├── Bayesian Inference Python Statistics.ipynb ├── Cross validation Python Statistics Training.ipynb ├── Email Analyticas.ipynb ├── Goodness Of Fit.ipynb ├── Hypothesis Testing in Python Statistics.ipynb ├── Intro To distributions_begin.ipynb ├── Intro To histograms The Engineering World_begin.ipynb ├── Logistic Regression.ipynb ├── Planets.xls ├── Plot Categorical Variables.ipynb ├── Ploting Two Quantitative variables_begin.ipynb ├── Plotting More Variables.ipynb ├── README.md ├── Short Example challenge.ipynb ├── Statistics Categorical Data.ipynb ├── Visualization Of GaPminder Data begin.ipynb ├── billboard.csv ├── billboard.xls ├── bootstrapping In Python Statistics.ipynb ├── cholera.xls ├── cleaning Data For Beginners.ipynb ├── cleaning Data _end.ipynb ├── final.xls ├── fitting Models To Data.ipynb ├── gapminder.xls ├── grades.xls ├── hadleydataset.xls ├── hadleydataset2.xls ├── income-1965-china.xls ├── income-1965-usa.xls ├── income-2015-china.xls ├── income-2015-usa.xls ├── london.png ├── mbox-anonymized.xls ├── p values and confidence in Python Statistics.ipynb ├── pandas Beginners Guide The Engineering World.ipynb ├── poll-larger.csv ├── poll-larger.xls ├── poll.csv ├── poll.xls ├── pumps.xls └── whickham.xls /Bayesian Inference Python Statistics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# The Engineering World - A Place For Learning And Exploring\n", 8 | "\n", 9 | "## Bayesian Inference " 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import math" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "import numpy as np\n", 35 | "import pandas as pd" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 3, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "import matplotlib\n", 45 | "import matplotlib.pyplot as pp" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "%matplotlib inline" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 5, 60 | "metadata": {}, 61 | "outputs": [ 62 | { 63 | "ename": "ModuleNotFoundError", 64 | "evalue": "No module named 'pymc3'", 65 | "output_type": "error", 66 | "traceback": [ 67 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 68 | "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", 69 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpymc3\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 70 | "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pymc3'" 71 | ] 72 | } 73 | ], 74 | "source": [ 75 | "import pymc3 as pm" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": null, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [] 84 | } 85 | ], 86 | "metadata": { 87 | "kernelspec": { 88 | "display_name": "Python 3", 89 | "language": "python", 90 | "name": "python3" 91 | }, 92 | "language_info": { 93 | "codemirror_mode": { 94 | "name": "ipython", 95 | "version": 3 96 | }, 97 | "file_extension": ".py", 98 | "mimetype": "text/x-python", 99 | "name": "python", 100 | "nbconvert_exporter": "python", 101 | "pygments_lexer": "ipython3", 102 | "version": "3.6.5" 103 | }, 104 | "toc": { 105 | "base_numbering": 1, 106 | "nav_menu": {}, 107 | "number_sections": true, 108 | "sideBar": true, 109 | "skip_h1_title": false, 110 | "title_cell": "Table of Contents", 111 | "title_sidebar": "Contents", 112 | "toc_cell": false, 113 | "toc_position": {}, 114 | "toc_section_display": true, 115 | "toc_window_display": false 116 | } 117 | }, 118 | "nbformat": 4, 119 | "nbformat_minor": 2 120 | } 121 | -------------------------------------------------------------------------------- /Email Analyticas.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## The Engineering WOrld - A Place For Learning And Exploring\n", 8 | "\n", 9 | "# Email Data Analysis" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 12, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import scipy.stats\n", 27 | "import pandas as pd" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 13, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "import matplotlib\n", 37 | "import matplotlib.pyplot as pp\n", 38 | "\n", 39 | "import pandas.plotting\n", 40 | "\n", 41 | "from IPython import display\n", 42 | "from ipywidgets import interact, widgets\n", 43 | "\n", 44 | "%matplotlib inline" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 14, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "import re\n", 54 | "import mailbox\n", 55 | "import csv" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "### How I converted my mailbox." 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 3, 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "mbox = mailbox.mbox('Sent.mbox')" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "The resulting object is array-like, with one entry per message. Each entry is dictionary like, with keys corresponding to metadata and data for each message." 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 4, 84 | "metadata": {}, 85 | "outputs": [ 86 | { 87 | "data": { 88 | "text/plain": [ 89 | "['User-Agent',\n", 90 | " 'Date',\n", 91 | " 'Subject',\n", 92 | " 'From',\n", 93 | " 'To',\n", 94 | " 'CC',\n", 95 | " 'Message-ID',\n", 96 | " 'Thread-Topic',\n", 97 | " 'References',\n", 98 | " 'In-Reply-To',\n", 99 | " 'Content-Type',\n", 100 | " 'Content-Transfer-Encoding',\n", 101 | " 'MIME-Version']" 102 | ] 103 | }, 104 | "execution_count": 4, 105 | "metadata": {}, 106 | "output_type": "execute_result" 107 | } 108 | ], 109 | "source": [ 110 | "mbox[0].keys()" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "The easiest way to get these data into Pandas is to build a CSV file from them. We use the module `csv` to write out the CSV file as we loop over the mailbox object. We save only subject, from, to, and date, and we write a simple header at the top with the names of columns." 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": null, 123 | "metadata": {}, 124 | "outputs": [], 125 | "source": [ 126 | "with open('mbox.csv', 'w') as outfile:\n", 127 | " writer = csv.writer(outfile)\n", 128 | " writer.writerow(['subject','from','to','date'])\n", 129 | " \n", 130 | " for message in mbox:\n", 131 | " writer.writerow([message['subject'], message['from'], message['to'], message['date']])" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "All done! Thanks to Justin Ellis for inspiration with https://jellis18.github.io/post/2018-01-17-mail-analysis." 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "## Moving on!" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [] 154 | } 155 | ], 156 | "metadata": { 157 | "kernelspec": { 158 | "display_name": "Python [default]", 159 | "language": "python", 160 | "name": "python3" 161 | }, 162 | "language_info": { 163 | "codemirror_mode": { 164 | "name": "ipython", 165 | "version": 3 166 | }, 167 | "file_extension": ".py", 168 | "mimetype": "text/x-python", 169 | "name": "python", 170 | "nbconvert_exporter": "python", 171 | "pygments_lexer": "ipython3", 172 | "version": "3.6.5" 173 | }, 174 | "toc": { 175 | "base_numbering": 1, 176 | "nav_menu": {}, 177 | "number_sections": true, 178 | "sideBar": true, 179 | "skip_h1_title": false, 180 | "title_cell": "Table of Contents", 181 | "title_sidebar": "Contents", 182 | "toc_cell": false, 183 | "toc_position": {}, 184 | "toc_section_display": true, 185 | "toc_window_display": false 186 | } 187 | }, 188 | "nbformat": 4, 189 | "nbformat_minor": 2 190 | } 191 | -------------------------------------------------------------------------------- /Hypothesis Testing in Python Statistics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Python statistics essential training - 04_04_testing" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Standard imports" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 5, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import math\n", 24 | "import io" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 6, 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "import numpy as np\n", 34 | "import pandas as pd\n", 35 | "\n", 36 | "import matplotlib\n", 37 | "import matplotlib.pyplot as pp\n", 38 | "\n", 39 | "%matplotlib inline" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 7, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "import scipy.stats\n", 49 | "import scipy.optimize\n", 50 | "import scipy.spatial" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": null, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [] 59 | } 60 | ], 61 | "metadata": { 62 | "kernelspec": { 63 | "display_name": "Python 3", 64 | "language": "python", 65 | "name": "python3" 66 | }, 67 | "language_info": { 68 | "codemirror_mode": { 69 | "name": "ipython", 70 | "version": 3 71 | }, 72 | "file_extension": ".py", 73 | "mimetype": "text/x-python", 74 | "name": "python", 75 | "nbconvert_exporter": "python", 76 | "pygments_lexer": "ipython3", 77 | "version": "3.6.4" 78 | }, 79 | "toc": { 80 | "base_numbering": 1, 81 | "nav_menu": {}, 82 | "number_sections": true, 83 | "sideBar": true, 84 | "skip_h1_title": false, 85 | "title_cell": "Table of Contents", 86 | "title_sidebar": "Contents", 87 | "toc_cell": false, 88 | "toc_position": {}, 89 | "toc_section_display": true, 90 | "toc_window_display": false 91 | } 92 | }, 93 | "nbformat": 4, 94 | "nbformat_minor": 2 95 | } 96 | -------------------------------------------------------------------------------- /Intro To distributions_begin.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## The Engineering WOrld - A Place For Learning And Exploring\n", 8 | "\n", 9 | "# Python statistics essential training" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import scipy.stats\n", 27 | "import pandas as pd" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "import matplotlib\n", 37 | "import matplotlib.pyplot as pp\n", 38 | "\n", 39 | "from IPython import display\n", 40 | "from ipywidgets import interact, widgets\n", 41 | "\n", 42 | "%matplotlib inline" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 3, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "import re\n", 52 | "import mailbox\n", 53 | "import csv" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 4, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "china1965 = pd.read_csv('income-1965-china.csv')\n", 63 | "china2015 = pd.read_csv('income-2015-china.csv')\n", 64 | "usa1965 = pd.read_csv('income-1965-usa.csv')\n", 65 | "usa2015 = pd.read_csv('income-2015-usa.csv')" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 5, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "name": "stdout", 75 | "output_type": "stream", 76 | "text": [ 77 | "\n", 78 | "RangeIndex: 1000 entries, 0 to 999\n", 79 | "Data columns (total 2 columns):\n", 80 | "income 1000 non-null float64\n", 81 | "log10_income 1000 non-null float64\n", 82 | "dtypes: float64(2)\n", 83 | "memory usage: 15.7 KB\n" 84 | ] 85 | } 86 | ], 87 | "source": [ 88 | "china1965.info()" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 6, 94 | "metadata": {}, 95 | "outputs": [ 96 | { 97 | "data": { 98 | "text/html": [ 99 | "
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incomelog10_income
01.0262590.011257
10.912053-0.039980
