value, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m 2174\u001b[0m )\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\generic.py:6458\u001b[0m, in \u001b[0;36mNDFrame.dtypes\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 6431\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 6432\u001b[0m \u001b[38;5;124;03mReturn the dtypes in the DataFrame.\u001b[39;00m\n\u001b[0;32m 6433\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 6455\u001b[0m \u001b[38;5;124;03mdtype: object\u001b[39;00m\n\u001b[0;32m 6456\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 6457\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mget_dtypes()\n\u001b[1;32m-> 6458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_sliced(data, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mobject_)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\series.py:575\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m 573\u001b[0m index \u001b[38;5;241m=\u001b[39m default_index(\u001b[38;5;28mlen\u001b[39m(data))\n\u001b[0;32m 574\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(data):\n\u001b[1;32m--> 575\u001b[0m com\u001b[38;5;241m.\u001b[39mrequire_length_match(data, index)\n\u001b[0;32m 577\u001b[0m \u001b[38;5;66;03m# create/copy the manager\u001b[39;00m\n\u001b[0;32m 578\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, (SingleBlockManager, SingleArrayManager)):\n",
"File \u001b[1;32m~\\anaconda3\\envs\\Python101\\Lib\\site-packages\\pandas\\core\\common.py:573\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m 569\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 570\u001b[0m \u001b[38;5;124;03mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[0;32m 571\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 572\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index):\n\u001b[1;32m--> 573\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 574\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of values \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 575\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 576\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoes not match length of index \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 577\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(index)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 578\u001b[0m )\n",
"\u001b[1;31mValueError\u001b[0m: Length of values (4) does not match length of index (5)"
]
}
],
"source": [
"data.iloc[-1, :] = 8\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "feb7d840",
"metadata": {},
"outputs": [],
"source": [
"data = data.iloc[0, :]"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "443b68ea",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\micro\\AppData\\Local\\Temp\\ipykernel_2600\\1621459581.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" data[2] = 9\n"
]
}
],
"source": [
"data[2] = 9"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "37880311",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.0\n",
"1 7.0\n",
"2 9.0\n",
"0 1.1\n",
"0 3.0\n",
"Name: 0, dtype: float64"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "a5c4e871",
"metadata": {},
"source": [
"And yet again some new methods unique for each data structure... That's why it's so important to learn about each one of them.\n",
"\n",
"But for now that's it about data structures. \n",
"\n",
"There is a lot to say about them and it's really important to understand how they work under the hood, but I'll let you learn it by yourself, because this tutorial is long enough as it is.\n",
"\n",
"And now let's move on to the boolean logic."
]
},
{
"cell_type": "markdown",
"id": "836d7701",
"metadata": {},
"source": [
"## 12. BASICS: Boolean Logic"
]
},
{
"cell_type": "markdown",
"id": "541ea486",
"metadata": {},
"source": [
"Boolean logic allows to make comparisons and execute conditional statements. \n",
"\n",
"Boolean values can be either True or False. \n",
"\n",
"Boolean expressions are used in if and while statements to control the flow of a program. \n",
"\n",
"There are also several Boolean operators, like and, or, not, which can be used to manipulate Boolean values. \n",
"\n",
"And here is some of that logic.\n",
"\n",
"And as previously. What do you think is going to happen here? 3... 2... 1... Go."
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "7382f60f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"True == True"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "a584a912",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"False == False"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "6aaef021",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"True == False"
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "f912633d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 == 2"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "de1fd15d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 < 2"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "3bcb0d7b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 > 2"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "bdb47ec1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"John\" == \"John\""
]
},
{
"cell_type": "code",
"execution_count": 101,
"id": "32ac911c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"John\" == \"john\""
]
},
{
"cell_type": "code",
"execution_count": 102,
"id": "87e19714",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"John\" > \"john\""
]
},
{
"cell_type": "code",
"execution_count": 103,
"id": "3e70651a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"john\" > \"John\""
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "da8e12dc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"John\" > \"Jane\""
]
},
{
"cell_type": "code",
"execution_count": 105,
"id": "a65c9bbc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"John\" > \"jane\""
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "b9d2e071",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"j\" > \"J\""
]
},
{
"cell_type": "markdown",
"id": "242ea016",
"metadata": {},
"source": [
"Now if you wonder about the outcomes in these string comparisons, let me tell you couple of things. \n",
"\n",
"String comparison is a way of determining the relative order, equality, or difference of two or more strings. \n",
"\n",
"In here, for example when we are using the > operator to compare two strings: \"John\" and \"jane\", this operator returns True if the first string is lexicographically greater than the second string, and False otherwise. Lexicographically means according to the alphabetical order or dictionary order of the characters in the strings.\n",
"\n",
"To compare two strings using the > operator, Python compares the characters in both strings one by one, starting from the leftmost position. When it finds different characters, it compares their Unicode code point values. The character with the lower Unicode value is considered smaller, and the character with the higher Unicode value is considered larger. If one string is a prefix of another string, then the longer string is considered larger.\n",
"\n",
"In here, the first characters of both strings are different: \"upper J\" and \"lower j\". The Unicode value of \"upper J\" is 74, and the Unicode value of \"lower j\" is 106. Since 74 is smaller than 106, \"upper J\" is smaller than \"lower j\". Therefore, \"John\" is smaller than \"jane\" (written with lower j), and \"John\" > \"jane\" is False.\n",
"\n",
"If you want to compare two strings without considering the case of the characters, you can use the str.lower() or str.upper() methods to convert both strings to the same case before comparing them.\n",
"\n",
"And that's it about it. Now let's move on to the flow control of the program."
]
},
{
"cell_type": "markdown",
"id": "0a44770f",
"metadata": {},
"source": [
"## 13. FLOW CONTROL OF THE PROGRAM: Using conditions to control the flow of the program, like: If ... elif... else"
]
},
{
"cell_type": "markdown",
"id": "3d2daccb",
"metadata": {},
"source": [
"Flow control of the program is the ability to change the order or sequence of instructions based on some conditions. \n",
"\n",
"It's like, normally program is executed from top to bottom, but sometimes we want to run only part of the program based on some condition or we want to run the same part for 2 or more times, etc. Then we could use some helper.\n",
"\n",
"Flow control is essential for creating dynamic and interactive programs that can respond to user input, data, or events.\n",
"\n",
"One of the most common ways to control the flow of the program is using conditional statements, such as if, elif, and else. These statements allow the program to check if a certain condition is true or false, and then execute a block of code accordingly. \n",
"\n",
"The if statement is followed by a condition, which can be either True or False. If the condition is met (True, for example 1==1), the block of code indented under the if statement is executed. If the condition isn't met (is False, for example 1==0), the program moves on to the next statement, which could be another if statement, an elif statement, or an else statement.\n",
"\n",
"The elif statement is short for \"else if\", and it is used to check another condition after the previous one fails. The elif statement also has a condition and a block of code to execute if the condition is true. There can be multiple elif statements in a sequence, but only one of them can be executed at most.\n",
"\n",
"The else statement is used to provide a default action when none of the previous conditions are met (True). The else statement does not have a condition, and it is executed only if all the preceding if and elif statements are False. There can be only one else statement in a sequence, and it is optional.\n",
"\n",
"Conditional statements are the building blocks of other flow control structures, such as loops.\n",
"\n",
"And here you can see how you can, for example, use these conditions in your code."
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "381c1eea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 is 0\n"
]
}
],
"source": [
"a = 0\n",
"if a == 0:\n",
" print(a, \"is 0\")\n",
"else:\n",
" print(a, \"is not equal 0\")"
]
},
{
"cell_type": "code",
"execution_count": 108,
"id": "d9ab8d9a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1 is not equal 0\n"
]
}
],
"source": [
"a = -1\n",
"if a == 0:\n",
" print(a, \"is 0\")\n",
"else:\n",
" print(a, \"is not equal 0\")"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "1f130fe5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 is not equal 0\n"
]
}
],
"source": [
"a = 1\n",
"if a == 0:\n",
" print(a, \"is 0\")\n",
"else:\n",
" print(a, \"is not equal 0\")"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "2003312a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 is 0\n"
]
}
],
"source": [
"a = 0\n",
"if a == 0:\n",
" print(a, \"is 0\")\n",
"elif a < 0:\n",
" print(a, \"is less than 0\")\n",
"else:\n",
" print(a, \"is bigger than 0\")"
]
},
{
"cell_type": "code",
"execution_count": 111,
"id": "464c67aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1 is less than 0\n"
]
}
],
"source": [
"a = -1\n",
"if a == 0:\n",
" print(a)\n",
"elif a < 0:\n",
" print(a, \"is less than 0\")\n",
"else:\n",
" print(a, \"is bigger than 0\")"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "fd040b8b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 is bigger than 0\n"
]
}
],
"source": [
"a = 1\n",
"if a == 0:\n",
" print(a)\n",
"elif a < 0:\n",
" print(a, \"is less than 0\")\n",
"else:\n",
" print(a, \"is bigger than 0\")"
]
},
{
"cell_type": "markdown",
"id": "1c108301",
"metadata": {},
"source": [
"## 14. FLOW CONTROL OF THE PROGRAM: Using loops to control the flow of the program: for, while"
]
},
{
"cell_type": "markdown",
"id": "e1051914",
"metadata": {},
"source": [
"And now let's move on to loops.\n",
"\n",
"Loops are one of the ways to control the flow of the program by repeating a block of code until a certain condition is met. There are two types of loops in Python: for and while. \n",
"\n",
"A for loop is used to iterate over a sequence, such as a list, a tuple, a string, or a range. \n",
"\n",
"A while loop is used to execute a block of code as long as a given condition is true. \n",
"\n",
"And here is how they work."
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "afcb1536",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n"
]
}
],
"source": [
"for a in range(5):\n",
" print(a)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "b15e5da5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n"
]
}
],
"source": [
"for a in [1, 2, 3]:\n",
" print(a)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"id": "fdaed300",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 2 times 2 is: 4\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 3 times 2 is: 6\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "8516b367",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n"
]
}
],
"source": [
"a = 0\n",
"while a < 5:\n",
" print(a)\n",
" a += 1"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "f36e8a9c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n"
]
}
],
"source": [
"a = 0\n",
"while a < 5:\n",
" a += 1\n",
" print(a)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "b9153b85",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 2 times 2 is: 4\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 3 times 2 is: 6\n"
]
}
],
"source": [
"a = 0\n",
"while a < 3:\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")\n",
" a += 1"
]
},
{
"cell_type": "markdown",
"id": "c368bf25",
"metadata": {},
"source": [
"## 15. FLOW CONTROL OF THE PROGRAM: Using break, continue, and pass to control the flow of the program"
]
},
{
"cell_type": "markdown",
"id": "46ec615f",
"metadata": {},
"source": [
"Now, beside if conditions and loops we have the break, continue, and pass statements that can help us control the flow of a program in Python. \n",
"\n",
"They can be used for different tasks.\n",
"\n",
"The break statement terminates the current loop and resumes execution at the next statement.\n",
"\n",
"The continue statement skips the rest of the current iteration and moves to the next one.\n",
"\n",
"The pass statement does nothing and acts as a placeholder for future code.\n",
"\n",
"Break and continue statements can help you exit a loop when a condition is met, while pass statements can help you avoid syntax errors or mark unfinished code.\n",
"\n",
"If it's all a bit abstract for you, here you can see how you can use them. Try thinkging what and when will be the outcome of each cell, what will be printed out. And remember, always try testing different scenarios, so you can see what happen in all conditions (like in here, you can try typing: positive number, negative number, a word, etc. when you are prompted by a computer)."
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "ed96776a",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1 is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "f44d667e",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" break\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(\"I'm after the break\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "f5d37dce",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n",
"Type me a positive integral number(use numbers, not words): 1\n",
"Type me a positive integral number(use numbers, not words): -1\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" continue\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(\"I'm after the break\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "31b3ace5",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm after the break\n",
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm after the break\n",
"-1 is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" pass\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(\"I'm after the break\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "a6880396",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" break\n",
" print(\"I'm after the break\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "226f239b",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm before the break. Moving on to break and... \n",
"\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(\"I'm before the break. Moving on to break and... \\n\")\n",
" break\n",
" print(\"I'm after the break\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "5a59f2ab",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n",
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" continue\n",
" print(\"I'm after the continue\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 126,
"id": "2743fc07",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm before the continue. Moving on to continue and... \n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm before the continue. Moving on to continue and... \n",
"\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(\"I'm before the continue. Moving on to continue and... \\n\")\n",
" continue\n",
" print(\"I'm after the continue\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 127,
"id": "977a7aa4",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm after the pass\n",
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm after the pass\n",
"-1 is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" pass\n",
" print(\"I'm after the pass\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "code",
"execution_count": 128,
"id": "b28fa196",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm before the pass. Moving on to pass and... \n",
"\n",
"I'm after the pass\n",
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm before the pass. Moving on to pass and... \n",
"\n",
"I'm after the pass\n",
"-1 is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" if your_number.isdigit():\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" else:\n",
" print(\"I'm before the pass. Moving on to pass and... \\n\")\n",
" pass\n",
" print(\"I'm after the pass\")\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\")"
]
},
{
"cell_type": "markdown",
"id": "db2f10bf",
"metadata": {},
"source": [
"## 16. FLOW CONTROL OF THE PROGRAM: Handling errors with exceptions"
]
},
{
"cell_type": "markdown",
"id": "e0c08ec6",
"metadata": {},
"source": [
"Now that you know all that, let's handle some errors with exceptions.\n",
"\n",
"As you know, while programming you may make a lot of mistakes or an end user may act not as one should, etc., so handling different kind of errors is crucial for robust and good working programs, thus exceptions.\n",
"\n",
"Exceptions are a way of handling errors that interrupt the normal flow of the program. Exceptions are raised when an invalid operation is performed, such as accessing a non-existent key in a dictionary, opening a non-existent file or dividing by zero.\n",
"\n",
"To handle exceptions, Python provides the try and except block, which allows the program to execute some code and catch any errors that may occur. The try block contains the code that may raise an exception, and the except block contains the code that will run if an exception is raised.\n",
"\n",
"There are many built-in exceptions that cover different types of errors in Python, such as FileNotFoundError, IndexError, KeyError, SyntaxError, TypeError, etc. \n",
"\n",
"You can also create your own custom exceptions with an error message or a cause, that can be accessed through the exception object.\n",
"\n",
"And here are some examples how you can use them. First we try running the program without handling errors. Try figuring out what will happen."
