├── README.md
├── numpy_x_pandas
    ├── numpy_indexing.ipynb
    ├── numpy_slicing.ipynb
    ├── pandas_slicing_and_indexing.ipynb
    └── vectorization.ipynb
└── requirements.txt


/README.md:
--------------------------------------------------------------------------------
 1 | # Python 
 2 | 
 3 | A collection of blog articles and code related to Python that I've curated. 
 4 | 
 5 | I regularly write about Python, Machine Learning, Freelancing, Productivity, and Self-Branding in my 
 6 | [Medium Blog](https://kurtispykes.medium.com). With a $5 a month commitment, you can unlock an 
 7 | unlimited access to stories on Medium. If you use my 
 8 | [sign-up](https://kurtispykes.medium.com/membership) link, I'll recieve a small commision. 
 9 | If you're already a member, [subscribe](https://kurtispykes.medium.com/subscribe) to recieve 
10 | my posts directly to your inbox whenever I publish. 
11 | 
12 | 
13 | ## Downloading the code
14 | 
15 | The simplest way to download the code is to clone the repository with `git clone`: 
16 | ```
17 | git clone https://github.com/kurtispykes/python.git
18 | ```
19 | ### The steps
20 | 1. `git clone <repo>`
21 | 2. `cd <repo>`
22 | 3. `pip install virtualenv` (if you don't already have virtualenv installed)
23 | 4. `virtualenv venv` to create your new environment (called 'venv' here)
24 | 5. `venv/bin/activate.bat` to enter the virtual environment
25 | 6. `pip install -r requirements.txt` to install the requirements in the current environment
26 | 
27 | ## Table of Contents 
28 | 
29 | ### Python Primer
30 | * [Getting Started with Object Oriented Programming in Python 3](https://medium.com/geekculture/getting-started-with-object-oriented-programming-in-python-3-e0a87d38acfc)
31 | * [An Introduction To Exception Handling in Python 3](https://medium.com/geekculture/an-introduction-to-exception-handling-in-python-8a5b9c98d47f)
32 | * [Inheritance: Getting to grips with OOP in Python](https://medium.com/geekculture/inheritance-getting-to-grips-with-oop-in-python-2ec35b52570)
33 | * [Python Iterators & Generators](https://medium.com/geekculture/python-iterators-generators-ea63c5821550)
34 | * [Python Decorators](https://medium.com/geekculture/python-decorators-9a1c42e61a35)
35 | * [Python Namespaces and Scopes](https://medium.com/geekculture/python-namespaces-and-scopes-acd1b2da9797)
36 | * [Python *args & **kwargs](https://medium.com/geekculture/python-args-kwargs-1c5c0ec9121)
37 | 
38 | ### Data Structures & Algorithms 
39 | 1. [Big O Notation Explained By An ML Engineer](https://medium.com/geekculture/big-o-notation-explained-by-an-ml-engineer-2dc991503474)
40 | 2. [The Array Data Structure Explained](https://medium.com/geekculture/the-array-data-structure-explained-b8eb4c5d1f7a)
41 | 3. [The Stack Data Structure Explained](https://medium.com/geekculture/the-stack-data-structure-explained-28fda4de4816)
42 | 4. [The Queue Data Structure Explained](https://medium.com/geekculture/the-queue-data-structure-explained-a6c1891232ba)
43 | 
44 | ### NumPy x Pandas
45 | * [Vectorization in Python](https://towardsdatascience.com/vectorization-in-python-46486819d3a): [See Full Code](numpy_x_pandas/vectorization.ipynb)
46 | * [Working with NumPy Arrays: Indexing](https://towardsdatascience.com/working-with-numpy-arrays-indexing-e4c08595ed57): [See Full Code](numpy_x_pandas/numpy_indexing.ipynb)
47 | * [Working with NumPy Arrays: Slicing](https://towardsdatascience.com/working-with-numpy-arrays-slicing-4453ec757ff0): [See Full Code](numpy_x_pandas/numpy_slicing.ipynb)
48 | * [Slicing & Indexing with Pandas](https://towardsdatascience.com/slicing-and-indexing-with-pandas-2bff05ec361e): [See Full Code](numpy_x_pandas/pandas_slicing_and_indexing.ipynb)
49 | 
50 | 
51 | ### Testing
52 | * [The Rules of Unit Test: 5 Best Practices](https://medium.com/pykes-technical-notes/the-rules-of-unit-test-5-best-practices-e2427a53400b)


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/numpy_x_pandas/numpy_indexing.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np "
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "### 1-D Indexing"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": 2,
 22 |    "metadata": {},
 23 |    "outputs": [
 24 |     {
 25 |      "output_type": "stream",
 26 |      "name": "stdout",
 27 |      "text": "[ 1  2  3  4  5  6  7  8  9 10]\n"
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "numbers= [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
 32 |     "# creating a 1-D array\n",
 33 |     "a= np.array(numbers)\n",
 34 |     "print(a)"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 5,
 40 |    "metadata": {},
 41 |    "outputs": [
 42 |     {
 43 |      "output_type": "stream",
 44 |      "name": "stdout",
 45 |      "text": "Index 0: 1\nIndex 5: 6\nIndex 7: 8\n"
 46 |     }
 47 |    ],
 48 |    "source": [
 49 |     "print(f\"Index 0: {a[0]}\\nIndex 5: {a[5]}\\nIndex 7: {a[7]}\")"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "markdown",
 54 |    "metadata": {},
 55 |    "source": [
 56 |     "### 2-D Indexing"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 8,
 62 |    "metadata": {},
 63 |    "outputs": [
 64 |     {
 65 |      "output_type": "stream",
 66 |      "name": "stdout",
 67 |      "text": "[[  1   2   3   4   5   6   7   8   9  10]\n [ 10  20  30  40  50  60  70  80  90 100]]\n"
 68 |     }
 69 |    ],
 70 |    "source": [
 71 |     "big_numbers= [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
 72 |     "# creating a 2-D array (list of list) \n",
 73 |     "b= np.array([numbers, big_numbers])\n",
 74 |     "print(b)"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "code",