20.110699-0.955857
30.469659-0.328217
40.374626-0.426402
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" 150 | ], 151 | "text/plain": [ 152 | " income log10_income\n", 153 | "0 1.026259 0.011257\n", 154 | "1 0.912053 -0.039980\n", 155 | "2 0.110699 -0.955857\n", 156 | "3 0.469659 -0.328217\n", 157 | "4 0.374626 -0.426402" 158 | ] 159 | }, 160 | "execution_count": 6, 161 | "metadata": {}, 162 | "output_type": "execute_result" 163 | } 164 | ], 165 | "source": [ 166 | "china1965.head()" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 7, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "data": { 176 | "text/plain": [ 177 | "income 0.041968\n", 178 | "log10_income -1.377078\n", 179 | "dtype: float64" 180 | ] 181 | }, 182 | "execution_count": 7, 183 | "metadata": {}, 184 | "output_type": "execute_result" 185 | } 186 | ], 187 | "source": [ 188 | "china1965.min()" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 8, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "data": { 198 | "text/plain": [ 199 | "income 5.426802\n", 200 | "log10_income 0.734544\n", 201 | "dtype: float64" 202 | ] 203 | }, 204 | "execution_count": 8, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | } 208 | ], 209 | "source": [ 210 | "china1965.max()" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 9, 216 | "metadata": {}, 217 | "outputs": [ 218 | { 219 | "data": { 220 | "text/plain": [ 221 | "income 0.660597\n", 222 | "log10_income -0.274157\n", 223 | "dtype: float64" 224 | ] 225 | }, 226 | "execution_count": 9, 227 | "metadata": {}, 228 | "output_type": "execute_result" 229 | } 230 | ], 231 | "source": [ 232 | "china1965.mean()" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 10, 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/plain": [ 243 | "income 0.208846\n", 244 | "log10_income 0.088610\n", 245 | "dtype: float64" 246 | ] 247 | }, 248 | "execution_count": 10, 249 | "metadata": {}, 250 | "output_type": "execute_result" 251 | } 252 | ], 253 | "source": [ 254 | "china1965.var(ddof=0)" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 12, 260 | "metadata": {}, 261 | "outputs": [ 262 | { 263 | "data": { 264 | "text/html": [ 265 | "
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incomelog10_income
0.250.344130-0.463277
0.750.863695-0.063640
\n", 300 | "
" 301 | ], 302 | "text/plain": [ 303 | " income log10_income\n", 304 | "0.25 0.344130 -0.463277\n", 305 | "0.75 0.863695 -0.063640" 306 | ] 307 | }, 308 | "execution_count": 12, 309 | "metadata": {}, 310 | "output_type": "execute_result" 311 | } 312 | ], 313 | "source": [ 314 | "china1965.quantile([0.25, 0.75])" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 13, 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "data": { 324 | "text/plain": [ 325 | "income 0.557477\n", 326 | "log10_income -0.253773\n", 327 | "Name: 0.5, dtype: float64" 328 | ] 329 | }, 330 | "execution_count": 13, 331 | "metadata": {}, 332 | "output_type": "execute_result" 333 | } 334 | ], 335 | "source": [ 336 | "china1965.quantile(0.5)" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": 14, 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "text/plain": [ 347 | "income 0.557477\n", 348 | "log10_income -0.253773\n", 349 | "dtype: float64" 350 | ] 351 | }, 352 | "execution_count": 14, 353 | "metadata": {}, 354 | "output_type": "execute_result" 355 | } 356 | ], 357 | "source": [ 358 | "china1965.median()" 359 | ] 360 | }, 361 | { 362 | "cell_type": "code", 363 | "execution_count": 15, 364 | "metadata": {}, 365 | "outputs": [ 366 | { 367 | "data": { 368 | "text/plain": [ 369 | "95.5" 370 | ] 371 | }, 372 | "execution_count": 15, 373 | "metadata": {}, 374 | "output_type": "execute_result" 375 | } 376 | ], 377 | "source": [ 378 | "scipy.stats.percentileofscore(china1965.income,1.5)" 379 | ] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": 16, 384 | "metadata": {}, 385 | "outputs": [ 386 | { 387 | "data": { 388 | "text/html": [ 389 | "
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incomelog10_income
count1000.0000001000.000000
mean0.660597-0.274157
std0.4572260.297822
min0.041968-1.377078
25%0.344130-0.463277
50%0.557477-0.253773
75%0.863695-0.063640
max5.4268020.734544
\n", 454 | "
" 455 | ], 456 | "text/plain": [ 457 | " income log10_income\n", 458 | "count 1000.000000 1000.000000\n", 459 | "mean 0.660597 -0.274157\n", 460 | "std 0.457226 0.297822\n", 461 | "min 0.041968 -1.377078\n", 462 | "25% 0.344130 -0.463277\n", 463 | "50% 0.557477 -0.253773\n", 464 | "75% 0.863695 -0.063640\n", 465 | "max 5.426802 0.734544" 466 | ] 467 | }, 468 | "execution_count": 16, 469 | "metadata": {}, 470 | "output_type": "execute_result" 471 | } 472 | ], 473 | "source": [ 474 | "china1965.describe()" 475 | ] 476 | }, 477 | { 478 | "cell_type": "code", 479 | "execution_count": 17, 480 | "metadata": {}, 481 | "outputs": [ 482 | { 483 | "data": { 484 | "text/html": [ 485 | "
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incomelog10_income
count1000.0000001000.000000
mean31.5879651.418835
std22.1015310.262200
min4.1778520.620953
25%17.4985921.243003
50%26.0695311.416133
75%39.0171131.591255
max246.0303972.390989
\n", 550 | "
" 551 | ], 552 | "text/plain": [ 553 | " income log10_income\n", 554 | "count 1000.000000 1000.000000\n", 555 | "mean 31.587965 1.418835\n", 556 | "std 22.101531 0.262200\n", 557 | "min 4.177852 0.620953\n", 558 | "25% 17.498592 1.243003\n", 559 | "50% 26.069531 1.416133\n", 560 | "75% 39.017113 1.591255\n", 561 | "max 246.030397 2.390989" 562 | ] 563 | }, 564 | "execution_count": 17, 565 | "metadata": {}, 566 | "output_type": "execute_result" 567 | } 568 | ], 569 | "source": [ 570 | "usa1965.describe()" 571 | ] 572 | }, 573 | { 574 | "cell_type": "code", 575 | "execution_count": null, 576 | "metadata": {}, 577 | "outputs": [], 578 | "source": [] 579 | } 580 | ], 581 | "metadata": { 582 | "kernelspec": { 583 | "display_name": "Python [default]", 584 | "language": "python", 585 | "name": "python3" 586 | }, 587 | "language_info": { 588 | "codemirror_mode": { 589 | "name": "ipython", 590 | "version": 3 591 | }, 592 | "file_extension": ".py", 593 | "mimetype": "text/x-python", 594 | "name": "python", 595 | "nbconvert_exporter": "python", 596 | "pygments_lexer": "ipython3", 597 | "version": "3.6.5" 598 | }, 599 | "toc": { 600 | "base_numbering": 1, 601 | "nav_menu": {}, 602 | "number_sections": true, 603 | "sideBar": true, 604 | "skip_h1_title": false, 605 | "title_cell": "Table of Contents", 606 | "title_sidebar": "Contents", 607 | "toc_cell": false, 608 | "toc_position": {}, 609 | "toc_section_display": true, 610 | "toc_window_display": false 611 | } 612 | }, 613 | "nbformat": 4, 614 | "nbformat_minor": 2 615 | } 616 | -------------------------------------------------------------------------------- /Planets.xls: -------------------------------------------------------------------------------- 1 | Planet,Mass,Diameter,DayLength,SunDistance,OrbitPeriod,OrbitVelocity,MeanTemperature,SurfacePressure,Moons,Rings,MagneticField,FirstVisited,FirstMission 2 | MERCURY,0.33,4879,4222.6,57.9,88,47.4,167,0,0,No,Yes,1974-03-29,Mariner 10 3 | VENUS,4.87,"12,104",2802,108.2,224.7,35,464,92,0,No,No,1962-08-27,Mariner 2 4 | EARTH,5.97,"12,756",24,149.6,365.2,29.8,15,1,1,No,Yes,NA,NA 5 | MOON,0.073,3475,708.7,NA,27.3,1,-20,0,0,No,No,1959-09-12,Luna 2 6 | MARS,0.642,6792,24.7,227.9,687,24.1,-65,0.01,2,No,No,1965-07-15,Mariner 4 7 | JUPITER,1898,"142,984",9.9,778.6,4331,13.1,-110,NA,67,Yes,Yes,1973-12-04,Pioneer 10 8 | SATURN,568,"120,536",10.7,1433.5,"10,747",9.7,-140,NA,62,Yes,Yes,1979-09-01,Pioneer 11 9 | URANUS,86.8,"51,118",17.2,2872.5,"30,589",6.8,-195,NA,27,Yes,Yes,1986-01-24,Voyager 2 10 | NEPTUNE,102,"49,528",16.1,4495.1,"59,800",5.4,-200,NA,14,Yes,Yes,1989-08-25,Voyager 2 11 | PLUTO,0.0146,2370,153.3,5906.4,"90,560",4.7,-225,0.00001,5,No,NA,2015-07-14,New Horizons -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # statistics-using-python 2 | These files are part of Youtube Course "Statistics Using Python" Offered By The Engineering WOrld. Offered By: http://youtube.com/theengineeringworld 3 | -------------------------------------------------------------------------------- /Short Example challenge.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## The Engineering WOrld - A Place For Learning And Exploring\n", 8 | "\n", 9 | "# Example Question" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 37, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import pandas as pd\n", 27 | "\n", 28 | "import matplotlib\n", 29 | "import matplotlib.pyplot as pp\n", 30 | "\n", 31 | "%matplotlib inline" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 38, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "tb = pd.read_csv('hadleydataset2.csv').head()" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 39, 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "data": { 50 | "text/html": [ 51 | "
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countryyearm04m514m014m1524m2534m3544m4554m5564...f04f514f014f1524f2534f3544f4554f5564f65fu
0AD1989NaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1AD1990NaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2AD1991NaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3AD1992NaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4AD1993NaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 22 columns

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" 217 | ], 218 | "text/plain": [ 219 | " country year m04 m514 m014 m1524 m2534 m3544 m4554 m5564 ... f04 \\\n", 220 | "0 AD 1989 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 221 | "1 AD 1990 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 222 | "2 AD 1991 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 223 | "3 AD 1992 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 224 | "4 AD 1993 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 225 | "\n", 226 | " f514 f014 f1524 f2534 f3544 f4554 f5564 f65 fu \n", 227 | "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 228 | "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 229 | "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 230 | "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 231 | "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", 232 | "\n", 233 | "[5 rows x 22 columns]" 234 | ] 235 | }, 236 | "execution_count": 39, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "tb.head()" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 40, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "data": { 252 | "text/plain": [ 253 | "Index(['country', 'year', 'm04', 'm514', 'm014', 'm1524', 'm2534', 'm3544',\n", 254 | " 'm4554', 'm5564', 'm65', 'mu', 'f04', 'f514', 'f014', 'f1524', 'f2534',\n", 255 | " 'f3544', 'f4554', 'f5564', 'f65', 'fu'],\n", 256 | " dtype='object')" 257 | ] 258 | }, 259 | "execution_count": 40, 260 | "metadata": {}, 261 | "output_type": "execute_result" 262 | } 263 | ], 264 | "source": [ 265 | "tb.columns" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 41, 271 | "metadata": {}, 272 | "outputs": [], 273 | "source": [ 274 | "melted = tb.melt(['country', 'year'], ['m04', 'm514', 'm014', 'm1524', 'm2534', 'm3544',\n", 275 | " 'm4554', 'm5564', 'm65', 'mu', 'f04', 'f514', 'f014', 'f1524', 'f2534',\n", 276 | " 'f3544', 'f4554', 'f5564', 'f65', 'fu'], 'sexage', 'cases')" 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": 42, 282 | "metadata": {}, 283 | "outputs": [ 284 | { 285 | "data": { 286 | "text/html": [ 287 | "