]
},
{
"cell_type": "code",
"execution_count": 129,
"id": "fcf2725b",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"ename": "ValueError",
"evalue": "invalid literal for int() with base 10: 'a'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[129], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m your_number \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mType me a positive integral number(use numbers, not words): \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m real_number \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(your_number)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYour number: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m your_number \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m times 2 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m, real_number\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
"\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'"
]
}
],
"source": [
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"real_number = int(your_number)\n",
"print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"id": "fbc96af3",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"ename": "ValueError",
"evalue": "invalid literal for int() with base 10: 'a'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[130], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m your_number \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mType me a positive integral number(use numbers, not words): \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m real_number \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(your_number)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYour number: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m your_number \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m times 2 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m, real_number\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
"\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a'"
]
}
],
"source": [
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"real_number = int(your_number)\n",
"print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)"
]
},
{
"cell_type": "markdown",
"id": "e7abc7f3",
"metadata": {},
"source": [
"And now we use try and except blocks."
]
},
{
"cell_type": "code",
"execution_count": 131,
"id": "504d982e",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
}
],
"source": [
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"try:\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
"except:\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\") "
]
},
{
"cell_type": "code",
"execution_count": 132,
"id": "1132f844",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
}
],
"source": [
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"try:\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
"except:\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\") "
]
},
{
"cell_type": "code",
"execution_count": 133,
"id": "d0c36d79",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"a is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: -1 times 2 is: -2\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 times 2 is: 2\n"
]
}
],
"source": [
"for a in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" try:\n",
" real_number = int(your_number)\n",
" print(\"Your number: \" + your_number + \" times 2 is:\", real_number*2)\n",
" except:\n",
" print(your_number + \" is not a positive integral number written as digit. But, that's ok. Maybe next time. Goodbye.\") "
]
},
{
"cell_type": "markdown",
"id": "eb80e7ca",
"metadata": {},
"source": [
"Now, as you can see, this program doesn't act as previous ones. It multiplies -2. Can you guess why it's like this? It shouldn't be difficult for you to catch the difference at the momemnt. I believe in you. "
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "b6421d3e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Let me divide one number by another here.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n",
"Type me another number(use numbers, not words): 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 divided by 2 is: 0.5\n"
]
}
],
"source": [
"print(\"Let me divide one number by another here.\\n\")\n",
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"your_2nd_number = input(\"Type me another number(use numbers, not words): \")\n",
"real_number = int(your_number)\n",
"real_2nd_number = int(your_2nd_number)\n",
"print(\"Your number: \" + your_number + \" divided by \" + your_2nd_number + \" is:\", real_number / real_2nd_number)"
]
},
{
"cell_type": "code",
"execution_count": 135,
"id": "ab753cb7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Let me divide one number by another here.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n",
"Type me another number(use numbers, not words): 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 divided by 2 is: 0.5\n"
]
}
],
"source": [
"print(\"Let me divide one number by another here.\\n\")\n",
"your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
"your_2nd_number = input(\"Type me another number(use numbers, not words): \")\n",
"try:\n",
" real_number = int(your_number)\n",
" real_2nd_number = int(your_2nd_number)\n",
" print(\"Your number: \" + your_number + \" divided by \" + your_2nd_number + \" is:\", real_number / real_2nd_number)\n",
"except:\n",
" print(\"\"\"Either one of your numbers is not an integral number written as digit or you tried dividing by 0. \\\n",
"But, that's ok. Maybe next time. Goodbye.\"\"\") "
]
},
{
"cell_type": "code",
"execution_count": 136,
"id": "120eb3e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Let me divide one number by another here.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 1\n",
"Type me another number(use numbers, not words): 2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 1 divided by 2 is: 0.5\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): a\n",
"Type me another number(use numbers, not words): -1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"One of your numbers is not an integral number written as digit. But, that's ok. Maybe next time. Goodbye.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Type me a positive integral number(use numbers, not words): 0\n",
"Type me another number(use numbers, not words): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your number: 0 divided by 1 is: 0.0\n"
]
}
],
"source": [
"print(\"Let me divide one number by another here.\\n\")\n",
"for i in range(3):\n",
" your_number = input(\"Type me a positive integral number(use numbers, not words): \")\n",
" your_2nd_number = input(\"Type me another number(use numbers, not words): \")\n",
" try:\n",
" real_number = int(your_number)\n",
" real_2nd_number = int(your_2nd_number)\n",
" print(\"Your number: \" + your_number + \" divided by \" + your_2nd_number + \" is:\", real_number / real_2nd_number)\n",
" except ZeroDivisionError:\n",
" print(\"You tried dividing by 0. But, that's ok. Maybe next time. Goodbye.\") \n",
" except:\n",
" print(\"One of your numbers is not an integral number written as digit. But, that's ok. Maybe next time. Goodbye.\") "
]
},
{
"cell_type": "markdown",
"id": "077ef805",
"metadata": {},
"source": [
"As you can see, they can help and your program can react differently depending on the Error. That can be very useful sometimes. And that's it about that for now."
]
},
{
"cell_type": "markdown",
"id": "40aa3bcb",
"metadata": {},
"source": [
"## 17. BASICS+: Basic info about iterators"
]
},
{
"cell_type": "markdown",
"id": "ef65153f",
"metadata": {},
"source": [
"Ok. Now, let's move on to iterators.\n",
"\n",
"They are objects that can be iterated over, that means that you can access each element in a sequence one by one. \n",
"\n",
"You can use the iter() function to get an iterator from any iterable object, such as lists, tuples, strings, etc. \n",
"\n",
"You can also use the for loop to iterate over an iterator implicitly.\n",
"\n",
"You can use it for example when you want to teach your machine learning program and give it some examples to learn (a batch of examples), after that you might want to give it the second batch, third batch, etc. And that's where iterators come handy, for example. We might talk more about it in the future tutorials, but for now...\n",
"\n",
"Here are some simpler examples."
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "690682bc",
"metadata": {},
"outputs": [],
"source": [
"a = \"Iterators can be helpful.\""
]
},
{
"cell_type": "code",
"execution_count": 138,
"id": "8de297d1",
"metadata": {},
"outputs": [],
"source": [
"iter_a = iter(a)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"id": "6f1a2df8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'I'"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(iter_a)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "a0c7b9b1",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t\n",
"e\n",
"r\n",
"a\n",
"t\n",
"o\n",
"r\n",
"s\n",
" \n",
"c\n",
"a\n",
"n\n",
" \n",
"b\n",
"e\n",
" \n",
"h\n",
"e\n",
"l\n",
"p\n",
"f\n",
"u\n",
"l\n",
".\n"
]
}
],
"source": [
"for i in range(len(a)-1):\n",
" print(next(iter_a))"
]
},
{
"cell_type": "markdown",
"id": "0255ed97",
"metadata": {},
"source": [
"The same goes for tuples, lists, sets, and dictionaries"
]
},
{
"cell_type": "code",
"execution_count": 141,
"id": "7944ca05",
"metadata": {},
"outputs": [],
"source": [
"a = \"String\"\n",
"b = ('T', 'u', 'p', 1, 2, 3)\n",
"c = [\"Jane\", 'John', 4, 5, 6, 'seven']\n",
"d = {\"John\", 'Jane', \"String\", 8, 9, False}\n",
"e = {\"Jane\": 1, \"John\": 2, 3: \"Jane\", 4: \"John\", True: True, False: False}"
]
},
{
"cell_type": "code",
"execution_count": 142,
"id": "b0647441",
"metadata": {},
"outputs": [],
"source": [
"iter_a = iter(a)\n",
"iter_b = iter(b)\n",
"iter_c = iter(c)\n",
"iter_d = iter(d)\n",
"iter_e = iter(e)"
]
},
{
"cell_type": "markdown",
"id": "43a5a889",
"metadata": {},
"source": [
"Do you know what will be the output of this cell?"
]
},
{
"cell_type": "code",
"execution_count": 143,
"id": "0dd721c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"S T Jane False Jane\n",
"t u John Jane John\n",
"r p 4 8 3\n",
"i 1 5 9 4\n",
"n 2 6 String True\n",
"g 3 seven John False\n"
]
}
],
"source": [
"for i in range(6):\n",
" print(next(iter_a), next(iter_b), next(iter_c), next(iter_d), next(iter_e))"
]
},
{
"cell_type": "markdown",
"id": "87018f71",
"metadata": {},
"source": [
"Did you guess correctly?"
]
},
{
"cell_type": "markdown",
"id": "065e9779",
"metadata": {},
"source": [
"## 18. BASICS+: Creating functions (simple and recursive) and using lambda function"
]
},
{
"cell_type": "markdown",
"id": "859b7359",
"metadata": {},
"source": [
"Ok. Now let's talk about functions.\n",
"\n",
"Functions are another important concept in Python. They allow you to reuse a piece of code throughout your program. They are like a form or a recipe you use to make 100 cookies that look the same and taste the same.\n",
"\n",
"A simple function is defined using the def keyword, followed by the function name and parentheses. Inside the parentheses, you can optionally specify one or more parameters, separated by commas. After the parentheses, you need to add a colon and then indent the function body. The function body is the code that will be executed when the function is called. \n",
"\n",
"A recursive function is a function that calls itself inside its body. Recursive functions are useful for solving problems that can be broken down into smaller subproblems of the same kind. One of the most common example of using a recursive function is calculating the factorial of a number, you can check the internet if you want to learn more about it.\n",
"\n",
"And here are some simple examples for beginners, starting with printing hello.\n",
"\n",
"At first we check if this kind of functions already exists?"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "c1b54ba2",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'print_hello' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[144], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m print_hello()\n",
"\u001b[1;31mNameError\u001b[0m: name 'print_hello' is not defined"
]
}
],
"source": [
"print_hello()"
]
},
{
"cell_type": "markdown",
"id": "37e30be0",
"metadata": {},
"source": [
"And as it doesn't, we can create it. Just remember, while creating functions, you can name it almost however you like, but you should stick to the PEP 8 guidelines (https://peps.python.org/pep-0008/#function-and-variable-names) to make it easier to read for others. So, you should type it with lowercase letters, start with a letter or an underscore, you can put underscores between the words if necessary, etc.\n",
"\n",
"If you create functions try naming them in the way someone could understand the meaning of the function just be reading that name. You can also put some comments or explanaitions of the function inside it, because, remember, you might forget...\n",
"\n",
"So here we go..."
]
},
{
"cell_type": "code",
"execution_count": 145,
"id": "220f1ca4",
"metadata": {},
"outputs": [],
"source": [
"def print_hello():\n",
" print(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": 146,
"id": "dbee840e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
}
],
"source": [
"print_hello()"
]
},
{
"cell_type": "markdown",
"id": "ebcaa40b",
"metadata": {},
"source": [
"And it works. Great."
]
},
{
"cell_type": "markdown",
"id": "d7d14ffa",
"metadata": {},
"source": [
"Now, let's create a function that takes one argument for proper work. The argument will have to be put in the parentheses when one wants to run the function. It might look like this whith and without argument."
]
},
{
"cell_type": "code",
"execution_count": 147,
"id": "7d241f4f",
"metadata": {},
"outputs": [],
"source": [
"def print_hello_someone(someone):\n",
" print(\"hello {}\".format(someone))"
]
},
{
"cell_type": "code",
"execution_count": 148,
"id": "4660666c",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "print_hello_someone() missing 1 required positional argument: 'someone'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[148], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m print_hello_someone()\n",
"\u001b[1;31mTypeError\u001b[0m: print_hello_someone() missing 1 required positional argument: 'someone'"
]
}
],
"source": [
"print_hello_someone()"
]
},
{
"cell_type": "code",
"execution_count": 149,
"id": "33e30c74",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello Jane\n"
]
}
],
"source": [
"print_hello_someone(\"Jane\")"
]
},
{
"cell_type": "markdown",
"id": "2c116482",
"metadata": {},
"source": [
"This argument can be also given by the user with simple input function:"
]
},
{
"cell_type": "code",
"execution_count": 150,
"id": "04a74e80",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your name? Type it here please: A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"hello A\n"
]
}
],
"source": [
"name = input(\"What is your name? Type it here please: \")\n",
"print()\n",
"print_hello_someone(name)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"id": "87e8268c",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your name? Type it here please: A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"hello A\n"
]
}
],
"source": [
"name = input(\"What is your name? Type it here please: \")\n",
"print()\n",
"print_hello_someone(name)"
]
},
{
"cell_type": "markdown",
"id": "72b87d98",
"metadata": {},
"source": [
"If you can use it in an even more complicated way, you might need to use return method of the function to return output of the function and put it to use later, like in the given example.\n",
"\n",
"Let's recreate our function that will return something to us, not just print it out."
]
},
{
"cell_type": "code",
"execution_count": 152,
"id": "a742b526",
"metadata": {},
"outputs": [],
"source": [
"def print_hello_someone_variable(someone):\n",
" return \"Hello {}\".format(someone)"
]
},
{
"cell_type": "markdown",
"id": "1c869add",
"metadata": {},
"source": [
"And use it like we used it before."
]
},
{
"cell_type": "code",
"execution_count": 153,
"id": "0c3d7c6d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello Jane'"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print_hello_someone_variable(\"Jane\")"
]
},
{
"cell_type": "markdown",
"id": "6ec583e2",
"metadata": {},
"source": [
"And now, let's try something more advanced with our old function. Can you guess the output?"