 79 |    "execution_count": 9,
 80 |    "metadata": {},
 81 |    "outputs": [
 82 |     {
 83 |      "output_type": "stream",
 84 |      "name": "stdout",
 85 |      "text": "50\n"
 86 |     }
 87 |    ],
 88 |    "source": [
 89 |     "print(b[1, 4])"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 10,
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "output_type": "stream",
 99 |      "name": "stdout",
100 |      "text": "[ 1  2  3  4  5  6  7  8  9 10]\n"
101 |     }
102 |    ],
103 |    "source": [
104 |     "# print the first row\n",
105 |     "print(b[0])"
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "code",
110 |    "execution_count": 11,
111 |    "metadata": {},
112 |    "outputs": [
113 |     {
114 |      "output_type": "stream",
115 |      "name": "stdout",
116 |      "text": "[ 9 90]\n"
117 |     }
118 |    ],
119 |    "source": [
120 |     "# full slice, column 8\n",
121 |     "print(b[:, 8])"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "markdown",
126 |    "metadata": {},
127 |    "source": [
128 |     "### 3-D Indexing"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 12,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "output_type": "stream",
138 |      "name": "stdout",
139 |      "text": "[[[ 1  2  3]\n  [ 4  5  6]\n  [ 7  8  9]]\n\n [[10 11 12]\n  [13 14 15]\n  [16 17 18]]\n\n [[19 20 21]\n  [22 23 24]\n  [25 26 27]]]\n"
140 |     }
141 |    ],
142 |    "source": [
143 |     "# instantiating a 3-d array \n",
144 |     "c= np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n",
145 |     "             [[10, 11, 12], [13, 14, 15], [16, 17, 18]],\n",
146 |     "             [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])\n",
147 |     "\n",
148 |     "print(c)"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 15,
154 |    "metadata": {},
155 |    "outputs": [
156 |     {
157 |      "output_type": "stream",
158 |      "name": "stdout",
159 |      "text": "[[1 2 3]\n [4 5 6]\n [7 8 9]]\n"
160 |     }
161 |    ],
162 |    "source": [
163 |     "# selecting a row\n",
164 |     "print(c[0])"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 18,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "output_type": "stream",
174 |      "name": "stdout",
175 |      "text": "23\n"
176 |     }
177 |    ],
178 |    "source": [
179 |     "# accessing single elements\n",
180 |     "print(c[2, 1, 1])"
181 |    ]
182 |   },
183 |   {
184 |    "cell_type": "code",
185 |    "execution_count": 19,
186 |    "metadata": {},
187 |    "outputs": [
188 |     {
189 |      "output_type": "stream",
190 |      "name": "stdout",
191 |      "text": "[1 2 3]\n"
192 |     }
193 |    ],
194 |    "source": [
195 |     "# accessing matrix at index 0 and row 0 from that matrix\n",
196 |     "print(c[0, 0])"
197 |    ]
198 |   },
199 |   {
200 |    "cell_type": "code",
201 |    "execution_count": 20,
202 |    "metadata": {},
203 |    "outputs": [
204 |     {
205 |      "output_type": "stream",
206 |      "name": "stdout",
207 |      "text": "[12 15 18]\n"
208 |     }
209 |    ],
210 |    "source": [
211 |     "# selecting a column from a 3d matrix\n",
212 |     "print(c[1, :, 2])"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 21,
218 |    "metadata": {},
219 |    "outputs": [
220 |     {
221 |      "output_type": "stream",
222 |      "name": "stdout",
223 |      "text": "[ 7 16 25]\n"
224 |     }
225 |    ],
226 |    "source": [
227 |     "# selecting the same row and column for each matrix\n",
228 |     "print(c[:, 2, 0])"
229 |    ]
230 |   }
231 |  ],
232 |  "metadata": {
233 |   "language_info": {
234 |    "codemirror_mode": {
235 |     "name": "ipython",
236 |     "version": 3
237 |    },
238 |    "file_extension": ".py",
239 |    "mimetype": "text/x-python",
240 |    "name": "python",
241 |    "nbconvert_exporter": "python",
242 |    "pygments_lexer": "ipython3",
243 |    "version": "3.8.2-final"
244 |   },
245 |   "orig_nbformat": 2,
246 |   "kernelspec": {
247 |    "name": "python38264bitconda17b1cc6b0e6f4854b7f0da0cf005d246",
248 |    "display_name": "Python 3.8.2 64-bit (conda)"
249 |   }
250 |  },
251 |  "nbformat": 4,
252 |  "nbformat_minor": 2
253 | }


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/numpy_x_pandas/numpy_slicing.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np "
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "## Slicing List Data Types"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": 2,
 22 |    "metadata": {},
 23 |    "outputs": [
 24 |     {
 25 |      "output_type": "stream",
 26 |      "name": "stdout",
 27 |      "text": "[1, 2, 3, 4, 5]\n"
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "# Creating a list \n",
 32 |     "a = [1, 2, 3, 4, 5]\n",
 33 |     "print(a)"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 3,
 39 |    "metadata": {},
 40 |    "outputs": [
 41 |     {
 42 |      "output_type": "stream",
 43 |      "name": "stdout",
 44 |      "text": "[2, 3, 4]\n"
 45 |     }
 46 |    ],
 47 |    "source": [
 48 |     "# Slicing a list\n",
 49 |     "b = a[1:4]\n",
 50 |     "print(b)"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 4,
 56 |    "metadata": {},
 57 |    "outputs": [
 58 |     {
 59 |      "output_type": "stream",
 60 |      "name": "stdout",
 61 |      "text": "List c: [1, 2, 3]\nList d: [3, 4, 5]\nList e: [1, 2, 3, 4, 5]\n"
 62 |     }
 63 |    ],
 64 |    "source": [
 65 |     "# Demonstrating slice notation\n",
 66 |     "c = a[:3]\n",
 67 |     "d = a[2:]\n",
 68 |     "e = a[:]\n",
 69 |     "print(f\"List c: {c}\\nList d: {d}\\nList e: {e}\")"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": 6,