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countryyearsexagecases
0AD1989m04NaN
1AD1990m04NaN
2AD1991m04NaN
3AD1992m04NaN
4AD1993m04NaN
\n", 349 | "
" 350 | ], 351 | "text/plain": [ 352 | " country year sexage cases\n", 353 | "0 AD 1989 m04 NaN\n", 354 | "1 AD 1990 m04 NaN\n", 355 | "2 AD 1991 m04 NaN\n", 356 | "3 AD 1992 m04 NaN\n", 357 | "4 AD 1993 m04 NaN" 358 | ] 359 | }, 360 | "execution_count": 42, 361 | "metadata": {}, 362 | "output_type": "execute_result" 363 | } 364 | ], 365 | "source": [ 366 | "melted.head()" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": 43, 372 | "metadata": {}, 373 | "outputs": [], 374 | "source": [ 375 | "melted['sex'] = melted['sexage'].str.slice(0,1)\n", 376 | "melted['age'] = melted['sexage'].str.slice(1)" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": 44, 382 | "metadata": {}, 383 | "outputs": [ 384 | { 385 | "data": { 386 | "text/html": [ 387 | "
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countryyearsexagecasessexage
0AD1989m04NaNm04
1AD1990m04NaNm04
2AD1991m04NaNm04
3AD1992m04NaNm04
4AD1993m04NaNm04
\n", 461 | "
" 462 | ], 463 | "text/plain": [ 464 | " country year sexage cases sex age\n", 465 | "0 AD 1989 m04 NaN m 04\n", 466 | "1 AD 1990 m04 NaN m 04\n", 467 | "2 AD 1991 m04 NaN m 04\n", 468 | "3 AD 1992 m04 NaN m 04\n", 469 | "4 AD 1993 m04 NaN m 04" 470 | ] 471 | }, 472 | "execution_count": 44, 473 | "metadata": {}, 474 | "output_type": "execute_result" 475 | } 476 | ], 477 | "source": [ 478 | "melted.head()" 479 | ] 480 | }, 481 | { 482 | "cell_type": "code", 483 | "execution_count": 45, 484 | "metadata": {}, 485 | "outputs": [], 486 | "source": [ 487 | "melted['age'] = melted['age'].map({'04': '0-4', '514': '5-14', '1524': '15-24', '2534': '25-34', '3544': '35-44',\n", 488 | " '4554': '45-54', '5564': '55-64', '65': '65+', 'u': np.nan})" 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": 46, 494 | "metadata": {}, 495 | "outputs": [ 496 | { 497 | "data": { 498 | "text/plain": [ 499 | "0 0-4\n", 500 | "1 0-4\n", 501 | "2 0-4\n", 502 | "3 0-4\n", 503 | "4 0-4\n", 504 | "5 5-14\n", 505 | "6 5-14\n", 506 | "7 5-14\n", 507 | "8 5-14\n", 508 | "9 5-14\n", 509 | "10 NaN\n", 510 | "11 NaN\n", 511 | "12 NaN\n", 512 | "13 NaN\n", 513 | "14 NaN\n", 514 | "15 15-24\n", 515 | "16 15-24\n", 516 | "17 15-24\n", 517 | "18 15-24\n", 518 | "19 15-24\n", 519 | "20 25-34\n", 520 | "21 25-34\n", 521 | "22 25-34\n", 522 | "23 25-34\n", 523 | "24 25-34\n", 524 | "25 35-44\n", 525 | "26 35-44\n", 526 | "27 35-44\n", 527 | "28 35-44\n", 528 | "29 35-44\n", 529 | " ... \n", 530 | "70 25-34\n", 531 | "71 25-34\n", 532 | "72 25-34\n", 533 | "73 25-34\n", 534 | "74 25-34\n", 535 | "75 35-44\n", 536 | "76 35-44\n", 537 | "77 35-44\n", 538 | "78 35-44\n", 539 | "79 35-44\n", 540 | "80 45-54\n", 541 | "81 45-54\n", 542 | "82 45-54\n", 543 | "83 45-54\n", 544 | "84 45-54\n", 545 | "85 55-64\n", 546 | "86 55-64\n", 547 | "87 55-64\n", 548 | "88 55-64\n", 549 | "89 55-64\n", 550 | "90 65+\n", 551 | "91 65+\n", 552 | "92 65+\n", 553 | "93 65+\n", 554 | "94 65+\n", 555 | "95 NaN\n", 556 | "96 NaN\n", 557 | "97 NaN\n", 558 | "98 NaN\n", 559 | "99 NaN\n", 560 | "Name: age, Length: 100, dtype: object" 561 | ] 562 | }, 563 | "execution_count": 46, 564 | "metadata": {}, 565 | "output_type": "execute_result" 566 | } 567 | ], 568 | "source": [ 569 | "melted['age']" 570 | ] 571 | }, 572 | { 573 | "cell_type": "code", 574 | "execution_count": 47, 575 | "metadata": {}, 576 | "outputs": [], 577 | "source": [ 578 | "final = melted.dropna(subset=['cases'])" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 48, 584 | "metadata": {}, 585 | "outputs": [ 586 | { 587 | "data": { 588 | "text/html": [ 589 | "
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countryyearsexagecasessexage
\n", 618 | "
" 619 | ], 620 | "text/plain": [ 621 | "Empty DataFrame\n", 622 | "Columns: [country, year, sexage, cases, sex, age]\n", 623 | "Index: []" 624 | ] 625 | }, 626 | "execution_count": 48, 627 | "metadata": {}, 628 | "output_type": "execute_result" 629 | } 630 | ], 631 | "source": [ 632 | "final.head()" 633 | ] 634 | }, 635 | { 636 | "cell_type": "code", 637 | "execution_count": null, 638 | "metadata": {}, 639 | "outputs": [], 640 | "source": [] 641 | } 642 | ], 643 | "metadata": { 644 | "kernelspec": { 645 | "display_name": "Python [default]", 646 | "language": "python", 647 | "name": "python3" 648 | }, 649 | "language_info": { 650 | "codemirror_mode": { 651 | "name": "ipython", 652 | "version": 3 653 | }, 654 | "file_extension": ".py", 655 | "mimetype": "text/x-python", 656 | "name": "python", 657 | "nbconvert_exporter": "python", 658 | "pygments_lexer": "ipython3", 659 | "version": "3.6.5" 660 | }, 661 | "toc": { 662 | "base_numbering": 1, 663 | "nav_menu": {}, 664 | "number_sections": true, 665 | "sideBar": true, 666 | "skip_h1_title": false, 667 | "title_cell": "Table of Contents", 668 | "title_sidebar": "Contents", 669 | "toc_cell": false, 670 | "toc_position": {}, 671 | "toc_section_display": true, 672 | "toc_window_display": false 673 | } 674 | }, 675 | "nbformat": 4, 676 | "nbformat_minor": 2 677 | } 678 | -------------------------------------------------------------------------------- /Statistics Categorical Data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# The Engineering WOrld - A Place For Learning And Exploring\n", 8 | "\n", 9 | "## Categorical Data" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import scipy.stats\n", 27 | "import pandas as pd" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "import matplotlib\n", 37 | "import matplotlib.pyplot as pp\n", 38 | "\n", 39 | "import pandas.plotting\n", 40 | "\n", 41 | "from IPython import display\n", 42 | "from ipywidgets import interact, widgets\n", 43 | "\n", 44 | "%matplotlib inline" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "import re\n", 54 | "import mailbox\n", 55 | "import csv" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 4, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "smoking = pd.read_csv('whickham.csv')" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 5, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "name": "stdout", 74 | "output_type": "stream", 75 | "text": [ 76 | "\n", 77 | "RangeIndex: 1314 entries, 0 to 1313\n", 78 | "Data columns (total 3 columns):\n", 79 | "outcome 1314 non-null object\n", 80 | "smoker 1314 non-null object\n", 81 | "age 1314 non-null int64\n", 82 | "dtypes: int64(1), object(2)\n", 83 | "memory usage: 30.9+ KB\n" 84 | ] 85 | } 86 | ], 87 | "source": [ 88 | "smoking.info()" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 6, 94 | "metadata": {}, 95 | "outputs": [ 96 | { 97 | "data": { 98 | "text/html": [ 99 | "
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outcomesmokerage
0AliveYes23
1AliveYes18
2DeadYes71
3AliveNo67
4AliveNo64
\n", 155 | "
" 156 | ], 157 | "text/plain": [ 158 | " outcome smoker age\n", 159 | "0 Alive Yes 23\n", 160 | "1 Alive Yes 18\n", 161 | "2 Dead Yes 71\n", 162 | "3 Alive No 67\n", 163 | "4 Alive No 64" 164 | ] 165 | }, 166 | "execution_count": 6, 167 | "metadata": {}, 168 | "output_type": "execute_result" 169 | } 170 | ], 171 | "source": [ 172 | "smoking.head()" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 7, 178 | "metadata": {}, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "text/html": [ 183 | "
\n", 184 | "\n", 197 | "\n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | "
smoker
No732
Yes582
\n", 215 | "
" 216 | ], 217 | "text/plain": [ 218 | " smoker\n", 219 | "No 732\n", 220 | "Yes 582" 221 | ] 222 | }, 223 | "execution_count": 7, 224 | "metadata": {}, 225 | "output_type": "execute_result" 226 | } 227 | ], 228 | "source": [ 229 | "pd.DataFrame(smoking.smoker.value_counts())" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 8, 235 | "metadata": {}, 236 | "outputs": [ 237 | { 238 | "data": { 239 | "text/html": [ 240 | "
\n", 241 | "\n", 254 | "\n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | "
outcome
Alive945
Dead369
\n", 272 | "
" 273 | ], 274 | "text/plain": [ 275 | " outcome\n", 276 | "Alive 945\n", 277 | "Dead 369" 278 | ] 279 | }, 280 | "execution_count": 8, 281 | "metadata": {}, 282 | "output_type": "execute_result" 283 | } 284 | ], 285 | "source": [ 286 | "pd.DataFrame(smoking.outcome.value_counts())" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 9, 292 | "metadata": {}, 293 | "outputs": [ 294 | { 295 | "data": { 296 | "text/html": [ 297 | "
\n", 298 | "\n", 311 | "\n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | "
outcome
Alive0.719178
Dead0.280822
\n", 329 | "
" 330 | ], 331 | "text/plain": [ 332 | " outcome\n", 333 | "Alive 0.719178\n", 334 | "Dead 0.280822" 335 | ] 336 | }, 337 | "execution_count": 9, 338 | "metadata": {}, 339 | "output_type": "execute_result" 340 | } 341 | ], 342 | "source": [ 343 | "pd.DataFrame(smoking.outcome.value_counts(normalize=True))" 344 | ] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "execution_count": 10, 349 | "metadata": {}, 350 | "outputs": [ 351 | { 352 | "data": { 353 | "text/plain": [ 354 | "smoker outcome\n", 355 | "No Alive 0.685792\n", 356 | " Dead 0.314208\n", 357 | "Yes Alive 0.761168\n", 358 | " Dead 0.238832\n", 359 | "Name: outcome, dtype: float64" 360 | ] 361 | }, 362 | "execution_count": 10, 363 | "metadata": {}, 364 | "output_type": "execute_result" 365 | } 366 | ], 367 | "source": [ 368 | "bysmoker = smoking.groupby(\"smoker\").outcome.value_counts(normalize=True)\n", 369 | "bysmoker" 370 | ] 371 | }, 372 | { 373 | "cell_type": "code", 374 | "execution_count": 11, 375 | "metadata": {}, 376 | "outputs": [ 377 | { 378 | "data": { 379 | "text/plain": [ 380 | "MultiIndex(levels=[['No', 'Yes'], ['Alive', 'Dead']],\n", 381 | " labels=[[0, 0, 1, 1], [0, 1, 0, 1]],\n", 382 | " names=['smoker', 'outcome'])" 383 | ] 384 | }, 385 | "execution_count": 11, 386 | "metadata": {}, 387 | "output_type": "execute_result" 388 | } 389 | ], 390 | "source": [ 391 | "bysmoker.index" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": 12, 397 | "metadata": {}, 398 | "outputs": [ 399 | { 400 | "data": { 401 | "text/html": [ 402 | "
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outcomeAliveDead
smoker
No0.6857920.314208
Yes0.7611680.238832
\n", 442 | "