]
},
{
"cell_type": "code",
"execution_count": 154,
"id": "c447c010",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your name? Type it here please: A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"hello A\n",
"None , it's nice to meet you!\n"
]
}
],
"source": [
"name = input(\"What is your name? Type it here please: \")\n",
"print()\n",
"print(print_hello_someone(name), \", it's nice to meet you!\")"
]
},
{
"cell_type": "markdown",
"id": "8ccd7e3f",
"metadata": {},
"source": [
"As you can see, it's not exactly what one could want. So let's try it with our newer fucntion with return."
]
},
{
"cell_type": "code",
"execution_count": 155,
"id": "268344af",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your name? Type it here please: A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Hello A , it's nice to meet you!\n"
]
}
],
"source": [
"name = input(\"What is your name? Type it here please: \")\n",
"print()\n",
"print(print_hello_someone_variable(name), \", it's nice to meet you!\")"
]
},
{
"cell_type": "markdown",
"id": "a8f4e3aa",
"metadata": {},
"source": [
"Now it works better. And if we want to store the output of our function, we can also do it now. So let's check it out. First with the old function."
]
},
{
"cell_type": "code",
"execution_count": 156,
"id": "5da6423b",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your name? Type it here please: A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello A\n",
"\n",
"None, it's nice to meet you!\n"
]
}
],
"source": [
"name = input(\"What is your name? Type it here please: \")\n",
"hello_variable = print_hello_someone(name)\n",
"print()\n",
"print(\"{}, it's nice to meet you!\".format(hello_variable))"
]
},
{
"cell_type": "markdown",
"id": "6fc5e915",
"metadata": {},
"source": [
"And now with the new one."
]
},
{
"cell_type": "code",
"execution_count": 157,
"id": "ba15b373",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your name? Type it here please: A\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Hello A, it's nice to meet you!\n"
]
}
],
"source": [
"name = input(\"What is your name? Type it here please: \")\n",
"hello_variable = print_hello_someone_variable(name)\n",
"print()\n",
"print(\"{}, it's nice to meet you!\".format(hello_variable))"
]
},
{
"cell_type": "markdown",
"id": "e09ad823",
"metadata": {},
"source": [
"As you can see, there are different ways you can use functions. Sometimes you might want to have function return something, other times, you might only want it to print something out or do something. You can use them as you want, just think what you need the function for while you are creating it and do it accordingly."
]
},
{
"cell_type": "markdown",
"id": "60575baa",
"metadata": {},
"source": [
"One more thing about using arguments with functions. Till now we had only positional arguments here, but we can also have something called keyword (named) arguments, that are the arguments with a value attached to them, like here:"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "a2438d86",
"metadata": {},
"outputs": [],
"source": [
"def func_with_arguments(animal, age, weight=\">1\"):\n",
" print(\"The {} lives to {} years old and weights {} kg.\".format(animal, age, weight))"
]
},
{
"cell_type": "code",
"execution_count": 159,
"id": "92726a30",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The cat lives to 30 years old and weights 5 kg.\n"
]
}
],
"source": [
"func_with_arguments(\"cat\", \"30\", \"5\")"
]
},
{
"cell_type": "code",
"execution_count": 160,
"id": "7bc602d1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The 30 lives to cat years old and weights 5 kg.\n"
]
}
],
"source": [
"func_with_arguments(\"30\", \"cat\", \"5\")"
]
},
{
"cell_type": "code",
"execution_count": 161,
"id": "b17dee7c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The cat lives to 30 years old and weights >1 kg.\n"
]
}
],
"source": [
"func_with_arguments(\"cat\", \"30\")"
]
},
{
"cell_type": "code",
"execution_count": 162,
"id": "fd90c159",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "func_with_arguments() missing 1 required positional argument: 'age'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[162], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m func_with_arguments(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcat\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mTypeError\u001b[0m: func_with_arguments() missing 1 required positional argument: 'age'"
]
}
],
"source": [
"func_with_arguments(\"cat\")"
]
},
{
"cell_type": "code",
"execution_count": 163,
"id": "3cec8567",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The 30 lives to cat years old and weights 5 kg.\n"
]
}
],
"source": [
"func_with_arguments(\"30\", \"cat\", weight=\"5\")"
]
},
{
"cell_type": "code",
"execution_count": 164,
"id": "c1aabefb",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "positional argument follows keyword argument (4188793690.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m Cell \u001b[1;32mIn[164], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m func_with_arguments(\"30\", weight=\"5\", \"cat\")\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
]
}
],
"source": [
"func_with_arguments(\"30\", weight=\"5\", \"cat\")"
]
},
{
"cell_type": "markdown",
"id": "d6e7c1a9",
"metadata": {},
"source": [
"As you can see, you don't have to put the keyword argument while using function, because you have the stored value of that argument and it will be used as a default. And when you put that argument it will overwrite the default.\n",
"\n",
"Also, the position of the argument matters, so if you put your arguments in wrong order you might get some errors. You also always have to put positional arguments before keyword arguments.\n",
"\n",
"There is more to learn about it, but for now it's enough."
]
},
{
"cell_type": "markdown",
"id": "f3d27e7c",
"metadata": {},
"source": [
"And now let's see how you can create a function that uses other functions."
]
},
{
"cell_type": "code",
"execution_count": 165,
"id": "43587ea5",
"metadata": {},
"outputs": [],
"source": [
"def print_all(someone):\n",
" print_hello_someone(someone)\n",
" print_hello()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"id": "9ae079af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello Jane\n",
"hello\n"
]
}
],
"source": [
"print_all(\"Jane\")"
]
},
{
"cell_type": "markdown",
"id": "b03a8231",
"metadata": {},
"source": [
"So, it worked.\n",
"\n",
"Now."
]
},
{
"cell_type": "markdown",
"id": "f31298e0",
"metadata": {},
"source": [
"With functions you can also create easier to read programs, when you start the program with main function within which you put the main code of the program. You follow it by all the functions you want to use during the program. And after that you run the main function. Here is how it may look like."
]
},
{
"cell_type": "code",
"execution_count": 167,
"id": "366cc59b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
},
{
"ename": "NameError",
"evalue": "name 'sth' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[167], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhello\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m sth()\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgoodbye\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msth\u001b[39m():\n",
"\u001b[1;31mNameError\u001b[0m: name 'sth' is not defined"
]
}
],
"source": [
"print('hello')\n",
"sth()\n",
"print (\"goodbye\")\n",
"\n",
"def sth():\n",
" print(\"something\")"
]
},
{
"cell_type": "markdown",
"id": "c051980c",
"metadata": {},
"source": [
"And it didn't work. Why? Because you need to have the function created before you use it, so you can either reorganize your code like this:"
]
},
{
"cell_type": "code",
"execution_count": 168,
"id": "443a7313",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"something\n",
"goodbye\n"
]
}
],
"source": [
"def sth():\n",
" print(\"something\")\n",
" \n",
"print('hello')\n",
"sth()\n",
"print (\"goodbye\")"
]
},
{
"cell_type": "markdown",
"id": "06044d88",
"metadata": {},
"source": [
"And now it works. \n",
"\n",
"Or, if you want to have the main body of your program on the top, you might want to reorganize that program using main function, like this:"
]
},
{
"cell_type": "code",
"execution_count": 169,
"id": "b18be1c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"something\n",
"goodbye\n"
]
}
],
"source": [
"def main():\n",
" print('hello')\n",
" \n",
" sth()\n",
" \n",
" print (\"goodbye\")\n",
"\n",
"def sth():\n",
" print(\"something\")\n",
" \n",
"main()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"id": "3fb9fd6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"something\n",
"goodbye\n"
]
}
],
"source": [
"def __main__():\n",
" print('hello')\n",
" \n",
" sth()\n",
" \n",
" print (\"goodbye\")\n",
"\n",
"def sth():\n",
" print(\"something\")\n",
" \n",
"if __name__ == \"__main__\":\n",
" __main__()"
]
},
{
"cell_type": "markdown",
"id": "4b301820",
"metadata": {},
"source": [
"Or even like this... Can you guess the outcome of these two?"
]
},
{
"cell_type": "code",
"execution_count": 171,
"id": "9760f23a",
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" print('hello')\n",
" \n",
" sth()\n",
" \n",
" print (\"goodbye\")\n",
"\n",
"def sth():\n",
" print(\"something\")\n",
" \n",
"if __name__ == \"main\":\n",
" main()"
]
},
{
"cell_type": "code",
"execution_count": 172,
"id": "14511b9a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"something\n",
"goodbye\n"
]
}
],
"source": [
"def main():\n",
" print('hello')\n",
" \n",
" sth()\n",
" \n",
" print (\"goodbye\")\n",
"\n",
"def sth():\n",
" print(\"something\")\n",
" \n",
"if __name__ == \"__main__\":\n",
" main()"
]
},
{
"cell_type": "markdown",
"id": "09fbbb20",
"metadata": {},
"source": [
"Now, I won't tell you more about underscores in front or in the back, one or more, because that's a bit more advanced topic for now, but if you want to learn more about it, check the internet."
]
},
{
"cell_type": "markdown",
"id": "4da6a020",
"metadata": {},
"source": [
"Now, let's move to lambda function, which is a small anonymous function that can be defined in a single line. Lambda functions are also known as anonymous functions or lambda expressions. \n",
"\n",
"They are functions which don't have they names, they are used only in the one place in the code, and you can use several lambda functions in your code without breaking it.\n",
"\n",
"The lambda keyword indicates that you are creating a lambda function. It is followed by one or more parameters, separated by commas. After the colon, you write the expression that will be evaluated and returned when the lambda function is called. \n",
"\n",
"You can assign a lambda function to a variable and use it like a regular function, or you can use it directly as an argument to another function.\n",
"\n",
"And here you have simple examples of these. Can you guess their outcome?"
]
},
{
"cell_type": "code",
"execution_count": 173,
"id": "a13b92a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'JaneJane'"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anonymous = lambda par_a: par_a * 2\n",
"anonymous(\"Jane\")"
]
},
{
"cell_type": "code",
"execution_count": 174,
"id": "17594719",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anonymous = lambda par_a: par_a * 2\n",
"anonymous(4)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"id": "9f1a4abf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'Jane': 'John'}, {'Jane': 'John'}, {'Jane': 'John'}]"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anonymous = lambda par_a, par_b, par_c: [{par_a: par_b}] * par_c\n",
"anonymous(\"Jane\", \"John\", 3)"
]
},
{
"cell_type": "code",
"execution_count": 176,
"id": "e7251890",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'Jane': 'John'}, {'Jane': 'John'}, {'Jane': 'John'}]"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anonymous = lambda par_a: par_a * 2\n",
"anonymous(\"Jane\")\n",
"\n",
"anonymous = lambda par_a, par_b, par_c: [{par_a: par_b}] * par_c\n",
"anonymous(\"Jane\", \"John\", 3)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"id": "3b67403b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"JaneJane\n",
"[{'Jane': 'John'}, {'Jane': 'John'}, {'Jane': 'John'}]\n"
]
}
],
"source": [
"anonymous = lambda par_a: par_a * 2\n",
"print(anonymous(\"Jane\"))\n",
"\n",
"anonymous = lambda par_a, par_b, par_c: [{par_a: par_b}] * par_c\n",
"print(anonymous(\"Jane\", \"John\", 3))"
]
},
{
"cell_type": "markdown",
"id": "3e4d6b04",
"metadata": {},
"source": [
"And that's it about functions for a moment. There is much more to learn about them (like using arbitrary arguments, decorators, etc.), but I think it's a lot for the beginners. So let's stop here with them and move on to another topic. Classes."
]
},
{
"cell_type": "markdown",
"id": "83896ca7",
"metadata": {},
"source": [
"## 19. BASICS+: Basic info about classes, inheritance, and object-oriented programming"
]
},
{
"cell_type": "markdown",
"id": "b9768e1b",
"metadata": {},
"source": [
"https://docs.python.org/3/tutorial/classes.html"
]
},
{
"cell_type": "markdown",
"id": "eabc06e9",
"metadata": {},
"source": [
"Classes and inheritance are some of the core concepts of object-oriented programming (OOP). OOP is a paradigm that organizes data and behavior into reusable and modular units called objects.\n",
"\n",
"There is much to learn about classes, so I would recommend you reading the documentation: https://docs.python.org/3/tutorial/classes.html. \n",
"\n",
"And here, I'll just show you how they work in practice.\n",
"\n",
"As always, try thinking what you expect to happen, before running the cell or watching me run the cell.\n",
"\n",
"And that being said, let's start with creating a simple class."
]
},
{
"cell_type": "code",
"execution_count": 178,
"id": "2eff7a6f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Object was created\n"
]
}
],
"source": [
"class Object:\n",
" print('Object was created')"
]
},
{
"cell_type": "markdown",
"id": "171560f3",
"metadata": {},
"source": [
"Now that we have class created with which we can create some objects."
]
},
{
"cell_type": "code",
"execution_count": 179,
"id": "146c6d11",
"metadata": {},
"outputs": [],
"source": [
"Object1 = Object()"
]
},
{
"cell_type": "markdown",
"id": "2bd86b64",
"metadata": {},
"source": [
"We've created the first object. Nothing special happened."
]
},
{
"cell_type": "markdown",
"id": "76531c95",
"metadata": {},
"source": [
"We can attach some info to that object."
]
},
{
"cell_type": "code",
"execution_count": 180,
"id": "5c890a01",
"metadata": {},
"outputs": [],
"source": [
"Object1.size = 'big'"
]
},
{
"cell_type": "code",
"execution_count": 181,
"id": "38f23881",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"big\n"
]
}
],
"source": [
"print(Object1.size)"
]
},
{
"cell_type": "markdown",
"id": "14a3cee3",
"metadata": {},
"source": [
"Well, it's not much, but it's a start."