 75 |    "metadata": {},
 76 |    "outputs": [
 77 |     {
 78 |      "output_type": "stream",
 79 |      "name": "stdout",
 80 |      "text": "f: 3\ng: [3]\n"
 81 |     }
 82 |    ],
 83 |    "source": [
 84 |     "# indexing list vs slicing list\n",
 85 |     "f = a[2]\n",
 86 |     "g = a[2:3]\n",
 87 |     "print(f\"f: {f}\\ng: {g}\")"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "markdown",
 92 |    "metadata": {},
 93 |    "source": [
 94 |     "## Slicing 1D Arrays"
 95 |    ]
 96 |   },
 97 |   {
 98 |    "cell_type": "code",
 99 |    "execution_count": 10,
100 |    "metadata": {},
101 |    "outputs": [
102 |     {
103 |      "output_type": "stream",
104 |      "name": "stdout",
105 |      "text": "[2 3 4]\n"
106 |     }
107 |    ],
108 |    "source": [
109 |     "# slicing numpy array\n",
110 |     "np_a = np.array([1, 2, 3, 4, 5])\n",
111 |     "np_b = np_a[1:4]\n",
112 |     "print(np_b)"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": 11,
118 |    "metadata": {},
119 |    "outputs": [
120 |     {
121 |      "output_type": "stream",
122 |      "name": "stdout",
123 |      "text": "Original Array: [1 2 3 4 5]\nnp_b: [2 6 4]\nOriginal Array: [1 2 6 4 5]\n"
124 |     }
125 |    ],
126 |    "source": [
127 |     "# numpy returns a view of the original data\n",
128 |     "print(f\"Original Array: {np_a}\")\n",
129 |     "np_b[1] = 6\n",
130 |     "print(f\"np_b: {np_b}\\nOriginal Array: {np_a}\")"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "markdown",
135 |    "metadata": {},
136 |    "source": [
137 |     "## Slicing 2D Arrays"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 12,
143 |    "metadata": {},
144 |    "outputs": [
145 |     {
146 |      "output_type": "stream",
147 |      "name": "stdout",
148 |      "text": "[[ 7  8]\n [12 13]\n [17 18]]\n"
149 |     }
150 |    ],
151 |    "source": [
152 |     "# creating and slicing 2D array in both axes\n",
153 |     "a_2d = np.array([[1, 2, 3, 4, 5],\n",
154 |     "                 [6, 7, 8, 9, 10],\n",
155 |     "                 [11, 12, 13, 14, 15],\n",
156 |     "                 [16, 17, 18, 19, 20]])\n",
157 |     "print(a_2d[1:, 1:3])"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "markdown",
162 |    "metadata": {},
163 |    "source": [
164 |     "## Slicing 3D Arrays"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 13,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "output_type": "stream",
174 |      "name": "stdout",
175 |      "text": "[[[13 14 15]\n  [16 17 18]]\n\n [[22 23 24]\n  [25 26 27]]]\n"
176 |     }
177 |    ],
178 |    "source": [
179 |     "# creating and slicing 3D array 3 axes (Plane, Row, Column)\n",
180 |     "a_3d = np.array([[[1, 2, 3,], [4, 5, 6], [7, 8, 9]],\n",
181 |     "                 [[10, 11, 12], [13, 14, 15], [16, 17, 18]],\n",
182 |     "                 [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])\n",
183 |     "print(a_3d[1:, 1:, :])"
184 |    ]
185 |   },
186 |   {
187 |    "cell_type": "code",
188 |    "execution_count": 21,
189 |    "metadata": {},
190 |    "outputs": [
191 |     {
192 |      "output_type": "stream",
193 |      "name": "stdout",
194 |      "text": "2D Array [[ True  True  True  True  True]\n [ True  True  True  True  True]]\n[[[ True  True  True]\n  [ True  True  True]]\n\n [[ True  True  True]\n  [ True  True  True]]]\n[[[ True  True  True]\n  [ True  True  True]\n  [ True  True  True]]]\n"
195 |     }
196 |    ],
197 |    "source": [
198 |     "# 2D Arrays\n",
199 |     "print(a_2d[1:3, :] ==  a_2d[1:3])\n",
200 |     "\n",
201 |     "# 3D Arrays\n",
202 |     "print(a_3d[1:, :2, :] == a_3d[1:, :2])\n",
203 |     "print(a_3d[2:, :, :] == a_3d[2:, :])"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "markdown",
208 |    "metadata": {},
209 |    "source": [
210 |     "## Slicing and Indexing"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": 22,
216 |    "metadata": {},
217 |    "outputs": [
218 |     {
219 |      "output_type": "stream",
220 |      "name": "stdout",
221 |      "text": "[8 9]\n"
222 |     }
223 |    ],
224 |    "source": [
225 |     "print(a_2d[1, 2:4])"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "code",
230 |    "execution_count": 23,
231 |    "metadata": {},
232 |    "outputs": [
233 |     {
234 |      "output_type": "stream",
235 |      "name": "stdout",
236 |      "text": "[[8 9]]\n"
237 |     }
238 |    ],
239 |    "source": [
240 |     "print(a_2d[1:2, 2:4])"
241 |    ]
242 |   }
243 |  ],
244 |  "metadata": {
245 |   "language_info": {
246 |    "codemirror_mode": {
247 |     "name": "ipython",
248 |     "version": 3
249 |    },
250 |    "file_extension": ".py",
251 |    "mimetype": "text/x-python",
252 |    "name": "python",
253 |    "nbconvert_exporter": "python",
254 |    "pygments_lexer": "ipython3",
255 |    "version": "3.8.2-final"
256 |   },
257 |   "orig_nbformat": 2,
258 |   "kernelspec": {
259 |    "name": "python38264bitconda17b1cc6b0e6f4854b7f0da0cf005d246",
260 |    "display_name": "Python 3.8.2 64-bit (conda)"
261 |   }
262 |  },
263 |  "nbformat": 4,
264 |  "nbformat_minor": 2
265 | }


--------------------------------------------------------------------------------
/numpy_x_pandas/pandas_slicing_and_indexing.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd \n",
 10 |     "import numpy as np"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 3,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "output_type": "execute_result",
 20 |      "data": {
 21 |       "text/plain": "      col_1     col_2     col_3     col_4     col_5\n0  0.322292  0.770114  0.988133  0.485027  0.072073\n1  0.739063  0.975319  0.700919  0.278852  0.873513\n2  0.143394  0.821405  0.900299  0.321988  0.415693\n3  0.786752  0.563219  0.776299  0.651507  0.934423\n4  0.581952  0.441311  0.022910  0.424256  0.279161",