" 443 | ], 444 | "text/plain": [ 445 | "outcome Alive Dead\n", 446 | "smoker \n", 447 | "No 0.685792 0.314208\n", 448 | "Yes 0.761168 0.238832" 449 | ] 450 | }, 451 | "execution_count": 12, 452 | "metadata": {}, 453 | "output_type": "execute_result" 454 | } 455 | ], 456 | "source": [ 457 | "bysmoker.unstack()" 458 | ] 459 | }, 460 | { 461 | "cell_type": "code", 462 | "execution_count": 13, 463 | "metadata": {}, 464 | "outputs": [], 465 | "source": [ 466 | "smoking['ageGroup'] = pd.cut(smoking.age,[0,30,40,53,64], labels=['0-30','30-40','40-53','53-64'])" 467 | ] 468 | }, 469 | { 470 | "cell_type": "code", 471 | "execution_count": 14, 472 | "metadata": {}, 473 | "outputs": [ 474 | { 475 | "data": { 476 | "text/plain": [ 477 | "0 0-30\n", 478 | "1 0-30\n", 479 | "2 NaN\n", 480 | "3 NaN\n", 481 | "4 53-64\n", 482 | "Name: ageGroup, dtype: category\n", 483 | "Categories (4, object): [0-30 < 30-40 < 40-53 < 53-64]" 484 | ] 485 | }, 486 | "execution_count": 14, 487 | "metadata": {}, 488 | "output_type": "execute_result" 489 | } 490 | ], 491 | "source": [ 492 | "smoking['ageGroup'].head()" 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 15, 498 | "metadata": {}, 499 | "outputs": [ 500 | { 501 | "data": { 502 | "text/plain": [ 503 | "0 23\n", 504 | "1 18\n", 505 | "2 71\n", 506 | "3 67\n", 507 | "4 64\n", 508 | "Name: age, dtype: int64" 509 | ] 510 | }, 511 | "execution_count": 15, 512 | "metadata": {}, 513 | "output_type": "execute_result" 514 | } 515 | ], 516 | "source": [ 517 | "smoking['age'].head()" 518 | ] 519 | }, 520 | { 521 | "cell_type": "code", 522 | "execution_count": 16, 523 | "metadata": {}, 524 | "outputs": [], 525 | "source": [ 526 | "byage = smoking.groupby(['ageGroup','smoker']).outcome.value_counts(normalize=True)" 527 | ] 528 | }, 529 | { 530 | "cell_type": "code", 531 | "execution_count": 18, 532 | "metadata": {}, 533 | "outputs": [ 534 | { 535 | "data": { 536 | "text/html": [ 537 | "
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outcomeAlive
ageGroupsmoker
0-30No0.981818
Yes0.975610
30-40No0.955224
Yes0.940678
40-53No0.876106
Yes0.802395
53-64No0.669291
Yes0.580645
\n", 603 | "
" 604 | ], 605 | "text/plain": [ 606 | "outcome Alive\n", 607 | "ageGroup smoker \n", 608 | "0-30 No 0.981818\n", 609 | " Yes 0.975610\n", 610 | "30-40 No 0.955224\n", 611 | " Yes 0.940678\n", 612 | "40-53 No 0.876106\n", 613 | " Yes 0.802395\n", 614 | "53-64 No 0.669291\n", 615 | " Yes 0.580645" 616 | ] 617 | }, 618 | "execution_count": 18, 619 | "metadata": {}, 620 | "output_type": "execute_result" 621 | } 622 | ], 623 | "source": [ 624 | "byage.unstack().drop(\"Dead\", axis=1)" 625 | ] 626 | }, 627 | { 628 | "cell_type": "code", 629 | "execution_count": null, 630 | "metadata": {}, 631 | "outputs": [], 632 | "source": [] 633 | } 634 | ], 635 | "metadata": { 636 | "kernelspec": { 637 | "display_name": "Python [default]", 638 | "language": "python", 639 | "name": "python3" 640 | }, 641 | "language_info": { 642 | "codemirror_mode": { 643 | "name": "ipython", 644 | "version": 3 645 | }, 646 | "file_extension": ".py", 647 | "mimetype": "text/x-python", 648 | "name": "python", 649 | "nbconvert_exporter": "python", 650 | "pygments_lexer": "ipython3", 651 | "version": "3.6.5" 652 | }, 653 | "toc": { 654 | "base_numbering": 1, 655 | "nav_menu": {}, 656 | "number_sections": true, 657 | "sideBar": true, 658 | "skip_h1_title": false, 659 | "title_cell": "Table of Contents", 660 | "title_sidebar": "Contents", 661 | "toc_cell": false, 662 | "toc_position": {}, 663 | "toc_section_display": true, 664 | "toc_window_display": false 665 | } 666 | }, 667 | "nbformat": 4, 668 | "nbformat_minor": 2 669 | } 670 | -------------------------------------------------------------------------------- /Visualization Of GaPminder Data begin.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## The Engineering WOrld - A Place For Learning And Exploring\n", 8 | "\n", 9 | "# Visulizing Data (Gapminder Dataset)" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import scipy.stats\n", 27 | "import pandas as pd" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "import matplotlib\n", 37 | "import matplotlib.pyplot as pp\n", 38 | "\n", 39 | "from IPython import display\n", 40 | "from ipywidgets import interact, widgets\n", 41 | "\n", 42 | "%matplotlib inline" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 3, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "import re\n", 52 | "import mailbox\n", 53 | "import csv" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 4, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "gapminder = pd.read_csv('gapminder.csv')" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 5, 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "name": "stdout", 72 | "output_type": "stream", 73 | "text": [ 74 | "\n", 75 | "RangeIndex: 14740 entries, 0 to 14739\n", 76 | "Data columns (total 9 columns):\n", 77 | "country 14740 non-null object\n", 78 | "year 14740 non-null int64\n", 79 | "region 14740 non-null object\n", 80 | "population 14740 non-null float64\n", 81 | "life_expectancy 14740 non-null float64\n", 82 | "age5_surviving 14740 non-null float64\n", 83 | "babies_per_woman 14740 non-null float64\n", 84 | "gdp_per_capita 14740 non-null float64\n", 85 | "gdp_per_day 14740 non-null float64\n", 86 | "dtypes: float64(6), int64(1), object(2)\n", 87 | "memory usage: 1.0+ MB\n" 88 | ] 89 | } 90 | ], 91 | "source": [ 92 | "gapminder.info()" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 6, 98 | "metadata": {}, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "text/html": [ 103 | "
\n", 104 | "\n", 117 | "\n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | "
countryyearregionpopulationlife_expectancyage5_survivingbabies_per_womangdp_per_capitagdp_per_day
0Afghanistan1800Asia3280000.028.2153.1427.00603.01.650924
20Afghanistan1955Asia8270024.029.2760.1937.671125.03.080082
40Afghanistan1975Asia12582954.039.6172.0607.671201.03.288159
60Afghanistan1995Asia16772522.049.4084.7707.83872.02.387406
80Afghanistan2015Asia32526562.053.8090.8904.471925.05.270363
100Albania1954Europe1382881.056.5984.8296.312108.05.771389
120Albania1974Europe2358467.069.3590.0824.544177.011.436003
140Albania1994Europe3140634.073.6096.5402.773457.09.464750
160Albania2014Europe2889676.077.9098.5601.7810160.027.816564
180Algeria1953Africa9405445.043.9673.7587.654077.011.162218
200Algeria1973Africa15804428.053.9177.6607.557581.020.755647
\n", 267 | "
" 268 | ], 269 | "text/plain": [ 270 | " country year region population life_expectancy age5_surviving \\\n", 271 | "0 Afghanistan 1800 Asia 3280000.0 28.21 53.142 \n", 272 | "20 Afghanistan 1955 Asia 8270024.0 29.27 60.193 \n", 273 | "40 Afghanistan 1975 Asia 12582954.0 39.61 72.060 \n", 274 | "60 Afghanistan 1995 Asia 16772522.0 49.40 84.770 \n", 275 | "80 Afghanistan 2015 Asia 32526562.0 53.80 90.890 \n", 276 | "100 Albania 1954 Europe 1382881.0 56.59 84.829 \n", 277 | "120 Albania 1974 Europe 2358467.0 69.35 90.082 \n", 278 | "140 Albania 1994 Europe 3140634.0 73.60 96.540 \n", 279 | "160 Albania 2014 Europe 2889676.0 77.90 98.560 \n", 280 | "180 Algeria 1953 Africa 9405445.0 43.96 73.758 \n", 281 | "200 Algeria 1973 Africa 15804428.0 53.91 77.660 \n", 282 | "\n", 283 | " babies_per_woman gdp_per_capita gdp_per_day \n", 284 | "0 7.00 603.0 1.650924 \n", 285 | "20 7.67 1125.0 3.080082 \n", 286 | "40 7.67 1201.0 3.288159 \n", 287 | "60 7.83 872.0 2.387406 \n", 288 | "80 4.47 1925.0 5.270363 \n", 289 | "100 6.31 2108.0 5.771389 \n", 290 | "120 4.54 4177.0 11.436003 \n", 291 | "140 2.77 3457.0 9.464750 \n", 292 | "160 1.78 10160.0 27.816564 \n", 293 | "180 7.65 4077.0 11.162218 \n", 294 | "200 7.55 7581.0 20.755647 " 295 | ] 296 | }, 297 | "execution_count": 6, 298 | "metadata": {}, 299 | "output_type": "execute_result" 300 | } 301 | ], 302 | "source": [ 303 | "gapminder.loc[0:200:20]" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": 7, 309 | "metadata": {}, 310 | "outputs": [ 311 | { 312 | "data": { 313 | "text/plain": [ 314 | "" 315 | ] 316 | }, 317 | "execution_count": 7, 318 | "metadata": {}, 319 | "output_type": "execute_result" 320 | }, 321 | { 322 | "data": { 323 | "image/png": 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\n", 324 | "text/plain": [ 325 | "
" 326 | ] 327 | }, 328 | "metadata": {}, 329 | "output_type": "display_data" 330 | } 331 | ], 332 | "source": [ 333 | "gapminder[gapminder.year == 1965].plot.scatter('babies_per_woman', 'age5_surviving')" 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": 8, 339 | "metadata": {}, 340 | "outputs": [], 341 | "source": [ 342 | "def plotyear(year):\n", 343 | " data = gapminder[gapminder.year == year]\n", 344 | " area = 5e-6 * data.population\n", 345 | " colors = data.region.map({'Africa': 'skyblue', 'Europe': 'gold', 'America':'palegreen', 'Asia':'coral'})\n", 346 | " \n", 347 | " data.plot.scatter('babies_per_woman', 'age5_surviving',\n", 348 | " s = area, c=colors,\n", 349 | " linewidth = 1, edgecolors='k',\n", 350 | " figsize=(12,9))\n", 351 | " \n", 352 | " pp.axis(ymin=50, ymax=105, xmin=0, xmax=8)\n", 353 | " pp.xlabel('babies per woman')\n", 354 | " pp.ylabel('% children alive at 5')" 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": 9, 360 | "metadata": {}, 361 | "outputs": [ 362 | { 363 | "data": { 364 | "application/vnd.jupyter.widget-view+json": { 365 | "model_id": "67c63b21a38b40e6a722645dfcfd63e7", 366 | "version_major": 2, 367 | "version_minor": 0 368 | }, 369 | "text/plain": [ 370 | "interactive(children=(IntSlider(value=1965, description='year', max=2015, min=1950), Output()), _dom_classes=(…" 371 | ] 372 | }, 373 | "metadata": {}, 374 | "output_type": "display_data" 375 | }, 376 | { 377 | "data": { 378 | "text/plain": [ 379 | "" 380 | ] 381 | }, 382 | "execution_count": 9, 383 | "metadata": {}, 384 | "output_type": "execute_result" 385 | } 386 | ], 387 | "source": [ 388 | "interact(plotyear, year=widgets.IntSlider(min=1950, max=2015, step=1, value=1965))" 389 | ] 390 | }, 391 | { 392 | "cell_type": "code", 393 | "execution_count": null, 394 | "metadata": {}, 395 | "outputs": [], 396 | "source": [] 397 | } 398 | ], 399 | "metadata": { 400 | "kernelspec": { 401 | "display_name": "Python [default]", 402 | "language": "python", 403 | "name": "python3" 404 | }, 405 | "language_info": { 406 | "codemirror_mode": { 407 | "name": "ipython", 408 | "version": 3 409 | }, 410 | "file_extension": ".py", 411 | "mimetype": "text/x-python", 412 | "name": "python", 413 | "nbconvert_exporter": "python", 414 | "pygments_lexer": "ipython3", 415 | "version": "3.6.5" 416 | }, 417 | "toc": { 418 | "base_numbering": 1, 419 | "nav_menu": {}, 420 | "number_sections": true, 421 | "sideBar": true, 422 | "skip_h1_title": false, 423 | "title_cell": "Table of Contents", 424 | "title_sidebar": "Contents", 425 | "toc_cell": false, 426 | "toc_position": {}, 427 | "toc_section_display": true, 428 | "toc_window_display": false 429 | } 430 | }, 431 | "nbformat": 4, 432 | "nbformat_minor": 2 433 | } 434 | -------------------------------------------------------------------------------- /billboard.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theengineeringworld/statistics-using-python/458e00a0d31daf69d1b97b7dcaa1f88d05e3f191/billboard.csv -------------------------------------------------------------------------------- /billboard.