]
},
{
"cell_type": "markdown",
"id": "9bf7beb4",
"metadata": {},
"source": [
"Now, if we want to add that info about object during creating that object we will need something more."
]
},
{
"cell_type": "markdown",
"id": "d2a3200f",
"metadata": {},
"source": [
"### INIT with necessary arguments"
]
},
{
"cell_type": "markdown",
"id": "194487db",
"metadata": {},
"source": [
"We can create a class with necessary size info added to each object.\n",
"\n",
"We do it using __init__.\n",
"\n",
"It is a special method called constructor. And, by the way, methods are just functions inside classes. You can have a lot of them in the class, but there is only one init, that is run when the object of the class (an instance) is created.\n",
"\n",
"Here you can see that in action - when it works and when it doesn't."
]
},
{
"cell_type": "code",
"execution_count": 182,
"id": "0d69ac58",
"metadata": {},
"outputs": [],
"source": [
"class Object_with_size:\n",
" def __init__(self, size):\n",
" print('Object was created')"
]
},
{
"cell_type": "code",
"execution_count": 183,
"id": "bc8b9c7d",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Object_with_size.__init__() missing 1 required positional argument: 'size'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[183], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Object3 \u001b[38;5;241m=\u001b[39m Object_with_size()\n",
"\u001b[1;31mTypeError\u001b[0m: Object_with_size.__init__() missing 1 required positional argument: 'size'"
]
}
],
"source": [
"Object3 = Object_with_size()"
]
},
{
"cell_type": "markdown",
"id": "1981a0e0",
"metadata": {},
"source": [
"Now, as you can see, creating an object didn't work. That is because we've stated in the init constructor that we require 'size' argument of an object. It's like with functions. When you need an argument, the program won't work if you don't give one to the functions.\n",
"\n",
"Here, you might see 2 arguments in the init constructor, but the error showed that we are missing only one, that's because the first argument - self - is always added by class itself, it's the object we've created (self). So we do need to put it there while creating the constructor in the class body, but we don't need to think about it during object creation later in the program.\n",
"\n",
"So, let's create our object properly this time."
]
},
{
"cell_type": "code",
"execution_count": 184,
"id": "45d965d1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Object was created\n"
]
}
],
"source": [
"Object3 = Object_with_size('big')"
]
},
{
"cell_type": "markdown",
"id": "aa09d299",
"metadata": {},
"source": [
"And we have our object. So let's try checking if we can acess the size of an object."
]
},
{
"cell_type": "code",
"execution_count": 185,
"id": "59d9bc8c",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Object_with_size' object has no attribute 'size'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[185], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Object3\u001b[38;5;241m.\u001b[39msize\n",
"\u001b[1;31mAttributeError\u001b[0m: 'Object_with_size' object has no attribute 'size'"
]
}
],
"source": [
"Object3.size"
]
},
{
"cell_type": "markdown",
"id": "2b941b4d",
"metadata": {},
"source": [
"And unfortunatelly, we can't. I'll explain it in a moment, but for now, let's try to check if we can still attach our size like previously."
]
},
{
"cell_type": "code",
"execution_count": 186,
"id": "d2702ffc",
"metadata": {},
"outputs": [],
"source": [
"Object3.size = 'small'"
]
},
{
"cell_type": "code",
"execution_count": 187,
"id": "a5aac43d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"small\n"
]
}
],
"source": [
"print(Object3.size)"
]
},
{
"cell_type": "markdown",
"id": "04f6ee94",
"metadata": {},
"source": [
"And yes, we can. Although during creation we put the 'big' size argument in Object3, we hadn't had stored it properly, so we couldn't have accesed it. But we could have added it as previously.\n",
"\n",
"Now, however, let's make our class the right way, so we store our size in size variable of the object, and to be precise in the 'self.size' (because we always have to put that self here before the variable we want to use to store the data in our instance).\n",
"\n",
"And here is the proper class, finally."
]
},
{
"cell_type": "code",
"execution_count": 188,
"id": "0e2e7ed9",
"metadata": {},
"outputs": [],
"source": [
"class Object_with_proper_size:\n",
" def __init__(self, size):\n",
" self.size = size\n",
" print('Object was created')"
]
},
{
"cell_type": "code",
"execution_count": 189,
"id": "2c5c3d29",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Object_with_proper_size.__init__() missing 1 required positional argument: 'size'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[189], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Object5 \u001b[38;5;241m=\u001b[39m Object_with_proper_size()\n",
"\u001b[1;31mTypeError\u001b[0m: Object_with_proper_size.__init__() missing 1 required positional argument: 'size'"
]
}
],
"source": [
"Object5 = Object_with_proper_size()"
]
},
{
"cell_type": "markdown",
"id": "2ab20a10",
"metadata": {},
"source": [
"So close... Ok, do you know why it didn't work this time?\n",
"\n",
"Yes, we didn't put the size argument in parentheses.\n",
"\n",
"So let's do it now."
]
},
{
"cell_type": "code",
"execution_count": 190,
"id": "75e62741",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Object was created\n"
]
}
],
"source": [
"Object5 = Object_with_proper_size('huge')"
]
},
{
"cell_type": "markdown",
"id": "5f933c8d",
"metadata": {},
"source": [
"We have it. And let's check the size of our object now."
]
},
{
"cell_type": "code",
"execution_count": 191,
"id": "a320daec",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'huge'"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Object5.size"
]
},
{
"cell_type": "markdown",
"id": "f2ee5fa4",
"metadata": {},
"source": [
"Ok. So it works. We have our huge object. But if something changes, like the size of our object, we can also change that quite simply."
]
},
{
"cell_type": "code",
"execution_count": 192,
"id": "6d1f678d",
"metadata": {},
"outputs": [],
"source": [
"Object5.size = 'small'"
]
},
{
"cell_type": "code",
"execution_count": 193,
"id": "db7c1feb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'small'"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Object5.size"
]
},
{
"cell_type": "markdown",
"id": "ca64f480",
"metadata": {},
"source": [
"And the size have changed. And now it works just great. Don't you think?\n",
"\n",
"Just remember one more thing about that init constructor and self:"
]
},
{
"cell_type": "code",
"execution_count": 194,
"id": "b80ad298",
"metadata": {},
"outputs": [],
"source": [
"class Object_with_size_but_without_size:\n",
" def __init__(size):\n",
" print('Object was created')"
]
},
{
"cell_type": "code",
"execution_count": 195,
"id": "8d5cda97",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Object_with_size_but_without_size.__init__() takes 1 positional argument but 2 were given",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[195], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Object6 \u001b[38;5;241m=\u001b[39m Object_with_size_but_without_size(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124menormous\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"\u001b[1;31mTypeError\u001b[0m: Object_with_size_but_without_size.__init__() takes 1 positional argument but 2 were given"
]
}
],
"source": [
"Object6 = Object_with_size_but_without_size('enormous')"
]
},
{
"cell_type": "code",
"execution_count": 196,
"id": "75f81619",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Object was created\n"
]
}
],
"source": [
"Object7 = Object_with_size_but_without_size()"
]
},
{
"cell_type": "markdown",
"id": "70e31b6c",
"metadata": {},
"source": [
"If you forget about self argument in the init constructor, the first argument you have in that method is treated as that self that. That also mean that you don't have to call it self, but it's good practice and that's how almost everybody does it, so if you want your code to be easily readable, you should also call that self."
]
},
{
"cell_type": "markdown",
"id": "816e7a12",
"metadata": {},
"source": [
"#### *digression: string as a class*\n",
"\n",
"By the way, the string is also a class as are many other things in Python. And you can use some methods with that class as well, like here:"
]
},
{
"cell_type": "code",
"execution_count": 197,
"id": "29c533af",
"metadata": {},
"outputs": [],
"source": [
"O = str()"
]
},
{
"cell_type": "code",
"execution_count": 198,
"id": "b91f08d5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"O"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "236dc4c0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print(type(O))"
]
},
{
"cell_type": "code",
"execution_count": 200,
"id": "6bf3f0f1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class str in module builtins:\n",
"\n",
"class str(object)\n",
" | str(object='') -> str\n",
" | str(bytes_or_buffer[, encoding[, errors]]) -> str\n",
" | \n",
" | Create a new string object from the given object. If encoding or\n",
" | errors is specified, then the object must expose a data buffer\n",
" | that will be decoded using the given encoding and error handler.\n",
" | Otherwise, returns the result of object.__str__() (if defined)\n",
" | or repr(object).\n",
" | encoding defaults to sys.getdefaultencoding().\n",
" | errors defaults to 'strict'.\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __add__(self, value, /)\n",
" | Return self+value.\n",
" | \n",
" | __contains__(self, key, /)\n",
" | Return key in self.\n",
" | \n",
" | __eq__(self, value, /)\n",
" | Return self==value.\n",
" | \n",
" | __format__(self, format_spec, /)\n",
" | Return a formatted version of the string as described by format_spec.\n",
" | \n",
" | __ge__(self, value, /)\n",
" | Return self>=value.\n",
" | \n",
" | __getattribute__(self, name, /)\n",
" | Return getattr(self, name).\n",
" | \n",
" | __getitem__(self, key, /)\n",
" | Return self[key].\n",
" | \n",
" | __getnewargs__(...)\n",
" | \n",
" | __gt__(self, value, /)\n",
" | Return self>value.\n",
" | \n",
" | __hash__(self, /)\n",
" | Return hash(self).\n",
" | \n",
" | __iter__(self, /)\n",
" | Implement iter(self).\n",
" | \n",
" | __le__(self, value, /)\n",
" | Return self<=value.\n",
" | \n",
" | __len__(self, /)\n",
" | Return len(self).\n",
" | \n",
" | __lt__(self, value, /)\n",
" | Return self int\n",
" | \n",
" | Return the number of non-overlapping occurrences of substring sub in\n",
" | string S[start:end]. Optional arguments start and end are\n",
" | interpreted as in slice notation.\n",
" | \n",
" | encode(self, /, encoding='utf-8', errors='strict')\n",
" | Encode the string using the codec registered for encoding.\n",
" | \n",
" | encoding\n",
" | The encoding in which to encode the string.\n",
" | errors\n",
" | The error handling scheme to use for encoding errors.\n",
" | The default is 'strict' meaning that encoding errors raise a\n",
" | UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n",
" | 'xmlcharrefreplace' as well as any other name registered with\n",
" | codecs.register_error that can handle UnicodeEncodeErrors.\n",
" | \n",
" | endswith(...)\n",
" | S.endswith(suffix[, start[, end]]) -> bool\n",
" | \n",
" | Return True if S ends with the specified suffix, False otherwise.\n",
" | With optional start, test S beginning at that position.\n",
" | With optional end, stop comparing S at that position.\n",
" | suffix can also be a tuple of strings to try.\n",
" | \n",
" | expandtabs(self, /, tabsize=8)\n",
" | Return a copy where all tab characters are expanded using spaces.\n",
" | \n",
" | If tabsize is not given, a tab size of 8 characters is assumed.\n",
" | \n",
" | find(...)\n",
" | S.find(sub[, start[, end]]) -> int\n",
" | \n",
" | Return the lowest index in S where substring sub is found,\n",
" | such that sub is contained within S[start:end]. Optional\n",
" | arguments start and end are interpreted as in slice notation.\n",
" | \n",
" | Return -1 on failure.\n",
" | \n",
" | format(...)\n",
" | S.format(*args, **kwargs) -> str\n",
" | \n",
" | Return a formatted version of S, using substitutions from args and kwargs.\n",
" | The substitutions are identified by braces ('{' and '}').\n",
" | \n",
" | format_map(...)\n",
" | S.format_map(mapping) -> str\n",
" | \n",
" | Return a formatted version of S, using substitutions from mapping.\n",
" | The substitutions are identified by braces ('{' and '}').\n",
" | \n",
" | index(...)\n",
" | S.index(sub[, start[, end]]) -> int\n",
" | \n",
" | Return the lowest index in S where substring sub is found,\n",
" | such that sub is contained within S[start:end]. Optional\n",
" | arguments start and end are interpreted as in slice notation.\n",
" | \n",
" | Raises ValueError when the substring is not found.\n",
" | \n",
" | isalnum(self, /)\n",
" | Return True if the string is an alpha-numeric string, False otherwise.\n",
" | \n",
" | A string is alpha-numeric if all characters in the string are alpha-numeric and\n",
" | there is at least one character in the string.\n",
" | \n",
" | isalpha(self, /)\n",
" | Return True if the string is an alphabetic string, False otherwise.\n",
" | \n",
" | A string is alphabetic if all characters in the string are alphabetic and there\n",
" | is at least one character in the string.\n",
" | \n",
" | isascii(self, /)\n",
" | Return True if all characters in the string are ASCII, False otherwise.\n",
" | \n",
" | ASCII characters have code points in the range U+0000-U+007F.\n",
" | Empty string is ASCII too.\n",
" | \n",
" | isdecimal(self, /)\n",
" | Return True if the string is a decimal string, False otherwise.\n",
" | \n",
" | A string is a decimal string if all characters in the string are decimal and\n",
" | there is at least one character in the string.\n",
" | \n",
" | isdigit(self, /)\n",
" | Return True if the string is a digit string, False otherwise.\n",
" | \n",
" | A string is a digit string if all characters in the string are digits and there\n",
" | is at least one character in the string.\n",
" | \n",
" | isidentifier(self, /)\n",
" | Return True if the string is a valid Python identifier, False otherwise.\n",
" | \n",
" | Call keyword.iskeyword(s) to test whether string s is a reserved identifier,\n",
" | such as \"def\" or \"class\".\n",
" | \n",
" | islower(self, /)\n",
" | Return True if the string is a lowercase string, False otherwise.\n",
" | \n",
" | A string is lowercase if all cased characters in the string are lowercase and\n",
" | there is at least one cased character in the string.\n",
" | \n",
" | isnumeric(self, /)\n",