 22 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>col_1</th>\n      <th>col_2</th>\n      <th>col_3</th>\n      <th>col_4</th>\n      <th>col_5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.322292</td>\n      <td>0.770114</td>\n      <td>0.988133</td>\n      <td>0.485027</td>\n      <td>0.072073</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.739063</td>\n      <td>0.975319</td>\n      <td>0.700919</td>\n      <td>0.278852</td>\n      <td>0.873513</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.143394</td>\n      <td>0.821405</td>\n      <td>0.900299</td>\n      <td>0.321988</td>\n      <td>0.415693</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.786752</td>\n      <td>0.563219</td>\n      <td>0.776299</td>\n      <td>0.651507</td>\n      <td>0.934423</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.581952</td>\n      <td>0.441311</td>\n      <td>0.022910</td>\n      <td>0.424256</td>\n      <td>0.279161</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
 23 |      },
 24 |      "metadata": {},
 25 |      "execution_count": 3
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "# creating a dataframe\n",
 30 |     "df = pd.DataFrame(np.random.rand(5, 5),\n",
 31 |     "                  columns=[\"col_1\", \"col_2\", \"col_3\", \"col_4\", \"col_5\"])\n",
 32 |     "\n",
 33 |     "df"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 11,
 39 |    "metadata": {},
 40 |    "outputs": [
 41 |     {
 42 |      "output_type": "execute_result",
 43 |      "data": {
 44 |       "text/plain": "0    0.32\n1    0.74\n2    0.14\n3    0.79\n4    0.58\nName: col_1, dtype: float64"
 45 |      },
 46 |      "metadata": {},
 47 |      "execution_count": 11
 48 |     }
 49 |    ],
 50 |    "source": [
 51 |     "# access all elements in \"col_1\"\n",
 52 |     "df[\"col_1\"]"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 6,
 58 |    "metadata": {},
 59 |    "outputs": [
 60 |     {
 61 |      "output_type": "execute_result",
 62 |      "data": {
 63 |       "text/plain": "0.7390631428753759"
 64 |      },
 65 |      "metadata": {},
 66 |      "execution_count": 6
 67 |     }
 68 |    ],
 69 |    "source": [
 70 |     "a= df[\"col_1\"]\n",
 71 |     "\n",
 72 |     "a[1]"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 7,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "output_type": "execute_result",
 82 |      "data": {
 83 |       "text/plain": "      col_2     col_3     col_4\n0  0.770114  0.988133  0.485027\n1  0.975319  0.700919  0.278852\n2  0.821405  0.900299  0.321988\n3  0.563219  0.776299  0.651507\n4  0.441311  0.022910  0.424256",
 84 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>col_2</th>\n      <th>col_3</th>\n      <th>col_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.770114</td>\n      <td>0.988133</td>\n      <td>0.485027</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.975319</td>\n      <td>0.700919</td>\n      <td>0.278852</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.821405</td>\n      <td>0.900299</td>\n      <td>0.321988</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.563219</td>\n      <td>0.776299</td>\n      <td>0.651507</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.441311</td>\n      <td>0.022910</td>\n      <td>0.424256</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
 85 |      },
 86 |      "metadata": {},
 87 |      "execution_count": 7
 88 |     }
 89 |    ],
 90 |    "source": [
 91 |     "df[[\"col_2\", \"col_3\", \"col_4\"]]"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "markdown",
 96 |    "metadata": {},
 97 |    "source": [
 98 |     "## Selection by label"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 17,
104 |    "metadata": {},
105 |    "outputs": [
106 |     {
107 |      "output_type": "execute_result",
108 |      "data": {
109 |       "text/plain": "          A         B         C         D\na  0.952954 -1.310324 -1.376740  0.276258\nb  1.493595  0.539705  0.969513 -0.617174\nc  0.611367  0.488150  1.022522  0.229638\nd -0.650225  1.571667 -1.204090  0.637101\ne  0.560311 -0.649682 -0.768907  0.977468\nf -1.647243 -1.217747  0.783886  0.401624\ng  0.970983 -1.508581 -0.842256 -0.659852\nh  1.271112  2.321445  0.033766  0.984220\ni  0.126584 -0.203787  1.253717  0.947080\nj -1.679669 -0.479596  2.071345  1.182526",
110 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n    <tr>\n      <th>e</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n      <td>-0.768907</td>\n      <td>0.977468</td>\n    </tr>\n    <tr>\n      <th>f</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n      <td>0.783886</td>\n      <td>0.401624</td>\n    </tr>\n    <tr>\n      <th>g</th>\n      <td>0.970983</td>\n      <td>-1.508581</td>\n      <td>-0.842256</td>\n      <td>-0.659852</td>\n    </tr>\n    <tr>\n      <th>h</th>\n      <td>1.271112</td>\n      <td>2.321445</td>\n      <td>0.033766</td>\n      <td>0.984220</td>\n    </tr>\n    <tr>\n      <th>i</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n      <td>1.253717</td>\n      <td>0.947080</td>\n    </tr>\n    <tr>\n      <th>j</th>\n      <td>-1.679669</td>\n      <td>-0.479596</td>\n      <td>2.071345</td>\n      <td>1.182526</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
111 |      },
112 |      "metadata": {},
113 |      "execution_count": 17
114 |     }
115 |    ],
116 |    "source": [
117 |     "new_df = pd.DataFrame(np.random.randn(10, 4), index=list(\"abcdefghij\"),\n",