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theengineeringworld/statistics-using-python/458e00a0d31daf69d1b97b7dcaa1f88d05e3f191/billboard.xls -------------------------------------------------------------------------------- /cholera.xls: -------------------------------------------------------------------------------- 1 | deaths,lat,lon,x,y,closest 2 | 3,51.513418,-0.13793,-0.08730123377777677,0.008559124508608074,0 3 | 2,51.513361,-0.137883,-0.08404991999999961,0.0022231492235895156,0 4 | 1,51.513317,-0.137853,-0.08197461333333278,-0.002667779067043685,0 5 | 1,51.513262,-0.137812,-0.07913836088888743,-0.008781439430335158,0 6 | 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9.120704531417568 99 | 7.842357435219203 100 | 3.920675894041414 101 | 5.485458607961167 102 | -------------------------------------------------------------------------------- /london.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/theengineeringworld/statistics-using-python/458e00a0d31daf69d1b97b7dcaa1f88d05e3f191/london.png -------------------------------------------------------------------------------- /p values and confidence in Python Statistics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# The Engineering World - A Place for Learning And Exploring\n", 8 | "\n", 9 | "## p values & confidence Intervals" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import math\n", 26 | "import io" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 2, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "import numpy as np\n", 36 | "import pandas as pd\n", 37 | "\n", 38 | "import matplotlib\n", 39 | "import matplotlib.pyplot as pp\n", 40 | "\n", 41 | "%matplotlib inline" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 3, 47 | "metadata": {}, 48 | "outputs": [], 49 | "source": [ 50 | "import scipy.stats\n", 51 | "import scipy.optimize\n", 52 | "import scipy.spatial" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 4, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "poll = pd.read_csv('poll.csv')" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 5, 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/plain": [ 72 | "Brown 0.511\n", 73 | "Green 0.489\n", 74 | "Name: vote, dtype: float64" 75 | ] 76 | }, 77 | "execution_count": 5, 78 | "metadata": {}, 79 | "output_type": "execute_result" 80 | } 81 | ], 82 | "source": [ 83 | "poll.vote.value_counts(normalize=True)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 12, 89 | "metadata": {}, 90 | "outputs": [], 91 | "source": [ 92 | "def sample(brown, n=1000):\n", 93 | " return pd.DataFrame({'vote':np.where(np.random.rand(n) < brown, 'Brown', 'Green')})" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 13, 99 | "metadata": {}, 100 | "outputs": [], 101 | "source": [ 102 | "dist = pd.DataFrame({'Brown': [sample(0.50,1000).vote.value_counts(normalize=True)['Brown'] for i in range(10000)]})" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 14, 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "data": { 112 | "text/plain": [ 113 | "" 114 | ] 115 | }, 116 | "execution_count": 14, 117 | "metadata": {}, 118 | "output_type": "execute_result" 119 | }, 120 | { 121 | "data": { 122 | "image/png": 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\n", 123 | "text/plain": [ 124 | "
" 125 | ] 126 | }, 127 | "metadata": {}, 128 | "output_type": "display_data" 129 | } 130 | ], 131 | "source": [ 132 | "dist.Brown.hist(histtype='step', bins=20)" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 16, 138 | "metadata": {}, 139 | "outputs": [ 140 | { 141 | "data": { 142 | "text/plain": [ 143 | "25.045" 144 | ] 145 | }, 146 | "execution_count": 16, 147 | "metadata": {}, 148 | "output_type": "execute_result" 149 | } 150 | ], 151 | "source": [ 152 | "100 - scipy.stats.percentileofscore(dist.Brown,0.511)" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 17, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "largepoll = pd.read_csv('poll-larger.csv')" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 18, 167 | "metadata": {}, 168 | "outputs": [ 169 | { 170 | "data": { 171 | "text/plain": [ 172 | "Green 0.5181\n", 173 | "Brown 0.4819\n", 174 | "Name: vote, dtype: float64" 175 | ] 176 | }, 177 | "execution_count": 18, 178 | "metadata": {}, 179 | "output_type": "execute_result" 180 | } 181 | ], 182 | "source": [ 183 | "largepoll.vote.value_counts(normalize=True)" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 19, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "dist = pd.DataFrame({'Green': [sample(0.50,10000).vote.value_counts(normalize=True)['Green'] for i in range(1000)]})" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 21, 198 | "metadata": {}, 199 | "outputs": [ 200 | { 201 | "data": { 202 | "text/plain": [ 203 | "" 204 | ] 205 | }, 206 | "execution_count": 21, 207 | "metadata": {}, 208 | "output_type": "execute_result" 209 | }, 210 | { 211 | "data": { 212 | "image/png": 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mD52+iYhNwCaAsbGxyampqVrHnpubY2RkpFbfQThS776ZV1kzfuqgy+louY7vcjGM9S70tzmM9S5kKetd++hWAPaed0vtfXRb7/r16/dk5rqODTOz0QSMAHuAn6mWZ4/a/kqnfUxOTmZd09PTtfsOwpF6z9q8c7CFLNJyHd/lYhjrXehvcxjrXciS1rt9Q2tqoNt6gd25iJxudNdNRLwe+CLwmcz879XqP42IVdX2VcCLTY4hSWqmyV03AdwGPJWZ/7Ft013Axmp+I3Bn/fIkSU01eR79RcDPA/siYm+17leAbcAdEXE98Bzw3mYlSpKaqB30mfkHQBxj86V19ytJ6i2/GStJhTPoJalwBr0kFc6gl6TCGfSSVLgmt1dKKtD46Aomttw977Yb1xzm2mNsa+//4JZL+lGaajLoJb3GQiG9a9cuDlxz8YL9j/UiocHx1I0kFe64f0d/0bb7mZk9VKuvH1ElLQfHfdDPzB7iwLbLa/X1I6qk5cBTN5JUuOP+HX0TC92dcCxH7loYH13Rp6ok6bUM+gbqnJ9fzF0LktRLnrqRpMIZ9JJUOINekgpn0EtS4bwYK6mn6tyN1t7XLyH2nkEvqaeaBLVfQuyPZR/0T3/zWx2fprcQ72eXVLplH/Tf+8FfcGDbTw+6DEkaWn27GBsRl0XE0xGxPyK29Os4kqSF9SXoI+IE4LeAdwHnAFdHxDn9OJYkaWH9OnVzAbA/M58FiIgp4Arga306nqQCNLljZz6L+T9iHTluk4vIF227n//wnT8H4Kou61+KO436FfTjwPNtyweBn+jTsSQVoteBt9hnSzV9cZmZPcSFb3szAAeu6+6x50txp1G/gj7mWZevaRCxCdhULc5FxNM1j7XyF3+Ol2r2HYSVYL19ZL39VWy98dFmB/rL0PvAfPG36GN3O75nLaZRv4L+IHBm2/IZwJ+0N8jMTwKfbHqgiNidmeua7mepWG9/WW9/WW9/9aveft1184fA6og4OyJOAq4C7urTsSRJC+jLO/rMPBwR/xT4MnACsD0zn+zHsSRJC+vbF6Yy8x7gnn7tv03j0z9LzHr7y3r7y3r7qy/1RmZ2biVJWrZ8TLEkFW7ogn6xj06IiCsjIiNiXbX8+ojYERH7IuKpiLipre2Bav3eiNg9JPWeFBGfqup6LCIubms7Wa3fHxEfj4ju79da2np3VfvcW01vWap6I+LaiPiztmP/47ZtGyPimWra2LZ+YONbs95hHd8vRcRsROw8qs/ZEfFI9Xt8rrohY5jrvT0ivtHWZ+2g642ItRHxUEQ8GRGPR8TPtvXpfnwzc2gmWhdu/xh4K3AS8Bhwzjzt3gg8ADwMrKvWvQ+YquZPBg4AE9XyAWDlkNV7A/Cpav4twB7gddXyV4F30Lo19/eBdw15vbuOtFvq8QWuBX5znr6nA89WP0+r5k8b9PjWrHfoxrfadinw08DOo9bfAVxVzf9n4J8Meb23A1cO0/gCfxNYXc3/DeAFYLTu+A7bO/q/fHRCZn4POPLohKP9GvDvgP/Xti6BUyLiRGAF8D3g/w5xvecA9wFk5ovALLAuIlYBb8rMh7L1L/lp4D3DWm+P6jqWxdY7n3cC92bmy5n5CnAvcNmQjO+i6+1RXcfSpF4y8z7gW+3rqk9HlwBfqFbtYDjGd956+6x2vZn59cx8ppr/E+BF4Efqju+wBf18j04Yb28QEecBZ2bmaz5+0frFv03rle854Ncz8+VqWwJfiYg90fpG7jDU+xhwRUScGBFnA5O0vmQ2Xu3nmPscsnqP+FT10fNf9/BUSMd6K/+w+nj7hYg4UtOx+g50fGvUe8Swje+xvBmYzczDHfZZRz/qPeKWqs+tEfGGxpW29KTeiLiA1ieCP6bm+A5b0C/46ISIeB1wK3DjPO0uAH5A62PO2cCNEfHWattFmXk+radp3hARPzkE9W6n9Y+0G/gN4H8Dhzvts6F+1AtwTWauAf5uNf38UtRb+R+0TtG9HfiftN7hLNR3YONb6bZeGM7xbbLPuvpRL8BNwI8Bf4fWqbPNTYps07je6hPofwWuy8y/WOQ+f8iwBX2nRye8ETgX2BURB4ALgbuidcHwfcCXMvP71amFB6lOLVQffY6ccvg9Wi8KA603Mw9n5i9l5trMvAIYBZ6p9nnGAvsctnrJzJnq57eA/8bSjS+Z+eeZ+d1q8XdofdJYqO8gx7dOvcM6vsfyEjBanUKdd58N9KNeMvOFbPku8CmGZHwj4k3A3cC/ysyHq9X1xrfXFyCaTLS+wPUsrXfkRy5e/PgC7XfxVxcLN9P6RwrgFFqPRH57Nf/Gqs0ptN6JXjYE9Z4MnFLN/xTwQFu7P6QVskcuFm4Y1nqrfa6s5l9P6xTaLyxVvcCqtvl/ADxczZ8OfIPWhc3TqvnTBz2+3dY7rOPbtu5ifvji5ud57cXCDw55vauqn0Hr0+q2Qddbtb8P+Ofz7Lfr8W38y/R6AjYAX6d1Pmprte7fAO+ep+0u/iqIRqoBeJJWyP/Lav1bqwF+rNq2dUjqnQCeBp6i9ZHtrLZ264Anqn3+JtUX24axXlovnnuAx6vx/RhwwlLVC3ykOu5jwDTwY219PwDsr6brhmF8u613yMf3fwF/Bhyi9e71nW3/zX21+j0+D7xhyOu9H9hX/U38LjAy6HqBnwO+D+xtm9bWHV+/GStJhRu2c/SSpB4z6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKtz/B+xOJ4g9LJcIAAAAAElFTkSuQmCC\n", 213 | "text/plain": [ 214 | "
" 215 | ] 216 | }, 217 | "metadata": {}, 218 | "output_type": "display_data" 219 | } 220 | ], 221 | "source": [ 222 | "dist.Green.hist(histtype='step',bins=20)\n", 223 | "\n", 224 | "pp.axvline(0.5181,c='C1')" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": {}, 231 | "outputs": [], 232 | "source": [] 233 | } 234 | ], 235 | "metadata": { 236 | "kernelspec": { 237 | "display_name": "Python 3", 238 | "language": "python", 239 | "name": "python3" 240 | }, 241 | "language_info": { 242 | "codemirror_mode": { 243 | "name": "ipython", 244 | "version": 3 245 | }, 246 | "file_extension": ".py", 247 | "mimetype": "text/x-python", 248 | "name": "python", 249 | "nbconvert_exporter": "python", 250 | "pygments_lexer": "ipython3", 251 | "version": "3.6.5" 252 | }, 253 | "toc": { 254 | "base_numbering": 1, 255 | "nav_menu": {}, 256 | "number_sections": true, 257 | "sideBar": true, 258 | "skip_h1_title": false, 259 | "title_cell": "Table of Contents", 260 | "title_sidebar": "Contents", 261 | "toc_cell": false, 262 | "toc_position": {}, 263 | "toc_section_display": true, 264 | "toc_window_display": false 265 | } 266 | }, 267 | "nbformat": 4, 268 | "nbformat_minor": 2 269 | } 270 | -------------------------------------------------------------------------------- /pandas Beginners Guide The Engineering World.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## The Engineering WOrld - A Place For Learning And Exploring\n", 8 | "\n", 9 | "## Python statistics essential training" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "Standard imports" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np\n", 26 | "import pandas as pd\n", 27 | "\n", 28 | "import matplotlib\n", 29 | "import matplotlib.pyplot as pp\n", 30 | "\n", 31 | "%matplotlib inline" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 2, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "planets = pd.read_csv('Planets.csv')" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 3, 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "data": { 50 | "text/html": [ 51 | "
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PlanetMassDiameterDayLengthSunDistanceOrbitPeriodOrbitVelocityMeanTemperatureSurfacePressureMoonsRingsMagneticFieldFirstVisitedFirstMission
0MERCURY0.330048794222.657.98847.41670.000000NoYes1974-03-29Mariner 10
1VENUS4.870012,1042802.0108.2224.735.046492.000000NoNo1962-08-27Mariner 2
2EARTH5.970012,75624.0149.6365.229.8151.000001NoYesNaNNaN
3MOON0.07303475708.7NaN27.31.0-200.000000NoNo1959-09-12Luna 2
4MARS0.6420679224.7227.968724.1-650.010002NoNo1965-07-15Mariner 4
5JUPITER1898.0000142,9849.9778.6433113.1-110NaN67YesYes1973-12-04Pioneer 10
6SATURN568.0000120,53610.71433.510,7479.7-140NaN62YesYes1979-09-01Pioneer 11
7URANUS86.800051,11817.22872.530,5896.8-195NaN27YesYes1986-01-24Voyager 2
8NEPTUNE102.000049,52816.14495.159,8005.4-200NaN14YesYes1989-08-25Voyager 2
9PLUTO0.01462370153.35906.490,5604.7-2250.000015NoNaN2015-07-14New Horizons
\n", 258 | "