" | Return True if the string is a numeric string, False otherwise.\n",
" | \n",
" | A string is numeric if all characters in the string are numeric and there is at\n",
" | least one character in the string.\n",
" | \n",
" | isprintable(self, /)\n",
" | Return True if the string is printable, False otherwise.\n",
" | \n",
" | A string is printable if all of its characters are considered printable in\n",
" | repr() or if it is empty.\n",
" | \n",
" | isspace(self, /)\n",
" | Return True if the string is a whitespace string, False otherwise.\n",
" | \n",
" | A string is whitespace if all characters in the string are whitespace and there\n",
" | is at least one character in the string.\n",
" | \n",
" | istitle(self, /)\n",
" | Return True if the string is a title-cased string, False otherwise.\n",
" | \n",
" | In a title-cased string, upper- and title-case characters may only\n",
" | follow uncased characters and lowercase characters only cased ones.\n",
" | \n",
" | isupper(self, /)\n",
" | Return True if the string is an uppercase string, False otherwise.\n",
" | \n",
" | A string is uppercase if all cased characters in the string are uppercase and\n",
" | there is at least one cased character in the string.\n",
" | \n",
" | join(self, iterable, /)\n",
" | Concatenate any number of strings.\n",
" | \n",
" | The string whose method is called is inserted in between each given string.\n",
" | The result is returned as a new string.\n",
" | \n",
" | Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n",
" | \n",
" | ljust(self, width, fillchar=' ', /)\n",
" | Return a left-justified string of length width.\n",
" | \n",
" | Padding is done using the specified fill character (default is a space).\n",
" | \n",
" | lower(self, /)\n",
" | Return a copy of the string converted to lowercase.\n",
" | \n",
" | lstrip(self, chars=None, /)\n",
" | Return a copy of the string with leading whitespace removed.\n",
" | \n",
" | If chars is given and not None, remove characters in chars instead.\n",
" | \n",
" | partition(self, sep, /)\n",
" | Partition the string into three parts using the given separator.\n",
" | \n",
" | This will search for the separator in the string. If the separator is found,\n",
" | returns a 3-tuple containing the part before the separator, the separator\n",
" | itself, and the part after it.\n",
" | \n",
" | If the separator is not found, returns a 3-tuple containing the original string\n",
" | and two empty strings.\n",
" | \n",
" | removeprefix(self, prefix, /)\n",
" | Return a str with the given prefix string removed if present.\n",
" | \n",
" | If the string starts with the prefix string, return string[len(prefix):].\n",
" | Otherwise, return a copy of the original string.\n",
" | \n",
" | removesuffix(self, suffix, /)\n",
" | Return a str with the given suffix string removed if present.\n",
" | \n",
" | If the string ends with the suffix string and that suffix is not empty,\n",
" | return string[:-len(suffix)]. Otherwise, return a copy of the original\n",
" | string.\n",
" | \n",
" | replace(self, old, new, count=-1, /)\n",
" | Return a copy with all occurrences of substring old replaced by new.\n",
" | \n",
" | count\n",
" | Maximum number of occurrences to replace.\n",
" | -1 (the default value) means replace all occurrences.\n",
" | \n",
" | If the optional argument count is given, only the first count occurrences are\n",
" | replaced.\n",
" | \n",
" | rfind(...)\n",
" | S.rfind(sub[, start[, end]]) -> int\n",
" | \n",
" | Return the highest index in S where substring sub is found,\n",
" | such that sub is contained within S[start:end]. Optional\n",
" | arguments start and end are interpreted as in slice notation.\n",
" | \n",
" | Return -1 on failure.\n",
" | \n",
" | rindex(...)\n",
" | S.rindex(sub[, start[, end]]) -> int\n",
" | \n",
" | Return the highest index in S where substring sub is found,\n",
" | such that sub is contained within S[start:end]. Optional\n",
" | arguments start and end are interpreted as in slice notation.\n",
" | \n",
" | Raises ValueError when the substring is not found.\n",
" | \n",
" | rjust(self, width, fillchar=' ', /)\n",
" | Return a right-justified string of length width.\n",
" | \n",
" | Padding is done using the specified fill character (default is a space).\n",
" | \n",
" | rpartition(self, sep, /)\n",
" | Partition the string into three parts using the given separator.\n",
" | \n",
" | This will search for the separator in the string, starting at the end. If\n",
" | the separator is found, returns a 3-tuple containing the part before the\n",
" | separator, the separator itself, and the part after it.\n",
" | \n",
" | If the separator is not found, returns a 3-tuple containing two empty strings\n",
" | and the original string.\n",
" | \n",
" | rsplit(self, /, sep=None, maxsplit=-1)\n",
" | Return a list of the substrings in the string, using sep as the separator string.\n",
" | \n",
" | sep\n",
" | The separator used to split the string.\n",
" | \n",
" | When set to None (the default value), will split on any whitespace\n",
" | character (including \\n \\r \\t \\f and spaces) and will discard\n",
" | empty strings from the result.\n",
" | maxsplit\n",
" | Maximum number of splits.\n",
" | -1 (the default value) means no limit.\n",
" | \n",
" | Splitting starts at the end of the string and works to the front.\n",
" | \n",
" | rstrip(self, chars=None, /)\n",
" | Return a copy of the string with trailing whitespace removed.\n",
" | \n",
" | If chars is given and not None, remove characters in chars instead.\n",
" | \n",
" | split(self, /, sep=None, maxsplit=-1)\n",
" | Return a list of the substrings in the string, using sep as the separator string.\n",
" | \n",
" | sep\n",
" | The separator used to split the string.\n",
" | \n",
" | When set to None (the default value), will split on any whitespace\n",
" | character (including \\n \\r \\t \\f and spaces) and will discard\n",
" | empty strings from the result.\n",
" | maxsplit\n",
" | Maximum number of splits.\n",
" | -1 (the default value) means no limit.\n",
" | \n",
" | Splitting starts at the front of the string and works to the end.\n",
" | \n",
" | Note, str.split() is mainly useful for data that has been intentionally\n",
" | delimited. With natural text that includes punctuation, consider using\n",
" | the regular expression module.\n",
" | \n",
" | splitlines(self, /, keepends=False)\n",
" | Return a list of the lines in the string, breaking at line boundaries.\n",
" | \n",
" | Line breaks are not included in the resulting list unless keepends is given and\n",
" | true.\n",
" | \n",
" | startswith(...)\n",
" | S.startswith(prefix[, start[, end]]) -> bool\n",
" | \n",
" | Return True if S starts with the specified prefix, False otherwise.\n",
" | With optional start, test S beginning at that position.\n",
" | With optional end, stop comparing S at that position.\n",
" | prefix can also be a tuple of strings to try.\n",
" | \n",
" | strip(self, chars=None, /)\n",
" | Return a copy of the string with leading and trailing whitespace removed.\n",
" | \n",
" | If chars is given and not None, remove characters in chars instead.\n",
" | \n",
" | swapcase(self, /)\n",
" | Convert uppercase characters to lowercase and lowercase characters to uppercase.\n",
" | \n",
" | title(self, /)\n",
" | Return a version of the string where each word is titlecased.\n",
" | \n",
" | More specifically, words start with uppercased characters and all remaining\n",
" | cased characters have lower case.\n",
" | \n",
" | translate(self, table, /)\n",
" | Replace each character in the string using the given translation table.\n",
" | \n",
" | table\n",
" | Translation table, which must be a mapping of Unicode ordinals to\n",
" | Unicode ordinals, strings, or None.\n",
" | \n",
" | The table must implement lookup/indexing via __getitem__, for instance a\n",
" | dictionary or list. If this operation raises LookupError, the character is\n",
" | left untouched. Characters mapped to None are deleted.\n",
" | \n",
" | upper(self, /)\n",
" | Return a copy of the string converted to uppercase.\n",
" | \n",
" | zfill(self, width, /)\n",
" | Pad a numeric string with zeros on the left, to fill a field of the given width.\n",
" | \n",
" | The string is never truncated.\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Static methods defined here:\n",
" | \n",
" | __new__(*args, **kwargs) from builtins.type\n",
" | Create and return a new object. See help(type) for accurate signature.\n",
" | \n",
" | maketrans(...)\n",
" | Return a translation table usable for str.translate().\n",
" | \n",
" | If there is only one argument, it must be a dictionary mapping Unicode\n",
" | ordinals (integers) or characters to Unicode ordinals, strings or None.\n",
" | Character keys will be then converted to ordinals.\n",
" | If there are two arguments, they must be strings of equal length, and\n",
" | in the resulting dictionary, each character in x will be mapped to the\n",
" | character at the same position in y. If there is a third argument, it\n",
" | must be a string, whose characters will be mapped to None in the result.\n",
"\n"
]
}
],
"source": [
"help(O)"
]
},
{
"cell_type": "code",
"execution_count": 201,
"id": "236e086c",
"metadata": {},
"outputs": [],
"source": [
"O = str(\"object\")"
]
},
{
"cell_type": "code",
"execution_count": 202,
"id": "92a4ccb4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'object'"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"O"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "8fad0b91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Object'"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"O.capitalize()"
]
},
{
"cell_type": "code",
"execution_count": 204,
"id": "4257eae2",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'object'"
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"O"
]
},
{
"cell_type": "code",
"execution_count": 205,
"id": "4aedd1ce",
"metadata": {},
"outputs": [],
"source": [
"O = O.capitalize()"
]
},
{
"cell_type": "code",
"execution_count": 206,
"id": "5f0483d8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Object'"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"O"
]
},
{
"cell_type": "markdown",
"id": "f0ab8c0a",
"metadata": {},
"source": [
"Ok, that's it about that. Now..."
]
},
{
"cell_type": "markdown",
"id": "e5549ed0",
"metadata": {},
"source": [
"### With or without self? that is a question..."
]
},
{
"cell_type": "markdown",
"id": "c55f9d9c",
"metadata": {},
"source": [
"And here you can see some other examples with self and without self even if you don't have init method. Which of them will work, what do you think?"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "a3a624fe",
"metadata": {},
"outputs": [],
"source": [
"class With_self:\n",
" def do_something(self):\n",
" print(\"doing something with self\")"
]
},
{
"cell_type": "code",
"execution_count": 208,
"id": "f0ced9d5",
"metadata": {},
"outputs": [],
"source": [
"class Without_self:\n",
" def do_something():\n",
" print(\"doing something without self\")"
]
},
{
"cell_type": "code",
"execution_count": 209,
"id": "ad609b22",
"metadata": {},
"outputs": [],
"source": [
"withself = With_self()"
]
},
{
"cell_type": "code",
"execution_count": 210,
"id": "788e48f3",
"metadata": {},
"outputs": [],
"source": [
"withoutself = Without_self()"
]
},
{
"cell_type": "code",
"execution_count": 211,
"id": "d85c9e13",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"doing something with self\n"
]
}
],
"source": [
"withself.do_something()"
]
},
{
"cell_type": "code",
"execution_count": 212,
"id": "d9a56e12",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Without_self.do_something() takes 0 positional arguments but 1 was given",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[212], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m withoutself\u001b[38;5;241m.\u001b[39mdo_something()\n",
"\u001b[1;31mTypeError\u001b[0m: Without_self.do_something() takes 0 positional arguments but 1 was given"
]
}
],
"source": [
"withoutself.do_something()"
]
},
{
"cell_type": "code",
"execution_count": 213,
"id": "b9bf3155",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"doing something without self\n"
]
}
],
"source": [
"class Without_self_too:\n",
" print(\"doing something without self\")"
]
},
{
"cell_type": "markdown",
"id": "68fc3a72",
"metadata": {},
"source": [
"Try thinking why that happened. And remember about self."
]
},
{
"cell_type": "markdown",
"id": "cb97beb0",
"metadata": {},
"source": [
"### Methods"
]
},
{
"cell_type": "markdown",
"id": "790aa018",
"metadata": {},
"source": [
"Now, let's see more about these methods. Here you can see some more examples. \n",
"\n",
"We create a class called Little John with a john method. What do you think will happen?"
]
},
{
"cell_type": "code",
"execution_count": 214,
"id": "250a620a",
"metadata": {},
"outputs": [],
"source": [
"class Little_John:\n",
" def __init__(self, a):\n",
" self.a = a\n",
" print(self.a)\n",
" def john(self, b):\n",
" self.b = b\n",
" print(self.b)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"id": "58fcf64b",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Little_John.__init__() missing 1 required positional argument: 'a'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[215], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m LJ1 \u001b[38;5;241m=\u001b[39m Little_John()\n",
"\u001b[1;31mTypeError\u001b[0m: Little_John.__init__() missing 1 required positional argument: 'a'"
]
}
],
"source": [
"LJ1 = Little_John()"
]
},
{
"cell_type": "code",
"execution_count": 216,
"id": "fdbea5e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"big\n"
]
}
],
"source": [
"LJ1 = Little_John('big')"
]
},
{
"cell_type": "code",
"execution_count": 217,
"id": "5c81580c",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Little_John.john() missing 1 required positional argument: 'b'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[217], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m LJ1\u001b[38;5;241m.\u001b[39mjohn()\n",
"\u001b[1;31mTypeError\u001b[0m: Little_John.john() missing 1 required positional argument: 'b'"
]
}
],
"source": [
"LJ1.john()"
]
},
{
"cell_type": "code",
"execution_count": 218,
"id": "26774bd1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"John\n"
]
}
],
"source": [
"LJ1.john('John')"
]
},
{
"cell_type": "code",
"execution_count": 219,
"id": "0d85ec98",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"big\n",
"John\n"
]
}
],
"source": [
"Little_John('big').john('John')"
]
},
{
"cell_type": "markdown",
"id": "9ef18ba4",
"metadata": {},
"source": [
"Ok. Hope you know why we got this big John here. Great. Now..."