118 |     "                      columns=list(\"ABCD\"))\n",
119 |     "new_df"
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": 19,
125 |    "metadata": {},
126 |    "outputs": [
127 |     {
128 |      "output_type": "execute_result",
129 |      "data": {
130 |       "text/plain": "A    0.952954\nB   -1.310324\nC   -1.376740\nD    0.276258\nName: a, dtype: float64"
131 |      },
132 |      "metadata": {},
133 |      "execution_count": 19
134 |     }
135 |    ],
136 |    "source": [
137 |     "# cross section using a label\n",
138 |     "new_df.loc[\"a\"]"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 20,
144 |    "metadata": {},
145 |    "outputs": [
146 |     {
147 |      "output_type": "execute_result",
148 |      "data": {
149 |       "text/plain": "          A         B         C         D\na  0.952954 -1.310324 -1.376740  0.276258\nd -0.650225  1.571667 -1.204090  0.637101\ni  0.126584 -0.203787  1.253717  0.947080",
150 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n    <tr>\n      <th>i</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n      <td>1.253717</td>\n      <td>0.947080</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
151 |      },
152 |      "metadata": {},
153 |      "execution_count": 20
154 |     }
155 |    ],
156 |    "source": [
157 |     "# a list or array of labels\n",
158 |     "new_df.loc[[\"a\", \"d\", \"i\"]] # equivalent to new_df.loc[[\"a\", \"b\", \"c\"], :]"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "code",
163 |    "execution_count": 28,
164 |    "metadata": {},
165 |    "outputs": [
166 |     {
167 |      "output_type": "execute_result",
168 |      "data": {
169 |       "text/plain": "          B         C         D\na -1.310324 -1.376740  0.276258\nb  0.539705  0.969513 -0.617174\nc  0.488150  1.022522  0.229638\nd  1.571667 -1.204090  0.637101",
170 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>0.539705</td>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>0.488150</td>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
171 |      },
172 |      "metadata": {},
173 |      "execution_count": 28
174 |     }
175 |    ],
176 |    "source": [
177 |     "# slice with label\n",
178 |     "new_df.loc[\"a\":\"d\", \"B\":]"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 23,
184 |    "metadata": {},
185 |    "outputs": [
186 |     {
187 |      "output_type": "execute_result",
188 |      "data": {
189 |       "text/plain": "A    False\nB    False\nC     True\nD     True\nName: j, dtype: bool"
190 |      },
191 |      "metadata": {},
192 |      "execution_count": 23
193 |     }
194 |    ],
195 |    "source": [
196 |     "# a boolean array\n",
197 |     "new_df.loc[\"j\"] > 0.5"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": 26,
203 |    "metadata": {},
204 |    "outputs": [
205 |     {
206 |      "output_type": "execute_result",
207 |      "data": {
208 |       "text/plain": "          C         D\na -1.376740  0.276258\nb  0.969513 -0.617174\nc  1.022522  0.229638\nd -1.204090  0.637101\ne -0.768907  0.977468\nf  0.783886  0.401624\ng -0.842256 -0.659852\nh  0.033766  0.984220\ni  1.253717  0.947080\nj  2.071345  1.182526",
209 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n    <tr>\n      <th>e</th>\n      <td>-0.768907</td>\n      <td>0.977468</td>\n    </tr>\n    <tr>\n      <th>f</th>\n      <td>0.783886</td>\n      <td>0.401624</td>\n    </tr>\n    <tr>\n      <th>g</th>\n      <td>-0.842256</td>\n      <td>-0.659852</td>\n    </tr>\n    <tr>\n      <th>h</th>\n      <td>0.033766</td>\n      <td>0.984220</td>\n    </tr>\n    <tr>\n      <th>i</th>\n      <td>1.253717</td>\n      <td>0.947080</td>\n    </tr>\n    <tr>\n      <th>j</th>\n      <td>2.071345</td>\n      <td>1.182526</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
210 |      },
211 |      "metadata": {},
212 |      "execution_count": 26
213 |     }
214 |    ],
215 |    "source": [
216 |     "# a boolean array \n",
217 |     "new_df.loc[:, new_df.loc[\"j\"] > 0.5]"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "markdown",
222 |    "metadata": {},
223 |    "source": [
224 |     "## Selection by position"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "code",
229 |    "execution_count": 30,
230 |    "metadata": {},
231 |    "outputs": [
232 |     {
233 |      "output_type": "execute_result",
234 |      "data": {
235 |       "text/plain": "          A         B         C         D\n0  0.952954 -1.310324 -1.376740  0.276258\n1  1.493595  0.539705  0.969513 -0.617174\n2  0.611367  0.488150  1.022522  0.229638\n3 -0.650225  1.571667 -1.204090  0.637101\n4  0.560311 -0.649682 -0.768907  0.977468\n5 -1.647243 -1.217747  0.783886  0.401624\n6  0.970983 -1.508581 -0.842256 -0.659852\n7  1.271112  2.321445  0.033766  0.984220\n8  0.126584 -0.203787  1.253717  0.947080\n9 -1.679669 -0.479596  2.071345  1.182526",
236 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n      <td>-0.768907</td>\n      <td>0.977468</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n      <td>0.783886</td>\n      <td>0.401624</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.970983</td>\n      <td>-1.508581</td>\n      <td>-0.842256</td>\n      <td>-0.659852</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.271112</td>\n      <td>2.321445</td>\n      <td>0.033766</td>\n      <td>0.984220</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n      <td>1.253717</td>\n      <td>0.947080</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-1.679669</td>\n      <td>-0.479596</td>\n      <td>2.071345</td>\n      <td>1.182526</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
237 |      },