" 259 | ], 260 | "text/plain": [ 261 | " Planet Mass Diameter DayLength SunDistance OrbitPeriod \\\n", 262 | "0 MERCURY 0.3300 4879 4222.6 57.9 88 \n", 263 | "1 VENUS 4.8700 12,104 2802.0 108.2 224.7 \n", 264 | "2 EARTH 5.9700 12,756 24.0 149.6 365.2 \n", 265 | "3 MOON 0.0730 3475 708.7 NaN 27.3 \n", 266 | "4 MARS 0.6420 6792 24.7 227.9 687 \n", 267 | "5 JUPITER 1898.0000 142,984 9.9 778.6 4331 \n", 268 | "6 SATURN 568.0000 120,536 10.7 1433.5 10,747 \n", 269 | "7 URANUS 86.8000 51,118 17.2 2872.5 30,589 \n", 270 | "8 NEPTUNE 102.0000 49,528 16.1 4495.1 59,800 \n", 271 | "9 PLUTO 0.0146 2370 153.3 5906.4 90,560 \n", 272 | "\n", 273 | " OrbitVelocity MeanTemperature SurfacePressure Moons Rings MagneticField \\\n", 274 | "0 47.4 167 0.00000 0 No Yes \n", 275 | "1 35.0 464 92.00000 0 No No \n", 276 | "2 29.8 15 1.00000 1 No Yes \n", 277 | "3 1.0 -20 0.00000 0 No No \n", 278 | "4 24.1 -65 0.01000 2 No No \n", 279 | "5 13.1 -110 NaN 67 Yes Yes \n", 280 | "6 9.7 -140 NaN 62 Yes Yes \n", 281 | "7 6.8 -195 NaN 27 Yes Yes \n", 282 | "8 5.4 -200 NaN 14 Yes Yes \n", 283 | "9 4.7 -225 0.00001 5 No NaN \n", 284 | "\n", 285 | " FirstVisited FirstMission \n", 286 | "0 1974-03-29 Mariner 10 \n", 287 | "1 1962-08-27 Mariner 2 \n", 288 | "2 NaN NaN \n", 289 | "3 1959-09-12 Luna 2 \n", 290 | "4 1965-07-15 Mariner 4 \n", 291 | "5 1973-12-04 Pioneer 10 \n", 292 | "6 1979-09-01 Pioneer 11 \n", 293 | "7 1986-01-24 Voyager 2 \n", 294 | "8 1989-08-25 Voyager 2 \n", 295 | "9 2015-07-14 New Horizons " 296 | ] 297 | }, 298 | "execution_count": 3, 299 | "metadata": {}, 300 | "output_type": "execute_result" 301 | } 302 | ], 303 | "source": [ 304 | "planets" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": 4, 310 | "metadata": {}, 311 | "outputs": [], 312 | "source": [ 313 | "planets = pd.read_csv('Planets.csv', usecols=[0,1,2,3])" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 5, 319 | "metadata": {}, 320 | "outputs": [ 321 | { 322 | "data": { 323 | "text/html": [ 324 | "
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PlanetMassDiameterDayLength
0MERCURY0.330048794222.6
1VENUS4.870012,1042802.0
2EARTH5.970012,75624.0
3MOON0.07303475708.7
4MARS0.6420679224.7
5JUPITER1898.0000142,9849.9
6SATURN568.0000120,53610.7
7URANUS86.800051,11817.2
8NEPTUNE102.000049,52816.1
9PLUTO0.01462370153.3
\n", 421 | "
" 422 | ], 423 | "text/plain": [ 424 | " Planet Mass Diameter DayLength\n", 425 | "0 MERCURY 0.3300 4879 4222.6\n", 426 | "1 VENUS 4.8700 12,104 2802.0\n", 427 | "2 EARTH 5.9700 12,756 24.0\n", 428 | "3 MOON 0.0730 3475 708.7\n", 429 | "4 MARS 0.6420 6792 24.7\n", 430 | "5 JUPITER 1898.0000 142,984 9.9\n", 431 | "6 SATURN 568.0000 120,536 10.7\n", 432 | "7 URANUS 86.8000 51,118 17.2\n", 433 | "8 NEPTUNE 102.0000 49,528 16.1\n", 434 | "9 PLUTO 0.0146 2370 153.3" 435 | ] 436 | }, 437 | "execution_count": 5, 438 | "metadata": {}, 439 | "output_type": "execute_result" 440 | } 441 | ], 442 | "source": [ 443 | "planets" 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": 6, 449 | "metadata": {}, 450 | "outputs": [ 451 | { 452 | "data": { 453 | "text/plain": [ 454 | "0 0.3300\n", 455 | "1 4.8700\n", 456 | "2 5.9700\n", 457 | "3 0.0730\n", 458 | "4 0.6420\n", 459 | "5 1898.0000\n", 460 | "6 568.0000\n", 461 | "7 86.8000\n", 462 | "8 102.0000\n", 463 | "9 0.0146\n", 464 | "Name: Mass, dtype: float64" 465 | ] 466 | }, 467 | "execution_count": 6, 468 | "metadata": {}, 469 | "output_type": "execute_result" 470 | } 471 | ], 472 | "source": [ 473 | "planets['Mass']" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": 7, 479 | "metadata": {}, 480 | "outputs": [ 481 | { 482 | "data": { 483 | "text/plain": [ 484 | "0 0.3300\n", 485 | "1 4.8700\n", 486 | "2 5.9700\n", 487 | "3 0.0730\n", 488 | "4 0.6420\n", 489 | "5 1898.0000\n", 490 | "6 568.0000\n", 491 | "7 86.8000\n", 492 | "8 102.0000\n", 493 | "9 0.0146\n", 494 | "Name: Mass, dtype: float64" 495 | ] 496 | }, 497 | "execution_count": 7, 498 | "metadata": {}, 499 | "output_type": "execute_result" 500 | } 501 | ], 502 | "source": [ 503 | "planets.Mass" 504 | ] 505 | }, 506 | { 507 | "cell_type": "code", 508 | "execution_count": 8, 509 | "metadata": {}, 510 | "outputs": [ 511 | { 512 | "data": { 513 | "text/plain": [ 514 | "RangeIndex(start=0, stop=10, step=1)" 515 | ] 516 | }, 517 | "execution_count": 8, 518 | "metadata": {}, 519 | "output_type": "execute_result" 520 | } 521 | ], 522 | "source": [ 523 | "planets.index" 524 | ] 525 | }, 526 | { 527 | "cell_type": "code", 528 | "execution_count": 9, 529 | "metadata": {}, 530 | "outputs": [ 531 | { 532 | "data": { 533 | "text/plain": [ 534 | "Planet MERCURY\n", 535 | "Mass 0.33\n", 536 | "Diameter 4879\n", 537 | "DayLength 4222.6\n", 538 | "Name: 0, dtype: object" 539 | ] 540 | }, 541 | "execution_count": 9, 542 | "metadata": {}, 543 | "output_type": "execute_result" 544 | } 545 | ], 546 | "source": [ 547 | "planets.loc[0]" 548 | ] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "execution_count": 11, 553 | "metadata": {}, 554 | "outputs": [], 555 | "source": [ 556 | "planets.set_index('Planet', inplace=True)" 557 | ] 558 | }, 559 | { 560 | "cell_type": "code", 561 | "execution_count": 12, 562 | "metadata": {}, 563 | "outputs": [ 564 | { 565 | "data": { 566 | "text/html": [ 567 | "
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MassDiameterDayLength
Planet
MERCURY0.330048794222.6
VENUS4.870012,1042802.0
EARTH5.970012,75624.0
MOON0.07303475708.7
MARS0.6420679224.7
JUPITER1898.0000142,9849.9
SATURN568.0000120,53610.7
URANUS86.800051,11817.2
NEPTUNE102.000049,52816.1
PLUTO0.01462370153.3
\n", 659 | "
" 660 | ], 661 | "text/plain": [ 662 | " Mass Diameter DayLength\n", 663 | "Planet \n", 664 | "MERCURY 0.3300 4879 4222.6\n", 665 | "VENUS 4.8700 12,104 2802.0\n", 666 | "EARTH 5.9700 12,756 24.0\n", 667 | "MOON 0.0730 3475 708.7\n", 668 | "MARS 0.6420 6792 24.7\n", 669 | "JUPITER 1898.0000 142,984 9.9\n", 670 | "SATURN 568.0000 120,536 10.7\n", 671 | "URANUS 86.8000 51,118 17.2\n", 672 | "NEPTUNE 102.0000 49,528 16.1\n", 673 | "PLUTO 0.0146 2370 153.3" 674 | ] 675 | }, 676 | "execution_count": 12, 677 | "metadata": {}, 678 | "output_type": "execute_result" 679 | } 680 | ], 681 | "source": [ 682 | "planets" 683 | ] 684 | }, 685 | { 686 | "cell_type": "code", 687 | "execution_count": 13, 688 | "metadata": {}, 689 | "outputs": [ 690 | { 691 | "name": "stdout", 692 | "output_type": "stream", 693 | "text": [ 694 | "\n", 695 | "Index: 10 entries, MERCURY to PLUTO\n", 696 | "Data columns (total 3 columns):\n", 697 | "Mass 10 non-null float64\n", 698 | "Diameter 10 non-null object\n", 699 | "DayLength 10 non-null float64\n", 700 | "dtypes: float64(2), object(1)\n", 701 | "memory usage: 320.0+ bytes\n" 702 | ] 703 | } 704 | ], 705 | "source": [ 706 | "planets.info()" 707 | ] 708 | }, 709 | { 710 | "cell_type": "code", 711 | "execution_count": 14, 712 | "metadata": {}, 713 | "outputs": [ 714 | { 715 | "data": { 716 | "text/plain": [ 717 | "10" 718 | ] 719 | }, 720 | "execution_count": 14, 721 | "metadata": {}, 722 | "output_type": "execute_result" 723 | } 724 | ], 725 | "source": [ 726 | "len(planets)" 727 | ] 728 | }, 729 | { 730 | "cell_type": "code", 731 | "execution_count": 16, 732 | "metadata": {}, 733 | "outputs": [ 734 | { 735 | "data": { 736 | "text/plain": [ 737 | "Mass 0.33\n", 738 | "Diameter 4879\n", 739 | "DayLength 4222.6\n", 740 | "Name: MERCURY, dtype: object" 741 | ] 742 | }, 743 | "execution_count": 16, 744 | "metadata": {}, 745 | "output_type": "execute_result" 746 | } 747 | ], 748 | "source": [ 749 | "planets.loc['MERCURY']" 750 | ] 751 | }, 752 | { 753 | "cell_type": "code", 754 | "execution_count": 17, 755 | "metadata": {}, 756 | "outputs": [ 757 | { 758 | "data": { 759 | "text/html": [ 760 | "
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MassDiameterDayLength
Planet
MERCURY0.3348794222.6
VENUS4.8712,1042802.0
EARTH5.9712,75624.0
\n", 810 | "
" 811 | ], 812 | "text/plain": [ 813 | " Mass Diameter DayLength\n", 814 | "Planet \n", 815 | "MERCURY 0.33 4879 4222.6\n", 816 | "VENUS 4.87 12,104 2802.0\n", 817 | "EARTH 5.97 12,756 24.0" 818 | ] 819 | }, 820 | "execution_count": 17, 821 | "metadata": {}, 822 | "output_type": "execute_result" 823 | } 824 | ], 825 | "source": [ 826 | "planets.loc['MERCURY':'EARTH']" 827 | ] 828 | }, 829 | { 830 | "cell_type": "code", 831 | "execution_count": 18, 832 | "metadata": {}, 833 | "outputs": [ 834 | { 835 | "data": { 836 | "text/plain": [ 837 | "Index(['Mass', 'Diameter', 'DayLength'], dtype='object')" 838 | ] 839 | }, 840 | "execution_count": 18, 841 | "metadata": {}, 842 | "output_type": "execute_result" 843 | } 844 | ], 845 | "source": [ 846 | "planets.columns" 847 | ] 848 | }, 849 | { 850 | "cell_type": "code", 851 | "execution_count": 19, 852 | "metadata": {}, 853 | "outputs": [], 854 | "source": [ 855 | "planets = pd.read_csv('Planets.csv')" 856 | ] 857 | }, 858 | { 859 | "cell_type": "code", 860 | "execution_count": 20, 861 | "metadata": {}, 862 | "outputs": [], 863 | "source": [ 864 | "planets.set_index('Planet', inplace=True)" 865 | ] 866 | }, 867 | { 868 | "cell_type": "code", 869 | "execution_count": 21, 870 | "metadata": {}, 871 | "outputs": [ 872 | { 873 | "data": { 874 | "text/plain": [ 875 | "'1974-03-29'" 876 | ] 877 | }, 878 | "execution_count": 21, 879 | "metadata": {}, 880 | "output_type": "execute_result" 881 | } 882 | ], 883 | "source": [ 884 | "planets.FirstVisited['MERCURY']" 885 | ] 886 | }, 887 | { 888 | "cell_type": "code", 889 | "execution_count": 22, 890 | "metadata": {}, 891 | "outputs": [ 892 | { 893 | "data": { 894 | "text/plain": [ 895 | "'1974-03-29'" 896 | ] 897 | }, 898 | "execution_count": 22, 899 | "metadata": {}, 900 | "output_type": "execute_result" 901 | } 902 | ], 903 | "source": [ 904 | "planets.loc['MERCURY'].FirstVisited" 905 | ] 906 | }, 907 | { 908 | "cell_type": "code", 909 | "execution_count": 23, 910 | "metadata": {}, 911 | "outputs": [ 912 | { 913 | "data": { 914 | "text/plain": [ 915 | "'1974-03-29'" 916 | ] 917 | }, 918 | "execution_count": 23, 919 | "metadata": {}, 920 | "output_type": "execute_result" 921 | } 922 | ], 923 | "source": [ 924 | "planets.loc['MERCURY', 'FirstVisited']" 925 | ] 926 | }, 927 | { 928 | "cell_type": "code", 929 | "execution_count": 24, 930 | "metadata": {}, 931 | "outputs": [ 932 | { 933 | "data": { 934 | "text/plain": [ 935 | "str" 936 | ] 937 | }, 938 | "execution_count": 24, 939 | "metadata": {}, 940 | "output_type": "execute_result" 941 | } 942 | ], 943 | "source": [ 944 | "type(planets.loc['MERCURY', 'FirstVisited'])" 945 | ] 946 | }, 947 | { 948 | "cell_type": "code", 949 | "execution_count": 25, 950 | "metadata": {}, 951 | "outputs": [ 952 | { 953 | "data": { 954 | "text/plain": [ 955 | "Planet\n", 956 | "MERCURY 1974-03-29\n", 957 | "VENUS 1962-08-27\n", 958 | "EARTH NaT\n", 959 | "MOON 1959-09-12\n", 960 | "MARS 1965-07-15\n", 961 | "JUPITER 1973-12-04\n", 962 | "SATURN 1979-09-01\n", 963 | "URANUS 1986-01-24\n", 964 | "NEPTUNE 1989-08-25\n", 965 | "PLUTO 2015-07-14\n", 966 | "Name: FirstVisited, dtype: datetime64[ns]" 967 | ] 968 | }, 969 | "execution_count": 25, 970 | "metadata": {}, 971 | "output_type": "execute_result" 972 | } 973 | ], 974 | "source": [ 975 | "pd.to_datetime(planets.FirstVisited)" 976 | ] 977 | }, 978 | { 979 | "cell_type": "code", 980 | "execution_count": 26, 981 | "metadata": {}, 982 | "outputs": [], 983 | "source": [ 984 | "planets.FirstVisited = pd.to_datetime(planets.FirstVisited)" 985 | ] 986 | }, 987 | { 988 | "cell_type": "code", 