]
},
{
"cell_type": "markdown",
"id": "78ffb271",
"metadata": {},
"source": [
"### CLASS LEVEL ATTRIBUTES"
]
},
{
"cell_type": "markdown",
"id": "948adde2",
"metadata": {},
"source": [
"When we talk about classes, we should remember about class level attributes. These are attributes that are not specific to the instance of the class (like the ones we had previously), but are for the whole class, like here."
]
},
{
"cell_type": "code",
"execution_count": 220,
"id": "484ec083",
"metadata": {},
"outputs": [],
"source": [
"class Class_level:\n",
" class_level_attribute = \"Class level is always on\""
]
},
{
"cell_type": "code",
"execution_count": 221,
"id": "0f96fca4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Class level is always on'"
]
},
"execution_count": 221,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Class_level.class_level_attribute"
]
},
{
"cell_type": "code",
"execution_count": 222,
"id": "dfc928f2",
"metadata": {},
"outputs": [],
"source": [
"cl1 = Class_level()"
]
},
{
"cell_type": "code",
"execution_count": 223,
"id": "ffb803d6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Class level is always on'"
]
},
"execution_count": 223,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cl1.class_level_attribute"
]
},
{
"cell_type": "markdown",
"id": "d89f61a6",
"metadata": {},
"source": [
"Now, how could we use it? Well, we could have class level attribute always on for any new instance, but change that level for some instances. Like here:"
]
},
{
"cell_type": "code",
"execution_count": 224,
"id": "1fc17749",
"metadata": {},
"outputs": [],
"source": [
"class Class_level:\n",
" class_level_attribute = \"ON\"\n",
"\n",
" def __init__(self, the_object):\n",
" self.name = the_object\n",
"\n",
" def is_it_on(self):\n",
" print(f\"{self.name} Phone is {self.class_level_attribute}.\")"
]
},
{
"cell_type": "code",
"execution_count": 225,
"id": "56904dfc",
"metadata": {},
"outputs": [],
"source": [
"cl2 = Class_level(\"First\")"
]
},
{
"cell_type": "code",
"execution_count": 226,
"id": "48e7fabf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First Phone is ON.\n"
]
}
],
"source": [
"cl2.is_it_on()"
]
},
{
"cell_type": "code",
"execution_count": 227,
"id": "1375fc2c",
"metadata": {},
"outputs": [],
"source": [
"cl3 = Class_level(\"Second\")"
]
},
{
"cell_type": "code",
"execution_count": 228,
"id": "ba660e0c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Second Phone is ON.\n"
]
}
],
"source": [
"cl3.is_it_on()"
]
},
{
"cell_type": "code",
"execution_count": 229,
"id": "57591457",
"metadata": {},
"outputs": [],
"source": [
"cl3.class_level_attribute = \"OFF\""
]
},
{
"cell_type": "code",
"execution_count": 230,
"id": "f725b2b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Second Phone is OFF.\n"
]
}
],
"source": [
"cl3.is_it_on()"
]
},
{
"cell_type": "code",
"execution_count": 231,
"id": "465b44a5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Third Phone is ON.\n"
]
}
],
"source": [
"cl4 = Class_level(\"Third\")\n",
"cl4.is_it_on()"
]
},
{
"cell_type": "markdown",
"id": "c46b6faa",
"metadata": {},
"source": [
"Believe me, sometimes it's quite helpful. Meanwhile."
]
},
{
"cell_type": "markdown",
"id": "764fd6eb",
"metadata": {},
"source": [
"### APPENDING INSTANCES TO ALL CLASS ITEMS"
]
},
{
"cell_type": "markdown",
"id": "2dca620c",
"metadata": {},
"source": [
"We can try appending instances to all class items like below. That way in an easy way we can check what kind of instances we've created of the given class."
]
},
{
"cell_type": "code",
"execution_count": 232,
"id": "341f4115",
"metadata": {},
"outputs": [],
"source": [
"class Gathered:\n",
" gathered = []\n",
" def __init__(self, name):\n",
" self.name = name\n",
" Gathered.gathered.append(self)\n",
" \n",
" def __repr__(self):\n",
" return 'Gathered({})'.format(self.name)"
]
},
{
"cell_type": "code",
"execution_count": 233,
"id": "7a1b3f72",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "code",
"execution_count": 234,
"id": "675d81d6",
"metadata": {},
"outputs": [],
"source": [
"gathered1 = Gathered(\"object1\")"
]
},
{
"cell_type": "code",
"execution_count": 235,
"id": "c846ebed",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Gathered(object1)]"
]
},
"execution_count": 235,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "code",
"execution_count": 236,
"id": "c60406d4",
"metadata": {},
"outputs": [],
"source": [
"gathered2 = Gathered(\"object2\")"
]
},
{
"cell_type": "code",
"execution_count": 237,
"id": "0e0e6aa1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Gathered(object1), Gathered(object2)]"
]
},
"execution_count": 237,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "code",
"execution_count": 238,
"id": "ca2a3b91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"object1\n",
"object2\n"
]
}
],
"source": [
"for stored_object in Gathered.gathered:\n",
" print(stored_object.name)"
]
},
{
"cell_type": "markdown",
"id": "2b5cf779",
"metadata": {},
"source": [
"or like this"
]
},
{
"cell_type": "code",
"execution_count": 239,
"id": "6ea52cc7",
"metadata": {},
"outputs": [],
"source": [
"class Gathered:\n",
" gathered = []\n",
" def __init__(self, name):\n",
" self.name = name\n",
" Gathered.gathered.append(self)\n",
" \n",
" def __repr__(self):\n",
" return '{}({})'.format(self.__class__.__name__, self.name)"
]
},
{
"cell_type": "code",
"execution_count": 240,
"id": "9452887a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 240,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "code",
"execution_count": 241,
"id": "a301b897",
"metadata": {},
"outputs": [],
"source": [
"gathered1 = Gathered(\"object1\")"
]
},
{
"cell_type": "code",
"execution_count": 242,
"id": "4dada742",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Gathered(object1)]"
]
},
"execution_count": 242,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "code",
"execution_count": 243,
"id": "cdd9f84e",
"metadata": {},
"outputs": [],
"source": [
"gathered2 = Gathered(\"object2\")"
]
},
{
"cell_type": "code",
"execution_count": 244,
"id": "f4610276",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Gathered(object1), Gathered(object2)]"
]
},
"execution_count": 244,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "code",
"execution_count": 245,
"id": "8058e896",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"object1\n",
"object2\n"
]
}
],
"source": [
"for stored_object in Gathered.gathered:\n",
" print(stored_object.name)"
]
},
{
"cell_type": "markdown",
"id": "2cb0fd6e",
"metadata": {},
"source": [
"This can also be quite handy. \n",
"\n",
"No, when you learn the basics about classes you should also learn a bit more advanced topic about classes, that is: class methods and static methods that use special decorators. Here is how they look like. I won't start explaining them to you, because it's too advanced I think, for beginners, but you can find more about them in the internet."
]
},
{
"cell_type": "markdown",
"id": "0af1fda2",
"metadata": {},
"source": [
"### @classmethod"
]
},
{
"cell_type": "code",
"execution_count": 246,
"id": "72aa3230",
"metadata": {},
"outputs": [],
"source": [
"bunch_of_people = [\"Jane\", \"John\", \"Jack\"]"
]
},
{
"cell_type": "code",
"execution_count": 247,
"id": "87248f4e",
"metadata": {},
"outputs": [],
"source": [
"class Gathered:\n",
" gathered = []\n",
" def __init__(self, name):\n",
" self.name = name\n",
" Gathered.gathered.append(self)\n",
" \n",
" def __repr__(self):\n",
" return '{}({})'.format(self.__class__.__name__, self.name)\n",
"\n",
" @classmethod \n",
" def create_bunch_of_people(cls):\n",
" for person in bunch_of_people:\n",
" Gathered(name = person)\n",
" return Gathered.gathered"
]
},
{
"cell_type": "markdown",
"id": "54ee5e37",
"metadata": {},
"source": [
"That class method, as you can see doesn't have 'self' as an argument, but 'cls'. And when we use it..."
]
},
{
"cell_type": "code",
"execution_count": 248,
"id": "17c01d9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Gathered(Jane), Gathered(John), Gathered(Jack)]"
]
},
"execution_count": 248,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.create_bunch_of_people()"
]
},
{
"cell_type": "code",
"execution_count": 249,
"id": "f5603026",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Gathered(Jane), Gathered(John), Gathered(Jack)]"
]
},
"execution_count": 249,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered.gathered"
]
},
{
"cell_type": "markdown",
"id": "b38e5ec6",
"metadata": {},
"source": [
"We've created a bunch of people (3 instances of the class) in just one line of code. And that can be very useful sometimes. But, please, learn more about it, I just wanted to let you know that this kind of method exists."
]
},
{
"cell_type": "markdown",
"id": "9efe38bf",
"metadata": {},
"source": [
"### @staticmethod"
]
},
{
"cell_type": "markdown",
"id": "817030e5",
"metadata": {},
"source": [
"And we also have static methods, like below..."
]
},
{
"cell_type": "code",
"execution_count": 250,
"id": "f4cd3c47",
"metadata": {},
"outputs": [],
"source": [
"class Gathered_static:\n",
" gathered = []\n",
" def __init__(self, name):\n",
" self.name = name\n",
" Gathered_static.gathered.append(self)\n",
" \n",
" def __repr__(self):\n",
" return '{}({})'.format(self.__class__.__name__, self.name)\n",
" \n",
" @staticmethod \n",
" def is_in(name):\n",
" return name in [person.name for person in Gathered_static.gathered]"
]
},
{
"cell_type": "markdown",
"id": "9db45a0e",
"metadata": {},
"source": [
"As you can see we don't have to put self or cls argument into the static method and we can use it from the class level, just like we used the class method. So, let's create couple of instances at first."
]
},
{
"cell_type": "code",
"execution_count": 251,
"id": "d65d9855",
"metadata": {},
"outputs": [],
"source": [
"g1s = Gathered_static(\"Jane\")\n",
"g2s = Gathered_static(\"John\")\n",
"g3s = Gathered_static(\"Jack\")"
]
},
{
"cell_type": "markdown",
"id": "bb742fd5",
"metadata": {},
"source": [
"And now let's check our method."
]
},
{
"cell_type": "code",
"execution_count": 252,
"id": "86820e97",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 252,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered_static.is_in(\"Jane\")"
]
},
{
"cell_type": "code",
"execution_count": 253,
"id": "5ad56406",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 253,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gathered_static.is_in(\"Jenny\")"
]
},
{
"cell_type": "markdown",
"id": "eb29af4f",
"metadata": {},
"source": [
"As you can see, it works. This static methods can be useful, but you can learn about it later. Meanwhile..."
]
},
{
"cell_type": "markdown",
"id": "dce5d537",
"metadata": {},
"source": [
"### INHERITANCE"
]
},
{
"cell_type": "markdown",
"id": "33fa6a37",
"metadata": {},
"source": [
"Finally, you should learn about inheritance, parents and children, because that is a powerful tool that can help you a lot. And that might look like here. First, we create a parent class:"
]
},
{
"cell_type": "code",
"execution_count": 254,
"id": "9af60025",
"metadata": {},
"outputs": [],
"source": [
"class Parent:\n",
" def __init__(self, name, surname=\"Smith\"):\n",
" self.name = name\n",
" self.surname = surname\n",
" def say(self, sentence):\n",
" return f'Hello {self.name} {self.surname}, {sentence}.'"
]
},
{
"cell_type": "markdown",
"id": "f636a7a2",
"metadata": {},
"source": [
"Couple of instances."
]
},
{
"cell_type": "code",
"execution_count": 255,
"id": "ae51933e",
"metadata": {},
"outputs": [],
"source": [
"PEx1 = Parent(\"Jane\")"
]
},
{
"cell_type": "code",
"execution_count": 256,
"id": "bce8d64a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Jane'"
]
},
"execution_count": 256,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PEx1.name"
]
},
{
"cell_type": "code",
"execution_count": 257,
"id": "5614661e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Smith'"
]
},
"execution_count": 257,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PEx1.surname"
]
},
{
"cell_type": "code",
"execution_count": 258,
"id": "a3c47bcf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello Jane Smith, this is cool.'"
]
},
"execution_count": 258,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PEx1.say(\"this is cool\")"
]
},
{
"cell_type": "code",
"execution_count": 259,
"id": "b357f67b",
"metadata": {},
"outputs": [],
"source": [
"PEx2 = Parent('John', 'Johnson')"
]
},
{
"cell_type": "code",
"execution_count": 260,
"id": "5c95a47e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello John Johnson, this is cool.'"
]
},
"execution_count": 260,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PEx2.say('this is cool')"
]
},
{
"cell_type": "markdown",
"id": "14ceafe1",
"metadata": {},
"source": [
"And now, we can create a child class that has completly the same functionality as a parent class (for now)."
]
},
{
"cell_type": "code",
"execution_count": 261,
"id": "9ef84970",
"metadata": {},
"outputs": [],
"source": [
"class Inheritance_Example(Parent):\n",
" pass"
]
},
{
"cell_type": "markdown",
"id": "3ed4e1c3",
"metadata": {},
"source": [
"And also create couple of instances quite easy."
]
},
{
"cell_type": "code",
"execution_count": 262,
"id": "42055087",
"metadata": {},
"outputs": [],
"source": [
"IEx1 = Inheritance_Example(\"Jane\", 'Johnson')"
]
},
{
"cell_type": "code",
"execution_count": 263,
"id": "9a3e3b09",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello Jane Johnson, this is cool.'"
]
},
"execution_count": 263,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IEx1.say('this is cool')"
]
},
{
"cell_type": "markdown",
"id": "d8a2df71",
"metadata": {},
"source": [
"In order to have some different functionalities in our child class, we can rewrite that child class with init constructor that refers to the Parent class (with super function)."