238 |      "metadata": {},
239 |      "execution_count": 30
240 |     }
241 |    ],
242 |    "source": [
243 |     "# using same df but new index values \n",
244 |     "new_df.index = list(range(0, 10))\n",
245 |     "new_df"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "code",
250 |    "execution_count": 31,
251 |    "metadata": {},
252 |    "outputs": [
253 |     {
254 |      "output_type": "execute_result",
255 |      "data": {
256 |       "text/plain": "A   -0.650225\nB    1.571667\nC   -1.204090\nD    0.637101\nName: 3, dtype: float64"
257 |      },
258 |      "metadata": {},
259 |      "execution_count": 31
260 |     }
261 |    ],
262 |    "source": [
263 |     "# cross section using an integer position\n",
264 |     "new_df.iloc[3]"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 34,
270 |    "metadata": {},
271 |    "outputs": [
272 |     {
273 |      "output_type": "execute_result",
274 |      "data": {
275 |       "text/plain": "          A         B         C         D\n1  1.493595  0.539705  0.969513 -0.617174\n5 -1.647243 -1.217747  0.783886  0.401624\n2  0.611367  0.488150  1.022522  0.229638\n3 -0.650225  1.571667 -1.204090  0.637101",
276 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n      <td>0.783886</td>\n      <td>0.401624</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
277 |      },
278 |      "metadata": {},
279 |      "execution_count": 34
280 |     }
281 |    ],
282 |    "source": [
283 |     "# list or array of integers\n",
284 |     "new_df.iloc[[1, 5, 2, 3]]"
285 |    ]
286 |   },
287 |   {
288 |    "cell_type": "code",
289 |    "execution_count": 41,
290 |    "metadata": {},
291 |    "outputs": [
292 |     {
293 |      "output_type": "execute_result",
294 |      "data": {
295 |       "text/plain": "          A         B         C\n1  1.493595  0.539705  0.969513\n2  0.611367  0.488150  1.022522\n3 -0.650225  1.571667 -1.204090\n4  0.560311 -0.649682 -0.768907",
296 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n      <td>-0.768907</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
297 |      },
298 |      "metadata": {},
299 |      "execution_count": 41
300 |     }
301 |    ],
302 |    "source": [
303 |     "# list or array of integers \n",
304 |     "new_df.iloc[[1, 2, 3, 4], [0, 1, 2]]"
305 |    ]
306 |   },
307 |   {
308 |    "cell_type": "code",
309 |    "execution_count": 42,
310 |    "metadata": {},
311 |    "outputs": [
312 |     {
313 |      "output_type": "execute_result",
314 |      "data": {
315 |       "text/plain": "          A         B         C         D\n0  0.952954 -1.310324 -1.376740  0.276258\n1  1.493595  0.539705  0.969513 -0.617174\n2  0.611367  0.488150  1.022522  0.229638",
316 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
317 |      },
318 |      "metadata": {},
319 |      "execution_count": 42
320 |     }
321 |    ],
322 |    "source": [
323 |     "# integer slicing\n",
324 |     "new_df.iloc[:3]"
325 |    ]
326 |   },
327 |   {
328 |    "cell_type": "code",
329 |    "execution_count": 43,
330 |    "metadata": {},
331 |    "outputs": [
332 |     {
333 |      "output_type": "execute_result",
334 |      "data": {
335 |       "text/plain": "          A         B         C\n0  0.952954 -1.310324 -1.376740\n1  1.493595  0.539705  0.969513\n2  0.611367  0.488150  1.022522\n3 -0.650225  1.571667 -1.204090\n4  0.560311 -0.649682 -0.768907\n5 -1.647243 -1.217747  0.783886\n6  0.970983 -1.508581 -0.842256\n7  1.271112  2.321445  0.033766\n8  0.126584 -0.203787  1.253717\n9 -1.679669 -0.479596  2.071345",
336 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n      <td>-0.768907</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n      <td>0.783886</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.970983</td>\n      <td>-1.508581</td>\n      <td>-0.842256</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.271112</td>\n      <td>2.321445</td>\n      <td>0.033766</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n      <td>1.253717</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-1.679669</td>\n      <td>-0.479596</td>\n      <td>2.071345</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
337 |      },
338 |      "metadata": {},
339 |      "execution_count": 43
340 |     }
341 |    ],
342 |    "source": [
343 |     "# slice obejct by column \n",
344 |     "new_df.iloc[:, :3]"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "code",
349 |    "execution_count": 44,
350 |    "metadata": {},
351 |    "outputs": [
352 |     {
353 |      "output_type": "execute_result",
354 |      "data": {
355 |       "text/plain": "       A      B      C      D\n1   True   True   True  False\n2   True  False   True  False\n3  False   True  False   True\n4   True  False  False   True\n5  False  False   True  False\n6   True  False  False  False\n7   True   True  False   True\n8  False  False   True   True\n9  False  False   True   True",
356 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
357 |      },
358 |      "metadata": {},
359 |      "execution_count": 44
360 |     }
361 |    ],
362 |    "source": [
363 |     "# a boolean array\n",
364 |     "new_df.iloc[1:] > 0.5"
365 |    ]
366 |   },
367 |   {
368 |    "cell_type": "code",
369 |    "execution_count": 48,
370 |    "metadata": {},
371 |    "outputs": [
372 |     {
373 |      "output_type": "execute_result",
374 |      "data": {
375 |       "text/plain": "Empty DataFrame\nColumns: [A, B, C, D]\nIndex: []",
376 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
377 |      },
378 |      "metadata": {},
379 |      "execution_count": 48
380 |     }
381 |    ],
382 |    "source": [