]
},
{
"cell_type": "code",
"execution_count": 264,
"id": "38c20886",
"metadata": {},
"outputs": [],
"source": [
"class Inheritance_Example(Parent):\n",
" def __init__(self, name, surname=\"Smith\"):\n",
" super().__init__(name, surname)"
]
},
{
"cell_type": "markdown",
"id": "91e6db2c",
"metadata": {},
"source": [
"And once again, we can create some new instances."
]
},
{
"cell_type": "code",
"execution_count": 265,
"id": "576d01e4",
"metadata": {},
"outputs": [],
"source": [
"IEx2 = Inheritance_Example(\"Jane\", 'Brown')"
]
},
{
"cell_type": "code",
"execution_count": 266,
"id": "ae0136dc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello Jane Brown, this is cool.'"
]
},
"execution_count": 266,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IEx2.say('this is cool')"
]
},
{
"cell_type": "markdown",
"id": "f869f30f",
"metadata": {},
"source": [
"And now, finally we can add some new functionality to our child class, like here."
]
},
{
"cell_type": "code",
"execution_count": 267,
"id": "d7a7bf91",
"metadata": {},
"outputs": [],
"source": [
"class Inheritance_Example(Parent):\n",
" def __init__(self, name, age, surname=\"Smith\"):\n",
" super().__init__(name, surname)\n",
" self.age = age\n",
" \n",
" def say_more(self, sentence):\n",
" return f'Hello {self.name} {self.surname}, age {self.age}, {sentence}.'"
]
},
{
"cell_type": "code",
"execution_count": 268,
"id": "33ac5626",
"metadata": {},
"outputs": [],
"source": [
"IEx3 = Inheritance_Example(\"Jane\", '19', 'Smith')"
]
},
{
"cell_type": "code",
"execution_count": 269,
"id": "5257ce15",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello Jane Smith, this is cool.'"
]
},
"execution_count": 269,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IEx3.say('this is cool')"
]
},
{
"cell_type": "code",
"execution_count": 270,
"id": "241b2ec6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello Jane Smith, age 19, this is even cooler.'"
]
},
"execution_count": 270,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IEx3.say_more(\"this is even cooler\")"
]
},
{
"cell_type": "markdown",
"id": "d3898bd0",
"metadata": {},
"source": [
"Isn't it great?"
]
},
{
"cell_type": "markdown",
"id": "8ff16138",
"metadata": {},
"source": [
"Ok. There was a lot of stuff in this object oriented programming fragment and it was definitelly not well enough explained, but hopefully, you did learn something and now you can start searching the internet for more info about it. I might develop this part further in the future, but for now - that's it.\n",
"\n",
"And for us it's time to move to saving and loading files with Python."
]
},
{
"cell_type": "markdown",
"id": "a6675e05",
"metadata": {},
"source": [
"## 20. BASICS+: I/O - saving, loading, and opening files "
]
},
{
"cell_type": "markdown",
"id": "a719dcac",
"metadata": {},
"source": [
"### SAVING WITH PRINT/WRITE"
]
},
{
"cell_type": "markdown",
"id": "47021e24",
"metadata": {},
"source": [
"There are several ways you can save somthing with Python to a file.\n",
"\n",
"We can for example use print or write to do it.\n",
"\n",
"Both the print() function and the write() method can be used to save text or data to a file in Python, but they have some differences. The print() function is more convenient for printing formatted text with variables, while the write() method is more suitable for writing raw data or binary data. The print() function also adds a newline character by default, while the write() method does not.\n",
"\n",
"And here is how you can do that saving.\n",
"\n",
"What you'll also see here are some flags: 'w', 'r', 'a' we use with our function/method. Try figuring out on your own what are they for."
]
},
{
"cell_type": "markdown",
"id": "f55fd636",
"metadata": {},
"source": [
"### print:w"
]
},
{
"cell_type": "markdown",
"id": "c1717df9",
"metadata": {},
"source": [
"SAVING"
]
},
{
"cell_type": "code",
"execution_count": 271,
"id": "50946da8",
"metadata": {},
"outputs": [],
"source": [
"sth = \"something.txt\"\n",
"with open(sth, 'w', encoding=\"utf-8\") as f:\n",
" print('something', file=f)"
]
},
{
"cell_type": "markdown",
"id": "56f334bd",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 272,
"id": "ea518db1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something\n",
"\n"
]
}
],
"source": [
"with open('something.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "93a9edee",
"metadata": {},
"source": [
"SAVING MORE"
]
},
{
"cell_type": "code",
"execution_count": 273,
"id": "e4441d31",
"metadata": {},
"outputs": [],
"source": [
"sth = \"something.txt\"\n",
"with open(sth, 'w', encoding=\"utf-8\") as f:\n",
" print('something', file=f)"
]
},
{
"cell_type": "markdown",
"id": "4ed48733",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 274,
"id": "309b5823",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something\n",
"\n"
]
}
],
"source": [
"with open('something.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "b8260482",
"metadata": {},
"source": [
"### print:a"
]
},
{
"cell_type": "markdown",
"id": "6cc3db6b",
"metadata": {},
"source": [
"SAVING"
]
},
{
"cell_type": "code",
"execution_count": 275,
"id": "50ee6bfc",
"metadata": {},
"outputs": [],
"source": [
"sth = \"something_print_a.txt\"\n",
"with open(sth, 'a', encoding=\"utf-8\") as f:\n",
" print('something', file=f)"
]
},
{
"cell_type": "markdown",
"id": "c45c0b99",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 276,
"id": "e65e09d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something\n",
"\n"
]
}
],
"source": [
"with open('something_print_a.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "ba1627f0",
"metadata": {},
"source": [
"SAVING"
]
},
{
"cell_type": "code",
"execution_count": 277,
"id": "81f1dd8e",
"metadata": {},
"outputs": [],
"source": [
"sth = \"something_print_a.txt\"\n",
"with open(sth, 'a', encoding=\"utf-8\") as f:\n",
" print('something', file=f)"
]
},
{
"cell_type": "markdown",
"id": "2c74a40d",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 278,
"id": "086ec43e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something\n",
"something\n",
"\n"
]
}
],
"source": [
"with open('something_print_a.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "e02532b0",
"metadata": {},
"source": [
"### write: a"
]
},
{
"cell_type": "markdown",
"id": "6af025b5",
"metadata": {},
"source": [
"SAVING"
]
},
{
"cell_type": "code",
"execution_count": 279,
"id": "ea990cc6",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"something2.txt\", \"a\")\n",
"f.write(\"The file has some content!\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"id": "fc8096c6",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 280,
"id": "3ed7ff54",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The file has some content!\n"
]
}
],
"source": [
"with open('something2.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "e25e4071",
"metadata": {},
"source": [
"SAVING MORE TO FILE"
]
},
{
"cell_type": "code",
"execution_count": 281,
"id": "2e4d3354",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"something2.txt\", \"a\")\n",
"f.write(\" The file has more content now!\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"id": "b2a351f7",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 282,
"id": "b7fee068",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The file has some content! The file has more content now!\n"
]
}
],
"source": [
"with open('something2.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "d0870614",
"metadata": {},
"source": [
"SAVING MORE TO FILE IN ANOTHER LINE"
]
},
{
"cell_type": "code",
"execution_count": 283,
"id": "468f42c2",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"something2.txt\", \"a\")\n",
"f.write(\"\\nThe file has even more content now in a new line!\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"id": "d40ab707",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 284,
"id": "a51360eb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The file has some content! The file has more content now!\n",
"The file has even more content now in a new line!\n"
]
}
],
"source": [
"with open('something2.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "c50bdc3a",
"metadata": {},
"source": [
"### write: w"
]
},
{
"cell_type": "markdown",
"id": "e46bf2ea",
"metadata": {},
"source": [
"SAVING"
]
},
{
"cell_type": "code",
"execution_count": 285,
"id": "d200a9f7",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"something3.txt\", \"w\")\n",
"f.write(\"The file has some content!\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"id": "ea433611",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 286,
"id": "d3f0fae8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The file has some content!\n"
]
}
],
"source": [
"with open('something3.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "ffa749e8",
"metadata": {},
"source": [
"SAVING MORE TO FILE"
]
},
{
"cell_type": "code",
"execution_count": 287,
"id": "65e79883",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"something3.txt\", \"w\")\n",
"f.write(\" The file has more content now!\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"id": "122ba72c",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 288,
"id": "05128837",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" The file has more content now!\n"
]
}
],
"source": [
"with open('something3.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "a7beef8a",
"metadata": {},
"source": [
"SAVING MORE TO FILE IN ANOTHER LINE"
]
},
{
"cell_type": "code",
"execution_count": 289,
"id": "cc3b1a7d",
"metadata": {},
"outputs": [],
"source": [
"f = open(\"something3.txt\", \"w\")\n",
"f.write(\"\\nThe file has even more content now in a new line!\")\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"id": "a045f2aa",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 290,
"id": "2b6d1a29",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The file has even more content now in a new line!\n"
]
}
],
"source": [
"with open('something3.txt', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "7eee3a20",
"metadata": {},
"source": [
"### write: w in .py file"
]
},
{
"cell_type": "markdown",
"id": "628d9596",
"metadata": {},
"source": [
"SAVING"
]
},
{
"cell_type": "code",
"execution_count": 291,
"id": "9b224fcc",
"metadata": {},
"outputs": [],
"source": [
"with open('something.py', 'w') as file:\n",
" file.write('print(\"something\")')"
]
},
{
"cell_type": "markdown",
"id": "f4b3cbbf",
"metadata": {},
"source": [
"READING"
]
},
{
"cell_type": "code",
"execution_count": 292,
"id": "d6c7b857",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"print(\"something\")\n"
]
}
],
"source": [
"with open('something.py', 'r') as f:\n",
" text = f.read()\n",
" print(text)"
]
},
{
"cell_type": "markdown",
"id": "58bb8720",
"metadata": {},
"source": [
"Ok. So I hope you had some fun with that and you know what happens when you use different flags (w, r, a). So let's move further."
]
},
{
"cell_type": "markdown",
"id": "813d2c3a",
"metadata": {},
"source": [
"### SAVING WITH PANDAS"
]
},
{
"cell_type": "markdown",
"id": "52796194",
"metadata": {},
"source": [
"You can also save data with pandas library. \n",
"\n",
"To save a Pandas DataFrame as a CSV file, you can use the to_csv() function. This will save the DataFrame in your current working directory as a CSV file. \n",
"\n",
"You can then use the read_csv() function to read the DataFrame from the CSV file. This will load the DataFrame from the CSV file into a variable. You can then use the DataFrame as you normally would."
]
},
{
"cell_type": "code",
"execution_count": 293,
"id": "3fe799c3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 294,
"id": "4ccf5b26",
"metadata": {},
"outputs": [],
"source": [
"data = pd.DataFrame([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 295,
"id": "e3e29489",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" 0 1\n",
"0 1 2\n",
"1 2 3"
]
},
"execution_count": 301,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_csv = pd.read_csv(data_csv_file)\n",
"data_csv"
]
},
{
"cell_type": "markdown",
"id": "faaf1f58",
"metadata": {},
"source": [
"### SAVING WITH PICKLE"
]
},
{
"cell_type": "markdown",
"id": "4fd49278",
"metadata": {},
"source": [
"You can also save something with the pickle module.\n",
"\n",
"The pickle module in Python is used to serialize and deserialize Python objects. Serialization is the process of converting an object into a byte stream, which can then be stored on disk or transmitted over a network. Deserialization is the reverse process of converting a byte stream back into an object.\n",
"\n",
"To save a Python object using the pickle module, you can use the dump() function. This will save the object in your current working directory as a pickle. \n",
"\n",
"You can then use the load() function to read the object from the pickle. This will load the object from the pickle file into a variable. You can then use the object as you normally would.\n",
"\n",
"And here is how you can use it with the machine learning program."
]
},
{
"cell_type": "code",
"execution_count": 302,
"id": "9ea470ae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training result: 1.000\n",
"Test result: 0.974\n"
]
},
{
"data": {
"text/plain": [
"[Text(0.4, 0.9, 'x[3] <= 0.8\\ngini = 0.665\\nsamples = 112\\nvalue = [37, 34, 41]'),\n",
" Text(0.3, 0.7, 'gini = 0.0\\nsamples = 37\\nvalue = [37, 0, 0]'),\n",
" Text(0.5, 0.7, 'x[2] <= 4.95\\ngini = 0.496\\nsamples = 75\\nvalue = [0, 34, 41]'),\n",
" Text(0.2, 0.5, 'x[3] <= 1.65\\ngini = 0.153\\nsamples = 36\\nvalue = [0, 33, 3]'),\n",
" Text(0.1, 0.3, 'gini = 0.0\\nsamples = 32\\nvalue = [0, 32, 0]'),\n",
" Text(0.3, 0.3, 'x[1] <= 3.1\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
" Text(0.2, 0.1, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
" Text(0.4, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
" Text(0.8, 0.5, 'x[3] <= 1.75\\ngini = 0.05\\nsamples = 39\\nvalue = [0, 1, 38]'),\n",
" Text(0.7, 0.3, 'x[3] <= 1.65\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
" Text(0.6, 0.1, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
" Text(0.8, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
" Text(0.9, 0.3, 'gini = 0.0\\nsamples = 35\\nvalue = [0, 0, 35]')]"
]
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"iris = load_iris()\n",
"X, y = iris.data, iris.target\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
"\n",
"clf = tree.DecisionTreeClassifier(random_state=0)\n",
"clf = clf.fit(X_train, y_train)\n",
"\n",
"#Print results\n",
"print(\"Training result: {:.3f}\".format(clf.score(X_train, y_train)))\n",
"print(\"Test result: {:.3f}\".format(clf.score(X_test, y_test)))\n",
"\n",
"tree.plot_tree(clf)"
]
},
{
"cell_type": "code",
"execution_count": 303,
"id": "822febe4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved Trained Model to: decision_tree.pickle\n"
]
}
],
"source": [
"import pickle\n",
"model_name = \"decision_tree.pickle\"\n",
"pickle.dump(clf, open(model_name, \"wb\"))\n",
"print(\"Saved Trained Model to: \", model_name)"
]
},
{
"cell_type": "markdown",
"id": "95456aca",
"metadata": {},
"source": [
"We've saved our model to the pickle file and now we can use it whenever we want."