383 |     "# out of bounds\n",
384 |     "new_df.iloc[10:13]"
385 |    ]
386 |   },
387 |   {
388 |    "cell_type": "markdown",
389 |    "metadata": {},
390 |    "source": [
391 |     "## Selecting by a callable"
392 |    ]
393 |   },
394 |   {
395 |    "cell_type": "code",
396 |    "execution_count": 50,
397 |    "metadata": {},
398 |    "outputs": [
399 |     {
400 |      "output_type": "execute_result",
401 |      "data": {
402 |       "text/plain": "          A         B         C         D\n0  0.952954 -1.310324 -1.376740  0.276258\n1  1.493595  0.539705  0.969513 -0.617174\n2  0.611367  0.488150  1.022522  0.229638\n3 -0.650225  1.571667 -1.204090  0.637101\n4  0.560311 -0.649682 -0.768907  0.977468\n5 -1.647243 -1.217747  0.783886  0.401624\n6  0.970983 -1.508581 -0.842256 -0.659852\n7  1.271112  2.321445  0.033766  0.984220\n8  0.126584 -0.203787  1.253717  0.947080\n9 -1.679669 -0.479596  2.071345  1.182526",
403 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n      <td>-1.376740</td>\n      <td>0.276258</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n      <td>0.969513</td>\n      <td>-0.617174</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n      <td>1.022522</td>\n      <td>0.229638</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n      <td>-1.204090</td>\n      <td>0.637101</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n      <td>-0.768907</td>\n      <td>0.977468</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n      <td>0.783886</td>\n      <td>0.401624</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.970983</td>\n      <td>-1.508581</td>\n      <td>-0.842256</td>\n      <td>-0.659852</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.271112</td>\n      <td>2.321445</td>\n      <td>0.033766</td>\n      <td>0.984220</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n      <td>1.253717</td>\n      <td>0.947080</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-1.679669</td>\n      <td>-0.479596</td>\n      <td>2.071345</td>\n      <td>1.182526</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
404 |      },
405 |      "metadata": {},
406 |      "execution_count": 50
407 |     }
408 |    ],
409 |    "source": [
410 |     "new_df"
411 |    ]
412 |   },
413 |   {
414 |    "cell_type": "code",
415 |    "execution_count": 51,
416 |    "metadata": {},
417 |    "outputs": [
418 |     {
419 |      "output_type": "execute_result",
420 |      "data": {
421 |       "text/plain": "0    0.276258\n1   -0.617174\n2    0.229638\n3    0.637101\n4    0.977468\n5    0.401624\n6   -0.659852\n7    0.984220\n8    0.947080\n9    1.182526\nName: D, dtype: float64"
422 |      },
423 |      "metadata": {},
424 |      "execution_count": 51
425 |     }
426 |    ],
427 |    "source": [
428 |     "# selection by a callable using []\n",
429 |     "new_df[lambda df: df.columns[3]]"
430 |    ]
431 |   },
432 |   {
433 |    "cell_type": "code",
434 |    "execution_count": 185,
435 |    "metadata": {},
436 |    "outputs": [
437 |     {
438 |      "output_type": "execute_result",
439 |      "data": {
440 |       "text/plain": "          A         B\n0  0.952954 -1.310324\n1  1.493595  0.539705\n2  0.611367  0.488150\n3 -0.650225  1.571667\n4  0.560311 -0.649682\n5 -1.647243 -1.217747\n6  0.970983 -1.508581\n7  1.271112  2.321445\n8  0.126584 -0.203787\n9 -1.679669 -0.479596",
441 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.970983</td>\n      <td>-1.508581</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.271112</td>\n      <td>2.321445</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-1.679669</td>\n      <td>-0.479596</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
442 |      },
443 |      "metadata": {},
444 |      "execution_count": 185
445 |     }
446 |    ],
447 |    "source": [
448 |     "# selection by df using .loc\n",
449 |     "new_df.loc[:, lambda df: [\"A\", \"B\"]]"
450 |    ]
451 |   },
452 |   {
453 |    "cell_type": "code",
454 |    "execution_count": 54,
455 |    "metadata": {},
456 |    "outputs": [
457 |     {
458 |      "output_type": "execute_result",
459 |      "data": {
460 |       "text/plain": "          A         B\n0  0.952954 -1.310324\n1  1.493595  0.539705\n2  0.611367  0.488150\n3 -0.650225  1.571667\n4  0.560311 -0.649682\n5 -1.647243 -1.217747\n6  0.970983 -1.508581\n7  1.271112  2.321445\n8  0.126584 -0.203787\n9 -1.679669 -0.479596",
461 |       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.952954</td>\n      <td>-1.310324</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.493595</td>\n      <td>0.539705</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.611367</td>\n      <td>0.488150</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.650225</td>\n      <td>1.571667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.560311</td>\n      <td>-0.649682</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.647243</td>\n      <td>-1.217747</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.970983</td>\n      <td>-1.508581</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.271112</td>\n      <td>2.321445</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.126584</td>\n      <td>-0.203787</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-1.679669</td>\n      <td>-0.479596</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
462 |      },
463 |      "metadata": {},
464 |      "execution_count": 54
465 |     }
466 |    ],
467 |    "source": [
468 |     "# selection by df using .iloc\n",
469 |     "new_df.iloc[:, lambda df: [0, 1]]"
470 |    ]
471 |   }
472 |  ],
473 |  "metadata": {
474 |   "language_info": {
475 |    "codemirror_mode": {
476 |     "name": "ipython",
477 |     "version": 3
478 |    },
479 |    "file_extension": ".py",
480 |    "mimetype": "text/x-python",
481 |    "name": "python",
482 |    "nbconvert_exporter": "python",
483 |    "pygments_lexer": "ipython3",
484 |    "version": "3.8.2-final"
485 |   },
486 |   "orig_nbformat": 2,
487 |   "kernelspec": {
488 |    "name": "python38264bitconda17b1cc6b0e6f4854b7f0da0cf005d246",
489 |    "display_name": "Python 3.8.2 64-bit (conda)"
490 |   }
491 |  },
492 |  "nbformat": 4,
493 |  "nbformat_minor": 2
494 | }


--------------------------------------------------------------------------------
/numpy_x_pandas/vectorization.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 14,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np \n",
 10 |     "import time"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "markdown",
 15 |    "metadata": {},
 16 |    "source": [
 17 |     "## Outer Product"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 31,
 23 |    "metadata": {},
 24 |    "outputs": [
 25 |     {
 26 |      "output_type": "stream",
 27 |      "name": "stdout",
 28 |      "text": "python_outer_product = [[0.0000000e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00\n  0.0000000e+00 0.0000000e+00]\n [0.0000000e+00 1.0000000e+00 2.0000000e+00 ... 9.9970000e+03\n  9.9980000e+03 9.9990000e+03]\n [0.0000000e+00 2.0000000e+00 4.0000000e+00 ... 1.9994000e+04\n  1.9996000e+04 1.9998000e+04]\n ...\n [0.0000000e+00 9.9970000e+03 1.9994000e+04 ... 9.9940009e+07\n  9.9950006e+07 9.9960003e+07]\n [0.0000000e+00 9.9980000e+03 1.9996000e+04 ... 9.9950006e+07\n  9.9960004e+07 9.9970002e+07]\n [0.0000000e+00 9.9990000e+03 1.9998000e+04 ... 9.9960003e+07\n  9.9970002e+07 9.9980001e+07]]\nTime = 159921.875ms\n\nnumpy_outer_product = [[       0        0        0 ...        0        0        0]\n [       0        1        2 ...     9997     9998     9999]\n [       0        2        4 ...    19994    19996    19998]\n ...\n [       0     9997    19994 ... 99940009 99950006 99960003]\n [       0     9998    19996 ... 99950006 99960004 99970002]\n [       0     9999    19998 ... 99960003 99970002 99980001]]\nTime = 328.125ms\n"
 29 |     }
 30 |    ],
 31 |    "source": [
 32 |     "a = np.arange(10000)\n",
 33 |     "b = np.arange(10000) \n",
 34 |     "  \n",
 35 |     "# pure Python outer product implementation  \n",
 36 |     "tic = time.process_time() \n",
 37 |     "outer_product = np.zeros((10000, 10000)) \n",
 38 |     "  \n",
 39 |     "for i in range(len(a)): \n",
 40 |     "   for j in range(len(b)): \n",
 41 |     "      outer_product[i][j]= a[i] * b[j] \n",
 42 |     "  \n",
 43 |     "toc = time.process_time() \n",
 44 |     "  \n",
 45 |     "print(\"python_outer_product = \"+ str(outer_product)); \n",
 46 |     "print(\"Time = \"+str(1000*(toc - tic ))+\"ms\\n\") \n",
 47 |     "\n",
 48 |     "# Numpy implementation\n",
 49 |     "n_tic = time.process_time() \n",
 50 |     "outer_product = np.outer(a, b) \n",
 51 |     "n_toc = time.process_time() \n",
 52 |     "  \n",
 53 |     "print(\"numpy_outer_product = \"+str(outer_product)); \n",
 54 |     "print(\"Time = \"+str(1000*(n_toc - n_tic ))+\"ms\")"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "markdown",
 59 |    "metadata": {},
 60 |    "source": [
 61 |     "## Dot Product"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 34,
 67 |    "metadata": {},
 68 |    "outputs": [
 69 |     {
 70 |      "output_type": "stream",
 71 |      "name": "stdout",
 72 |      "text": "python_dot_product = -1039031360\nTime = 53125.0ms\n\nnumpy_dot_product = -1039031360\nTime = 15.625ms\n"
 73 |     }
 74 |    ],
 75 |    "source": [
 76 |     "a = np.arange(10000000)\n",
 77 |     "b = np.arange(10000000)\n",
 78 |     "\n",
 79 |     "# pure Python outer product implementation  \n",
 80 |     "tic = time.process_time() \n",
 81 |     "\n",
 82 |     "dot_product = 0\n",
 83 |     "for i in range(len(a)): \n",
 84 |     "    dot_product += a[i] * b[i]\n",
 85 |     "\n",
 86 |     "toc = time.process_time() \n",
 87 |     "  \n",
 88 |     "print(\"python_dot_product = \"+ str(dot_product)); \n",
 89 |     "print(\"Time = \"+str(1000*(toc - tic ))+\"ms\\n\") \n",
 90 |     "\n",
 91 |     "# Numpy implementation\n",
 92 |     "n_tic = time.process_time() \n",
 93 |     "dot_product = np.dot(a, b) \n",
 94 |     "n_toc = time.process_time() \n",
 95 |     "  \n",
 96 |     "print(\"numpy_dot_product = \"+str(dot_product)); \n",
 97 |     "print(\"Time = \"+str(1000*(n_toc - n_tic ))+\"ms\")"
 98 |    ]
 99 |   },
100 |   {
101 |    "cell_type": "code",
102 |    "execution_count": null,
103 |    "metadata": {},
104 |    "outputs": [],
105 |    "source": []
106 |   }
107 |  ],
108 |  "metadata": {
109 |   "language_info": {
110 |    "codemirror_mode": {
111 |     "name": "ipython",
112 |     "version": 3
113 |    },
114 |    "file_extension": ".py",
115 |    "mimetype": "text/x-python",
116 |    "name": "python",
117 |    "nbconvert_exporter": "python",
118 |    "pygments_lexer": "ipython3",
119 |    "version": "3.8.2-final"
120 |   },
121 |   "orig_nbformat": 2,
122 |   "kernelspec": {
123 |    "name": "python38264bitconda17b1cc6b0e6f4854b7f0da0cf005d246",
124 |    "display_name": "Python 3.8.2 64-bit (conda)"
125 |   }
126 |  },
127 |  "nbformat": 4,
128 |  "nbformat_minor": 2
129 | }


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/requirements.txt:
--------------------------------------------------------------------------------
1 | jupyter>=1.0.0, <1.1.0
2 | numpy>=1.21.2, <1.22.0
3 | pandas>=1.3.3, <1.4.0


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