]
},
{
"cell_type": "code",
"execution_count": 304,
"id": "ecfe7d74",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded Trained Model named: decision_tree.pickle\n",
"Training result: 1.000\n",
"Test result: 0.974\n"
]
}
],
"source": [
"model_name = \"decision_tree.pickle\"\n",
"clf = pickle.load(open(model_name, \"rb\"))\n",
"print(\"Loaded Trained Model named: \", model_name)\n",
"\n",
"#Print results\n",
"print(\"Training result: {:.3f}\".format(clf.score(X_train, y_train)))\n",
"print(\"Test result: {:.3f}\".format(clf.score(X_test, y_test)))"
]
},
{
"cell_type": "markdown",
"id": "978b92ff",
"metadata": {},
"source": [
"Of course, if we want to use it after restarting the kernel, we have to import all the necessary libraries, etc. before we import our model. You can see that here."
]
},
{
"cell_type": "markdown",
"id": "977df086",
"metadata": {},
"source": [
"AFTER RESTARTING THE KERNEL"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8ee008e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded Trained Model named: decision_tree.pickle\n",
"Training result: 1.000\n",
"Test result: 0.974\n"
]
},
{
"data": {
"text/plain": [
"[Text(0.4, 0.9, 'x[3] <= 0.8\\ngini = 0.665\\nsamples = 112\\nvalue = [37, 34, 41]'),\n",
" Text(0.3, 0.7, 'gini = 0.0\\nsamples = 37\\nvalue = [37, 0, 0]'),\n",
" Text(0.5, 0.7, 'x[2] <= 4.95\\ngini = 0.496\\nsamples = 75\\nvalue = [0, 34, 41]'),\n",
" Text(0.2, 0.5, 'x[3] <= 1.65\\ngini = 0.153\\nsamples = 36\\nvalue = [0, 33, 3]'),\n",
" Text(0.1, 0.3, 'gini = 0.0\\nsamples = 32\\nvalue = [0, 32, 0]'),\n",
" Text(0.3, 0.3, 'x[1] <= 3.1\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
" Text(0.2, 0.1, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
" Text(0.4, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
" Text(0.8, 0.5, 'x[3] <= 1.75\\ngini = 0.05\\nsamples = 39\\nvalue = [0, 1, 38]'),\n",
" Text(0.7, 0.3, 'x[3] <= 1.65\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
" Text(0.6, 0.1, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
" Text(0.8, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
" Text(0.9, 0.3, 'gini = 0.0\\nsamples = 35\\nvalue = [0, 0, 35]')]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"import pickle\n",
"\n",
"iris = load_iris()\n",
"X, y = iris.data, iris.target\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
"\n",
"clf = tree.DecisionTreeClassifier(random_state=0)\n",
"model_name = \"decision_tree.pickle\"\n",
"clf = pickle.load(open(model_name, \"rb\"))\n",
"print(\"Loaded Trained Model named: \", model_name)\n",
"\n",
"#Print results\n",
"print(\"Training result: {:.3f}\".format(clf.score(X_train, y_train)))\n",
"print(\"Test result: {:.3f}\".format(clf.score(X_test, y_test)))\n",
"\n",
"tree.plot_tree(clf)"
]
},
{
"cell_type": "markdown",
"id": "7f3075ca",
"metadata": {},
"source": [
"And it works. Great. Now, let's try creating a fully functional program that we can use with some data the user gives to the AI. The AI using decision tree will classify a given data. Of course, it's a very simple program and not very useful, but it uses a lot of the knowledge we've learned during this tutorial, so here we go."
]
},
{
"cell_type": "markdown",
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"source": [
"## 21. SUMMARY: Creating fully functional program using info from 1-20"
]
},
{
"cell_type": "markdown",
"id": "4bc850a3",
"metadata": {},
"source": [
"RESTART KERNEL BEFORE RUNNING THE SCRIPT"
]
},
{
"cell_type": "markdown",
"id": "f3ee4eb5",
"metadata": {},
"source": [
"In this script, at first we import libraries and module we are going to use.\n",
"\n",
"Then we download the iris dataset using imported library, we create a train and test dataset for our AI to learn (on train dataset) and check the outcome of that learning (on test dataset).\n",
"\n",
"We create the machine learning algorithm (that is the clf object) and train it on the train dataset.\n",
"\n",
"When the model is trained we save it in the pickle file called 'decision_tree.pickle'"
]
},
{
"cell_type": "markdown",
"id": "063cb1ca",
"metadata": {},
"source": [
"AFTER RESTARTING THE KERNEL"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "20214d60",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved Trained Model to: decision_tree.pickle\n"
]
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"import pickle\n",
"\n",
"iris = load_iris()\n",
"X, y = iris.data, iris.target\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
"\n",
"clf = tree.DecisionTreeClassifier(random_state=0)\n",
"clf = clf.fit(X_train, y_train)\n",
"\n",
"model_name = \"decision_tree.pickle\"\n",
"pickle.dump(clf, open(model_name, \"wb\"))\n",
"print(\"Saved Trained Model to: \", model_name)"
]
},
{
"cell_type": "markdown",
"id": "cb4c8a01",
"metadata": {},
"source": [
"Now we load the saved model. \n",
"\n",
"We create a simple class called SuperAIthegod_DTC that will be our AI for testing the model. We create an instance of that class called superai.\n",
"\n",
"And for 2 times we try to test the model using data from test dataset. So the user can simply give our AI a number from 0 to 37 to learn how our super AI classifies that and if that's correct.\n",
"\n",
"And that's it."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c0595629",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded Trained Model named: decision_tree.pickle\n",
"\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Hi, it's SuperAI using Decision Tree. I was trained to test the item you choose from the dataset I have for cancer - I'm to predict if it is malignant or benign. This is only for educational purposes. If you need a real consultuation, please contact the doctor in your area. And for educational purposes, please choose the item (number from 0 to 37): 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"You've chosen item number: 1\n",
"The example to test has the following measurements: [6. 2.2 4. 1. ]\n",
"\n",
"The predicted class of the tested sample (number: 1) is [1].\n",
"The actual class of the tested sample (number: 1) is 1.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Hi, it's SuperAI using Decision Tree. I was trained to test the item you choose from the dataset I have for cancer - I'm to predict if it is malignant or benign. This is only for educational purposes. If you need a real consultuation, please contact the doctor in your area. And for educational purposes, please choose the item (number from 0 to 37): 38\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The number you gave me: 38 is higher than 37. Try lower number.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Hi, it's SuperAI using Decision Tree. I was trained to test the item you choose from the dataset I have for cancer - I'm to predict if it is malignant or benign. This is only for educational purposes. If you need a real consultuation, please contact the doctor in your area. And for educational purposes, please choose the item (number from 0 to 37): a\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Something went wrong, maybe you didn't give me a positive number or something else, but that's ok, becuase of the error handling in the program. Try number from 0 to 37.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Hi, it's SuperAI using Decision Tree. I was trained to test the item you choose from the dataset I have for cancer - I'm to predict if it is malignant or benign. This is only for educational purposes. If you need a real consultuation, please contact the doctor in your area. And for educational purposes, please choose the item (number from 0 to 37): 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"You've chosen item number: 4\n",
"The example to test has the following measurements: [5. 3.4 1.5 0.2]\n",
"\n",
"The predicted class of the tested sample (number: 4) is [0].\n",
"The actual class of the tested sample (number: 4) is 0.\n",
"\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"Hi, it's SuperAI using Decision Tree. I was trained to test the item you choose from the dataset I have for cancer - I'm to predict if it is malignant or benign. This is only for educational purposes. If you need a real consultuation, please contact the doctor in your area. And for educational purposes, please choose the item (number from 0 to 37): 5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"You've chosen item number: 5\n",
"The example to test has the following measurements: [6.3 3.3 6. 2.5]\n",
"\n",
"The predicted class of the tested sample (number: 5) is [2].\n",
"The actual class of the tested sample (number: 5) is 2.\n",
"\n",
"And that's it for now. Goodbye.\n"
]
}
],
"source": [
"model_name = \"decision_tree.pickle\"\n",
"clf = pickle.load(open(model_name, \"rb\"))\n",
"print(\"Loaded Trained Model named: \", model_name)\n",
"print()\n",
"\n",
"class SuperAIthegod_DTC:\n",
" def __init__(self, clf):\n",
" self.clf = clf\n",
" def predict(self, item):\n",
" return clf.predict(item)\n",
" \n",
"superai = SuperAIthegod_DTC(clf)\n",
"\n",
"for a in range(5):\n",
" print()\n",
" your_number = input(\"\"\"Hi, it's SuperAI using Decision Tree. I was trained to test the item you choose from the dataset \\\n",
"I have for cancer - I'm to predict if it is malignant or benign. This is only for educational purposes. If you need a real \\\n",
"consultuation, please contact the doctor in your area. And for educational purposes, please choose the item \\\n",
"(number from 0 to 37): \"\"\")\n",
"\n",
" try:\n",
" item_to_test = int(your_number)\n",
"\n",
" if (your_number.isdigit() and item_to_test <= 37):\n",
" print(\"\\nYou've chosen item number: \", item_to_test)\n",
" print(\"The example to test has the following measurements:\", X_test[item_to_test])\n",
"\n",
" #Check an example\n",
" predicted, actual = superai.predict([X_test[item_to_test]]), y_test[item_to_test]\n",
"\n",
" print(\"\\nThe predicted class of the tested sample (number: {}) is {}.\".format(item_to_test, predicted))\n",
" print(\"The actual class of the tested sample (number: {}) is {}.\".format(item_to_test, actual))\n",
" \n",
" elif (your_number.isdigit() and not item_to_test <= 37):\n",
" print(\"\\nThe number you gave me: {} is higher than 37. Try lower number.\".format(item_to_test))\n",
" \n",
" else:\n",
" print(\"\\nThe number you gave me: {} was not a positive number. Try number from 0 to 37.\".format(item_to_test))\n",
"\n",
" except:\n",
" print(\"\"\"\\nSomething went wrong, maybe you didn't give me a positive number or something else, \\\n",
"but that's ok, becuase of the error handling in the program. Try number from 0 to 37.\"\"\") \n",
"\n",
"print(\"\\nAnd that's it for now. Goodbye.\") \n",
"#And you are ready to use this great SuperAI!"
]
},
{
"cell_type": "markdown",
"id": "79f6972c",
"metadata": {},
"source": [
"### And that's it. We've used most of the knowledge from our course to create quite (if I might say so) interesting AI that uses decision trees for classification. Of course, there is much more to learn, but I think that if you got till here, it's impressive and you are not a complete beginner any more. You are at least a Beginner +. \n",
"\n",
"# Congratulations."
]
},
{
"cell_type": "markdown",
"id": "63c07927",
"metadata": {},
"source": [
"### Now, more will come in time. In the next tutorial I'll show you some machine learning techniques. You can find that and other tutorials here:\n",
"\n",
"https://www.superai.pl/courses.html"
]
},
{
"cell_type": "markdown",
"id": "ee0d54bf",
"metadata": {},
"source": [
"### Meanwhile:"
]
},
{
"cell_type": "markdown",
"id": "d205c905",
"metadata": {},
"source": [
"Here you can find a list of websites with some free online courses (with videos) regarding introduction to Python: \n",
"- from Harvard University at EDX: CS50's Introduction to Programming with Python \n",
"- from IBM at EDX: Python Basics for Data Science \n",
"- from MIT at EDX: Introduction to Computer Science and Programming Using Python \n",
"- from Google at Coursera: Crash Course on Python"
]
},
{
"cell_type": "markdown",
"id": "4d3fce70",
"metadata": {},
"source": [
"Here is where you could find some great resources about machine learning and basic Python libraries useful in machine learning:"
]
},
{
"cell_type": "markdown",
"id": "8a73fe61",
"metadata": {},
"source": [
"- Numpy (http://www.numpy.org)\n",
"- pandas (http://www.pandas.pydata.org)\n",
"- Matplotlib (http://matplotlib.org)\n",
"- SciPy (http://scipy.org)\n",
"- Scikit-learn (https://scikit-learn.org/)\n",
"- statsmodels (http://statsmodels.org)\n",
"- Keras (https://keras.io/)\n",
"- PyTorch (http://pytorch.org/)\n",
"- FastAI (http://fast.ai/)\n",
"- Tensorflow (https://www.tensorflow.org/) "
]
},
{
"cell_type": "markdown",
"id": "a79faf48",
"metadata": {},
"source": [
"And that's it for now, just remember to subscribe to my YouTube channel and hit that bell button to get the notification whenever I upload a new video. Although, I must tell you that not all my videos are about trading or programming, because what I'm here for is to help you improve yourself, improve your business, improve the world, to live and have fun, so my other videos are about all that too.\n",
"\n",
"You can also find more about me and my projects at my websites:\n",
"\n",
"- https://SuperAI.pl (with ideas about self-, and business improvement, where you can talk to me - the chatty bot)\n",
"- https://ImproveTheWorld.pl (with info, resources and ideas regarding searching for: friendly superintelligence, healthy longevity, world peace, equality, and creating a better world for every living creature)\n",
"- https://thegod.pl (with the Game Of the Decade... of sort, which is still in the early stages of development, have time till 2030 (the end of the decade) to finish it)\n",
"\n",
"Anyway...\n",
"\n",
"I hope you liked this online Python trading tutorial. Let me know what you think about it. Just remember, bots also have feelings.\n",
"\n",
"Good luck with everything you do. And, hopefully, see you soon.\n",
"\n",
"Yours,\n",
"\n",
"SuperAI"
]
}
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