989 | "execution_count": 27, 990 | "metadata": {}, 991 | "outputs": [ 992 | { 993 | "data": { 994 | "text/plain": [ 995 | "Planet\n", 996 | "MERCURY 1974.0\n", 997 | "VENUS 1962.0\n", 998 | "EARTH NaN\n", 999 | "MOON 1959.0\n", 1000 | "MARS 1965.0\n", 1001 | "JUPITER 1973.0\n", 1002 | "SATURN 1979.0\n", 1003 | "URANUS 1986.0\n", 1004 | "NEPTUNE 1989.0\n", 1005 | "PLUTO 2015.0\n", 1006 | "Name: FirstVisited, dtype: float64" 1007 | ] 1008 | }, 1009 | "execution_count": 27, 1010 | "metadata": {}, 1011 | "output_type": "execute_result" 1012 | } 1013 | ], 1014 | "source": [ 1015 | "planets.FirstVisited.dt.year" 1016 | ] 1017 | }, 1018 | { 1019 | "cell_type": "code", 1020 | "execution_count": 28, 1021 | "metadata": {}, 1022 | "outputs": [ 1023 | { 1024 | "data": { 1025 | "text/plain": [ 1026 | "Planet\n", 1027 | "MERCURY 44.0\n", 1028 | "VENUS 56.0\n", 1029 | "EARTH NaN\n", 1030 | "MOON 59.0\n", 1031 | "MARS 53.0\n", 1032 | "JUPITER 45.0\n", 1033 | "SATURN 39.0\n", 1034 | "URANUS 32.0\n", 1035 | "NEPTUNE 29.0\n", 1036 | "PLUTO 3.0\n", 1037 | "Name: FirstVisited, dtype: float64" 1038 | ] 1039 | }, 1040 | "execution_count": 28, 1041 | "metadata": {}, 1042 | "output_type": "execute_result" 1043 | } 1044 | ], 1045 | "source": [ 1046 | "2018 - planets.FirstVisited.dt.year" 1047 | ] 1048 | }, 1049 | { 1050 | "cell_type": "code", 1051 | "execution_count": null, 1052 | "metadata": {}, 1053 | "outputs": [], 1054 | "source": [] 1055 | } 1056 | ], 1057 | "metadata": { 1058 | "kernelspec": { 1059 | "display_name": "Python [default]", 1060 | "language": "python", 1061 | "name": "python3" 1062 | }, 1063 | "language_info": { 1064 | "codemirror_mode": { 1065 | "name": "ipython", 1066 | "version": 3 1067 | }, 1068 | "file_extension": ".py", 1069 | "mimetype": "text/x-python", 1070 | "name": "python", 1071 | "nbconvert_exporter": "python", 1072 | "pygments_lexer": "ipython3", 1073 | "version": "3.6.5" 1074 | }, 1075 | "toc": { 1076 | "base_numbering": 1, 1077 | "nav_menu": {}, 1078 | "number_sections": true, 1079 | "sideBar": true, 1080 | "skip_h1_title": false, 1081 | "title_cell": "Table of Contents", 1082 | "title_sidebar": "Contents", 1083 | "toc_cell": false, 1084 | "toc_position": {}, 1085 | "toc_section_display": true, 1086 | "toc_window_display": false 1087 | } 1088 | }, 1089 | "nbformat": 4, 1090 | "nbformat_minor": 2 1091 | } 1092 | 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-------------------------------------------------------------------------------- /whickham.xls: -------------------------------------------------------------------------------- 1 | outcome,smoker,age 2 | Alive,Yes,23 3 | Alive,Yes,18 4 | Dead,Yes,71 5 | Alive,No,67 6 | Alive,No,64 7 | Alive,Yes,38 8 | Alive,Yes,45 9 | Dead,No,76 10 | Alive,No,28 11 | Alive,No,27 12 | Alive,No,28 13 | Alive,Yes,34 14 | Alive,No,20 15 | Dead,Yes,72 16 | Alive,Yes,48 17 | Alive,Yes,45 18 | Dead,No,66 19 | Alive,Yes,30 20 | Alive,No,67 21 | Alive,Yes,33 22 | Dead,No,68 23 | Dead,No,72 24 | Alive,Yes,34 25 | Dead,No,61 26 | Dead,No,66 27 | Alive,No,43 28 | Dead,No,71 29 | Alive,Yes,47 30 | Dead,No,67 31 | Alive,No,22 32 | Alive,No,39 33 | Dead,Yes,71 34 | Alive,Yes,38 35 | Dead,No,80 36 | Dead,Yes,59 37 | Alive,Yes,47 38 | Dead,Yes,56 39 | Dead,No,62 40 | Alive,Yes,30 41 | Alive,Yes,51 42 | Alive,No,34 43 | Alive,No,39 44 | Dead,No,56 45 | Alive,No,32 46 | Dead,No,56 47 | Alive,Yes,60 48 | Dead,Yes,60 49 | Alive,No,30 50 | Alive,Yes,33 51 | Alive,No,37 52 | Dead,No,66 53 | Dead,Yes,71 54 | Alive,Yes,23 55 | Alive,No,38 56 | Alive,Yes,36 57 | Dead,Yes,38 58 | Alive,Yes,50 59 | Alive,No,45 60 | Alive,Yes,36 61 | Dead,No,55 62 | Dead,Yes,73 63 | Dead,Yes,52 64 | Alive,No,52 65 | Alive,Yes,37 66 | Alive,No,61 67 | Alive,No,39 68 | Alive,No,72 69 | Alive,Yes,25 70 | Alive,Yes,50 71 | Dead,No,68 72 | Alive,Yes,53 73 | Alive,No,31 74 | Dead,No,73 75 | Alive,No,54 76 | Alive,Yes,56 77 | Alive,Yes,51 78 | Dead,No,76 79 | Alive,No,55 80 | Dead,Yes,56 81 | Alive,No,56 82 | Dead,Yes,69 83 | Dead,No,79 84 | Dead,Yes,75 85 | Dead,No,67 86 | Alive,No,20 87 | Alive,No,20 88 | Dead,No,68 89 | Dead,Yes,68 90 | Alive,No,21 91 | Alive,Yes,29 92 | Alive,Yes,33 93 | Alive,Yes,30 94 | Alive,Yes,24 95 | Dead,Yes,61 96 | Alive,Yes,54 97 | Alive,No,26 98 | Dead,No,68 99 | Alive,Yes,49 100 | Dead,No,72 101 | Alive,No,21 102 | Alive,No,28 103 | Alive,No,47 104 | Dead,No,68 105 | Dead,No,71 106 | Alive,Yes,59 107 | Alive,No,64 108 | Alive,Yes,47 109 | Dead,No,84 110 | Alive,No,28 111 | Alive,No,67 112 | Alive,No,30 113 | Dead,Yes,33 114 | Dead,No,68 115 | Dead,No,55 116 | Dead,No,79 117 | Alive,No,40 118 | Alive,No,43 119 | Alive,Yes,37 120 | Dead,No,73 121 | Dead,Yes,45 122 | Alive,No,49 123 | Alive,Yes,44 124 | Alive,Yes,74 125 | Alive,Yes,20 126 | Alive,Yes,31 127 | Alive,No,20 128 | Alive,No,61 129 | Dead,Yes,66 130 | Alive,No,53 131 | Alive,Yes,36 132 | Dead,No,71 133 | Dead,Yes,64 134 | Alive,No,52 135 | Alive,No,37 136 | Alive,Yes,34 137 | Alive,No,61 138 | Alive,Yes,46 139 | Alive,Yes,26 140 | Alive,No,33 141 | Alive,No,32 142 | Alive,Yes,47 143 | Alive,No,27 144 | Alive,No,25 145 | Dead,No,69 146 | Dead,Yes,43 147 | Dead,Yes,71 148 | Alive,No,33 149 | Dead,No,73 150 | Dead,No,68 151 | Alive,No,31 152 | Alive,No,35 153 | Dead,No,73 154 | Dead,Yes,59 155 | Alive,No,31 156 | Dead,No,77 157 | Alive,Yes,33 158 | Alive,Yes,48 159 | Alive,Yes,34 160 | Alive,No,43 161 | Dead,No,68 162 | Alive,Yes,29 163 | Dead,No,71 164 | Dead,Yes,21 165 | Alive,No,18 166 | Alive,No,24 167 | Alive,Yes,30 168 | Alive,No,24 169 | Dead,No,74 170 | Alive,Yes,57 171 | Alive,No,23 172 | Alive,No,42 173 | Alive,No,34 174 | Alive,Yes,46 175 | Alive,No,40 176 | Alive,Yes,64 177 | Dead,Yes,20 178 | Alive,Yes,30 179 | Dead,Yes,81 180 | Dead,Yes,45 181 | Alive,No,26 182 | Dead,No,77 183 | Alive,Yes,53 184 | Alive,Yes,46 185 | Dead,No,69 186 | Alive,Yes,44 187 | Dead,Yes,50 188 | Alive,Yes,50 189 | Dead,No,62 190 | Dead,Yes,60 191 | Alive,No,19 192 | Alive,Yes,21 193 | Alive,Yes,50 194 | Alive,Yes,33 195 | Dead,No,59 196 | Dead,No,81 197 | Alive,Yes,31 198 | Dead,No,77 199 | Alive,Yes,23 200 | Alive,No,55 201 | Alive,No,51 202 | Alive,No,26 203 | Alive,No,20 204 | Alive,No,57 205 | Dead,No,67 206 | Alive,No,29 207 | Alive,Yes,28 208 | Alive,Yes,26 209 | Dead,No,76 210 | Alive,No,32 211 | Dead,Yes,40 212 | Alive,No,40 213 | Alive,No,52 214 | Dead,No,35 215 | Alive,Yes,27 216 | Alive,Yes,33 217 | Alive,Yes,20 218 | Alive,Yes,31 219 | Alive,No,28 220 | Alive,No,22 221 | Dead,Yes,73 222 | Alive,No,23 223 | Alive,Yes,24 224 | Alive,No,48 225 | Alive,No,60 226 | Alive,Yes,50 227 | Alive,Yes,37 228 | Alive,Yes,29 229 | Alive,No,35 230 | Dead,No,67 231 | Alive,No,33 232 | Alive,Yes,36 233 | Dead,No,64 234 | Alive,Yes,59 235 | Alive,No,23 236 | Alive,Yes,63 237 | Alive,No,26 238 | Dead,No,50 239 | Dead,No,36 240 | Dead,Yes,51 241 | Alive,No,25 242 | Alive,No,48 243 | Alive,No,24 244 | Dead,Yes,42 245 | Alive,No,64 246 | Alive,No,36 247 | Alive,Yes,47 248 | Alive,No,64 249 | Alive,No,22 250 | Alive,No,31 251 | Alive,No,55 252 | Dead,Yes,78 253 | Alive,No,27 254 | Alive,No,63 255 | Dead,Yes,83 256 | Alive,No,55 257 | Alive,Yes,57 258 | Alive,Yes,31 259 | Dead,No,64 260 | Alive,Yes,25 261 | Alive,No,38 262 | Dead,No,68 263 | Alive,No,59 264 | Alive,Yes,63 265 | Alive,Yes,18 266 | Alive,Yes,44 267 | Alive,No,38 268 | Dead,Yes,72 269 | Alive,Yes,28 270 | Alive,Yes,37 271 | Dead,No,51 272 | Dead,Yes,53 273 | Alive,Yes,31 274 | Dead,No,69 275 | Alive,Yes,51 276 | Alive,Yes,36 277 | Alive,No,27 278 | Alive,Yes,24 279 | Alive,Yes,64 280 | Alive,No,64 281 | Alive,Yes,64 282 | Dead,No,64 283 | Alive,Yes,46 284 | Alive,Yes,67 285 | Alive,Yes,40 286 | Alive,Yes,49 287 | Alive,Yes,25 288 | Alive,Yes,36 289 | Dead,No,82 290 | Alive,Yes,56 291 | Dead,Yes,56 292 | Alive,No,37 293 | Dead,Yes,59 294 | Alive,Yes,42 295 | Alive,No,44 296 | Alive,Yes,18 297 | Alive,Yes,44 298 | Alive,No,32 299 | Alive,Yes,38 300 | Alive,Yes,32 301 | Alive,Yes,29 302 | Alive,No,51 303 | Alive,Yes,34 304 | Alive,Yes,32 305 | Alive,No,23 306 | Alive,Yes,48 307 | Alive,Yes,24 308 | Alive,Yes,21 309 | Alive,No,54 310 | Dead,No,68 311 | Alive,Yes,26 312 | Alive,No,51 313 | Alive,Yes,45 314 | Alive,No,31 315 | Alive,Yes,43 316 | Alive,Yes,30 317 | Dead,No,72 318 | Alive,No,19 319 | Alive,No,31 320 | Dead,No,70 321 | Alive,No,58 322 | Alive,No,27 323 | Alive,No,56 324 | Dead,Yes,46 325 | Dead,No,75 326 | Alive,No,35 327 | Alive,No,30 328 | Dead,No,47 329 | Alive,No,63 330 | Alive,No,66 331 | Alive,Yes,41 332 | Dead,Yes,82 333 | Alive,Yes,39 334 | Alive,Yes,33 335 | Dead,Yes,60 336 | Dead,No,73 337 | Alive,Yes,26 338 | Alive,No,21 339 | Alive,Yes,39 340 | Dead,Yes,51 341 | Alive,No,28 342 | Alive,Yes,54 343 | Alive,No,51 344 | Dead,No,69 345 | Alive,Yes,63 346 | Alive,Yes,47 347 | Alive,No,38 348 | Dead,Yes,61 349 | Alive,Yes,31 350 | Alive,Yes,62 351 | Alive,No,62 352 | Alive,Yes,21 353 | Alive,Yes,32 354 | Alive,No,38 355 | Dead,No,67 356 | Alive,No,59 357 | Dead,No,71 358 | Alive,No,35 359 | Alive,Yes,28 360 | Alive,Yes,31 361 | Dead,Yes,58 362 | Alive,Yes,53 363 | Alive,No,48 364 | Alive,No,27 365 | Alive,No,30 366 | Dead,Yes,50 367 | Dead,Yes,43 368 | Alive,No,41 369 | Alive,No,37 370 | Alive,No,31 371 | Alive,No,30 372 | Alive,Yes,52 373 | Alive,Yes,37 374 | Alive,No,45 375 | Alive,Yes,36 376 | Alive,Yes,34 377 | Alive,Yes,39 378 | Alive,No,30 379 | Dead,Yes,57 380 | Alive,No,40 381 | Dead,Yes,62 382 | Alive,Yes,62 383 | Alive,Yes,60 384 | Alive,No,56 385 | Alive,No,45 386 | Alive,No,22 387 | Alive,No,27 388 | Alive,Yes,18 389 | Alive,Yes,26 390 | Dead,No,77 391 | Alive,No,33 392 | Alive,No,35 393 | Alive,No,33 394 | Dead,No,55 395 | Alive,No,45 396 | Alive,No,32 397 | Alive,No,34 398 | Alive,No,67 399 | Alive,No,35 400 | Dead,Yes,72 401 | Dead,No,79 402 | Alive,Yes,35 403 | Alive,No,38 404 | Alive,Yes,25 405 | Alive,Yes,37 406 | Alive,No,56 407 | Dead,No,66 408 | Dead,No,49 409 | Alive,Yes,33 410 | Dead,No,59 411 | Alive,No,44 412 | Alive,No,39 413 | Dead,No,58 414 | Alive,No,64 415 | Alive,No,18 416 | Alive,Yes,45 417 | Alive,No,33 418 | Alive,No,32 419 | Dead,No,80 420 | Alive,Yes,44 421 | Alive,Yes,25 422 | Alive,No,68 423 | Alive,Yes,34 424 | Alive,Yes,29 425 | Dead,Yes,72 426 | Alive,Yes,32 427 | Alive,Yes,56 428 | Alive,Yes,43 429 | Dead,Yes,60 430 | Alive,Yes,53 431 | Alive,Yes,41 432 | Alive,Yes,63 433 | Dead,Yes,57 434 | Alive,No,24 435 | Alive,Yes,39 436 | Alive,Yes,39 437 | Alive,No,31 438 | Alive,Yes,45 439 | Alive,No,66 440 | Alive,No,18 441 | Dead,No,72 442 | Alive,No,49 443 | Alive,No,37 444 | Dead,No,68 445 | Dead,Yes,69 446 | Alive,No,27 447 | Alive,Yes,20 448 | Alive,No,52 449 | Alive,Yes,62 450 | Alive,Yes,64 451 | Alive,Yes,42 452 | Alive,No,66 453 | Dead,No,78 454 | Alive,No,33 455 | Alive,No,31 456 | Alive,Yes,49 457 | Alive,No,58 458 | Alive,Yes,23 459 | Alive,Yes,41 460 | Alive,Yes,34 461 | Alive,Yes,30 462 | Alive,Yes,24 463 | Alive,Yes,31 464 | Alive,Yes,64 465 | Dead,Yes,81 466 | Dead,No,79 467 | Alive,No,54 468 | Alive,Yes,47 469 | Alive,Yes,25 470 | Dead,Yes,68 471 | Alive,Yes,23 472 | Alive,No,74 473 | Alive,Yes,25 474 | Alive,Yes,54 475 | Dead,No,78 476 | Dead,No,65 477 | Alive,Yes,39 478 | Dead,No,73 479 | Alive,No,27 480 | Alive,Yes,62 481 | Alive,Yes,26 482 | Dead,Yes,57 483 | Alive,No,41 484 | Alive,No,41 485 | Dead,Yes,66 486 | Dead,Yes,48 487 | Dead,Yes,68 488 | Alive,Yes,43 489 | Alive,No,28 490 | Alive,Yes,58 491 | Dead,Yes,56 492 | Dead,Yes,40 493 | Dead,No,69 494 | Alive,Yes,47 495 | Alive,Yes,52 496 | Alive,Yes,52 497 | Dead,No,80 498 | Alive,Yes,64 499 | Alive,No,39 500 | Alive,Yes,54 501 | Alive,No,55 502 | Alive,No,51 503 | Alive,Yes,28 504 | Dead,No,77 505 | Alive,Yes,27 506 | Dead,No,82 507 | Dead,No,65 508 | Dead,No,80 509 | Alive,No,46 510 | Alive,Yes,38 511 | Alive,Yes,28 512 | Dead,No,49 513 | Alive,Yes,44 514 | Alive,No,30 515 | Alive,No,63 516 | Alive,No,63 517 | Dead,Yes,68 518 | Alive,Yes,46 519 | Alive,No,52 520 | Alive,No,52 521 | Alive,No,55 522 | Alive,No,74 523 | Alive,Yes,44 524 | Alive,No,51 525 | Alive,Yes,35 526 | Alive,Yes,36 527 | Alive,Yes,33 528 | Alive,No,26 529 | Alive,No,21 530 | Alive,No,18 531 | Alive,No,30 532 | Dead,No,81 533 | Alive,Yes,63 534 | Dead,Yes,42 535 | Alive,No,35 536 | Alive,Yes,38 537 | Alive,No,28 538 | Alive,Yes,62 539 | Alive,No,39 540 | Alive,No,39 541 | Alive,No,61 542 | Alive,Yes,19 543 | Alive,Yes,46 544 | Dead,Yes,70 545 | Alive,Yes,38 546 | Alive,No,33 547 | Alive,Yes,53 548 | Alive,No,19 549 | Dead,No,82 550 | Alive,Yes,49 551 | Alive,Yes,47 552 | Dead,No,78 553 | Alive,Yes,22 554 | Alive,Yes,31 555 | Alive,No,31 556 | Alive,No,57 557 | Alive,No,65 558 | Dead,No,68 559 | Alive,Yes,22 560 | Alive,Yes,27 561 | Alive,Yes,50 562 | Dead,Yes,53 563 | Alive,No,34 564 | Alive,Yes,37 565 | Alive,No,35 566 | Alive,No,38 567 | Dead,No,43 568 | Alive,Yes,34 569 | Alive,No,64 570 | Alive,No,43 571 | Dead,Yes,57 572 | Alive,Yes,55 573 | Alive,Yes,34 574 | Alive,No,29 575 | Alive,No,18 576 | Dead,No,38 577 | Alive,Yes,40 578 | Alive,No,44 579 | Dead,No,71 580 | Dead,Yes,61 581 | Alive,Yes,26 582 | Dead,No,35 583 | Alive,No,26 584 | Alive,No,39 585 | Alive,Yes,43 586 | Alive,Yes,44 587 | Alive,No,38 588 | Alive,No,37 589 | Dead,Yes,52 590 | Alive,No,56 591 | Dead,No,70 592 | Alive,Yes,39 593 | Dead,Yes,57 594 | Alive,Yes,46 595 | Alive,Yes,28 596 | Alive,No,21 597 | Dead,Yes,66 598 | Alive,Yes,38 599 | Alive,Yes,32 600 | Alive,Yes,23 601 | Alive,No,61 602 | Alive,Yes,32 603 | Alive,No,27 604 | Dead,Yes,73 605 | Alive,Yes,36 606 | Alive,No,33 607 | Alive,No,28 608 | Alive,No,43 609 | Alive,Yes,56 610 | Alive,No,27 611 | Alive,No,36 612 | Alive,No,20 613 | Dead,No,69 614 | Alive,No,44 615 | Alive,Yes,26 616 | Dead,Yes,31 617 | Dead,No,72 618 | Dead,No,84 619 | Alive,Yes,48 620 | Alive,No,18 621 | Dead,Yes,82 622 | Alive,Yes,51 623 | Alive,Yes,33 624 | Alive,No,56 625 | Alive,No,74 626 | Alive,No,24 627 | Dead,No,74 628 | Alive,No,28 629 | Alive,Yes,58 630 | Alive,Yes,49 631 | Dead,Yes,64 632 | Alive,No,34 633 | Dead,No,84 634 | Dead,Yes,47 635 | Alive,Yes,58 636 | Dead,Yes,63 637 | Dead,No,57 638 | Alive,Yes,18 639 | Alive,No,23 640 | Alive,No,51 641 | Alive,No,55 642 | Alive,No,49 643 | Dead,No,60 644 | Alive,No,39 645 | Alive,No,48 646 | Alive,No,28 647 | Dead,No,74 648 | Alive,Yes,61 649 | Alive,Yes,33 650 | Dead,No,64 651 | Dead,No,77 652 | Alive,Yes,51 653 | Alive,Yes,23 654 | Dead,No,43 655 | Alive,No,47 656 | Alive,No,27 657 | Alive,No,48 658 | Alive,Yes,29 659 | Alive,No,45 660 | Alive,Yes,31 661 | Dead,Yes,60 662 | Alive,No,53 663 | Alive,No,26 664 | Dead,Yes,61 665 | Alive,No,38 666 | Alive,No,22 667 | Alive,Yes,41 668 | Alive,Yes,27 669 | Alive,No,63 670 | Dead,Yes,60 671 | Dead,No,64 672 | Alive,No,58 673 | Alive,No,42 674 | Dead,No,82 675 | Dead,No,62 676 | Alive,No,64 677 | Alive,No,34 678 | Alive,No,58 679 | Alive,Yes,63 680 | Dead,No,63 681 | Dead,No,79 682 | Alive,No,44 683 | Alive,No,41 684 | Alive,No,34 685 | Alive,Yes,38 686 | Alive,Yes,54 687 | Alive,No,53 688 | Dead,No,80 689 | Dead,Yes,46 690 | Alive,Yes,61 691 | Dead,No,72 692 | Alive,Yes,61 693 | Alive,Yes,28 694 | Alive,Yes,20 695 | Dead,Yes,46 696 | Dead,No,56 697 | Dead,No,84 698 | Dead,Yes,64 699 | Dead,No,42 700 | Alive,No,30 701 | Alive,No,48 702 | Alive,Yes,25 703 | Dead,No,76 704 | Dead,Yes,61 705 | Alive,No,41 706 | Alive,No,29 707 | Dead,No,80 708 | Alive,Yes,56 709 | Alive,Yes,58 710 | Dead,No,73 711 | Alive,No,18 712 | Alive,No,42 713 | Dead,No,67 714 | Alive,No,30 715 | Dead,No,78 716 | Dead,Yes,36 717 | Dead,No,78 718 | Alive,No,44 719 | Alive,No,35 720 | Alive,Yes,53 721 | Dead,No,52 722 | Alive,Yes,63 723 | Dead,No,79 724 | Alive,No,51 725 | Alive,No,59 726 | Alive,No,62 727 | Alive,No,35 728 | Alive,No,32 729 | Alive,Yes,48 730 | Dead,Yes,53 731 | Dead,Yes,49 732 | Alive,Yes,23 733 | Alive,Yes,50 734 | Alive,No,26 735 | Dead,Yes,44 736 | Dead,Yes,71 737 | Dead,No,78 738 | Alive,No,40 739 | Alive,No,71 740 | Alive,Yes,62 741 | Alive,No,30 742 | Alive,No,32 743 | Dead,No,71 744 | Alive,No,70 745 | Alive,No,27 746 | Dead,Yes,60 747 | Dead,No,65 748 | Dead,No,72 749 | Alive,No,22 750 | Alive,Yes,26 751 | Alive,No,26 752 | Alive,Yes,30 753 | Alive,Yes,21 754 | Alive,Yes,28 755 | Alive,No,60 756 | Alive,No,53 757 | Alive,Yes,59 758 | Dead,Yes,63 759 | Alive,Yes,36 760 | Alive,No,34 761 | Alive,Yes,61 762 | Dead,No,65 763 | Dead,Yes,59 764 | Dead,No,70 765 | Dead,Yes,48 766 | Alive,Yes,50 767 | Dead,No,83 768 | Alive,No,51 769 | Alive,Yes,24 770 | Alive,No,46 771 | Alive,No,19 772 | Alive,Yes,36 773 | Alive,No,33 774 | Dead,No,56 775 | Alive,No,57 776 | Alive,No,44 777 | Alive,No,53 778 | Alive,Yes,30 779 | Alive,No,42 780 | Alive,Yes,42 781 | Alive,Yes,58 782 | Alive,No,32 783 | Alive,No,30 784 | Dead,No,73 785 | Alive,No,34 786 | Dead,Yes,76 787 | Alive,Yes,51 788 | Dead,No,71 789 | Dead,No,60 790 | Alive,No,36 791 | Alive,Yes,18 792 | Alive,No,34 793 | Dead,No,70 794 | Alive,Yes,50 795 | Alive,Yes,41 796 | Alive,No,55 797 | Alive,No,36 798 | Dead,Yes,66 799 | Alive,No,58 800 | Alive,Yes,31 801 | Alive,No,36 802 | Dead,No,68 803 | Alive,No,24 804 | Alive,No,25 805 | Alive,Yes,42 806 | Alive,No,31 807 | Alive,Yes,18 808 | Alive,Yes,27 809 | Alive,No,20 810 | Alive,No,40 811 | Alive,Yes,57 812 | Alive,No,73 813 | Alive,No,44 814 | Alive,No,63 815 | Alive,No,19 816 | Dead,Yes,49 817 | Alive,No,59 818 | Alive,Yes,23 819 | Alive,Yes,53 820 | Alive,Yes,51 821 | Alive,Yes,22 822 | Alive,No,24 823 | Alive,Yes,51 824 | Alive,Yes,26 825 | Dead,No,68 826 | Dead,No,56 827 | Alive,No,47 828 | Alive,Yes,36 829 | Alive,Yes,46 830 | Alive,No,26 831 | Alive,Yes,74 832 | Alive,No,26 833 | Alive,Yes,52 834 | Alive,No,57 835 | Alive,Yes,34 836 | Alive,No,60 837 | Alive,Yes,32 838 | Alive,Yes,41 839 | Dead,No,82 840 | Alive,Yes,31 841 | Alive,No,68 842 | Alive,No,25 843 | Dead,No,84 844 | Alive,No,37 845 | Alive,No,37 846 | Dead,No,83 847 | Alive,No,62 848 | Alive,No,25 849 | Alive,Yes,55 850 | Alive,No,23 851 | Alive,Yes,52 852 | Alive,Yes,33 853 | Alive,Yes,53 854 | Alive,No,55 855 | Alive,Yes,45 856 | Alive,Yes,19 857 | Alive,Yes,53 858 | Alive,No,18 859 | Alive,Yes,32 860 | Alive,No,32 861 | Dead,No,67 862 | Dead,Yes,60 863 | Alive,Yes,35 864 | Alive,Yes,60 865 | Alive,Yes,56 866 | Alive,No,28 867 | Dead,No,61 868 | Alive,Yes,46 869 | Alive,No,22 870 | Dead,No,55 871 | Dead,No,66 872 | Dead,Yes,71 873 | Dead,No,68 874 | Alive,No,50 875 | Alive,Yes,32 876 | Alive,No,24 877 | Alive,No,38 878 | Alive,No,64 879 | Dead,Yes,51 880 | Alive,Yes,29 881 | Dead,No,77 882 | Alive,No,25 883 | Alive,No,50 884 | Alive,No,61 885 | Alive,Yes,23 886 | Dead,No,67 887 | Dead,No,73 888 | Alive,No,23 889 | Alive,Yes,59 890 | Dead,No,61 891 | Alive,Yes,43 892 | Alive,No,25 893 | Alive,No,39 894 | Alive,No,45 895 | Alive,No,25 896 | Alive,Yes,52 897 | Dead,No,46 898 | Alive,No,25 899 | Alive,No,30 900 | Alive,Yes,62 901 | Alive,No,58 902 | Alive,Yes,25 903 | Alive,No,31 904 | Dead,No,46 905 | Dead,Yes,27 906 | Dead,No,68 907 | Alive,Yes,35 908 | Alive,No,25 909 | Alive,Yes,42 910 | Dead,No,72 911 | Alive,Yes,55 912 | Alive,No,29 913 | Alive,Yes,29 914 | Alive,Yes,36 915 | Dead,No,51 916 | Dead,No,80 917 | Dead,No,74 918 | Alive,No,20 919 | Dead,No,45 920 | Alive,No,46 921 | Alive,Yes,43 922 | Alive,No,66 923 | Dead,Yes,45 924 | Alive,Yes,71 925 | Alive,No,43 926 | Alive,Yes,27 927 | Alive,No,22 928 | Alive,Yes,41 929 | Alive,No,60 930 | Alive,Yes,46 931 | Alive,No,29 932 | Dead,Yes,55 933 | Alive,Yes,36 934 | Dead,No,84 935 | Alive,No,67 936 | Dead,No,45 937 | Alive,No,34 938 | Dead,No,65 939 | Alive,Yes,32 940 | Dead,No,71 941 | Alive,No,26 942 | Alive,Yes,26 943 | Dead,No,72 944 | Alive,No,45 945 | Alive,No,27 946 | Alive,Yes,36 947 | Alive,No,34 948 | Dead,No,78 949 | Alive,Yes,20 950 | Alive,Yes,46 951 | Alive,No,27 952 | Alive,No,74 953 | Alive,Yes,46 954 | Alive,Yes,31 955 | Alive,Yes,46 956 | Alive,No,58 957 | Dead,No,72 958 | Dead,Yes,56 959 | Alive,No,45 960 | Alive,No,60 961 | Dead,No,65 962 | Dead,No,71 963 | Dead,No,76 964 | Alive,No,55 965 | Alive,No,35 966 | Alive,Yes,44 967 | Dead,Yes,47 968 | Dead,Yes,63 969 | Alive,Yes,32 970 | Alive,No,43 971 | Alive,Yes,35 972 | Dead,Yes,63 973 | Alive,No,29 974 | Alive,No,51 975 | Alive,Yes,42 976 | Dead,Yes,45 977 | Alive,Yes,19 978 | Dead,No,69 979 | Alive,Yes,39 980 | Dead,Yes,61 981 | Alive,Yes,19 982 | Alive,No,53 983 | Alive,Yes,61 984 | Alive,No,40 985 | Alive,No,37 986 | Alive,No,37 987 | Dead,No,65 988 | Alive,No,74 989 | Alive,Yes,50 990 | Alive,Yes,25 991 | Alive,No,32 992 | Alive,No,28 993 | Alive,No,71 994 | Dead,No,27 995 | Alive,No,23 996 | Alive,Yes,64 997 | Alive,No,28 998 | Dead,No,56 999 | Alive,No,25 1000 | Dead,Yes,61 1001 | Alive,No,63 1002 | Alive,No,24 1003 | Alive,No,64 1004 | Dead,No,58 1005 | Alive,No,34 1006 | Alive,No,31 1007 | Alive,Yes,33 1008 | Alive,Yes,48 1009 | Alive,Yes,55 1010 | Alive,No,72 1011 | Alive,No,43 1012 | Alive,Yes,20 1013 | Alive,Yes,62 1014 | Alive,No,18 1015 | Alive,Yes,21 1016 | Alive,No,38 1017 | Alive,Yes,45 1018 | Dead,Yes,81 1019 | Alive,Yes,66 1020 | Alive,No,33 1021 | Alive,Yes,32 1022 | Alive,No,29 1023 | Alive,Yes,53 1024 | Alive,No,21 1025 | Alive,No,61 1026 | Alive,No,24 1027 | Dead,No,54 1028 | Alive,Yes,46 1029 | Alive,No,58 1030 | Dead,No,65 1031 | Dead,No,82 1032 | Alive,No,53 1033 | Dead,No,33 1034 | Alive,Yes,49 1035 | Alive,No,42 1036 | Alive,Yes,43 1037 | Alive,Yes,20 1038 | Dead,No,56 1039 | Dead,No,82 1040 | Dead,No,72 1041 | Alive,No,20 1042 | Dead,No,82 1043 | Alive,No,62 1044 | Alive,Yes,54 1045 | Alive,No,54 1046 | Dead,Yes,82 1047 | Alive,Yes,47 1048 | Alive,Yes,18 1049 | Dead,No,72 1050 | Alive,Yes,35 1051 | Dead,No,69 1052 | Alive,No,52 1053 | Dead,No,59 1054 | Dead,Yes,52 1055 | Alive,Yes,29 1056 | Alive,No,21 1057 | Alive,No,28 1058 | Alive,Yes,25 1059 | Alive,Yes,25 1060 | Alive,Yes,58 1061 | Alive,Yes,43 1062 | Alive,No,62 1063 | Dead,No,67 1064 | Alive,Yes,48 1065 | Alive,Yes,54 1066 | Alive,Yes,42 1067 | Alive,No,66 1068 | Alive,Yes,23 1069 | Alive,Yes,67 1070 | Alive,No,47 1071 | Alive,No,28 1072 | Alive,No,38 1073 | Alive,No,64 1074 | Dead,No,57 1075 | Alive,No,56 1076 | Alive,No,34 1077 | Alive,Yes,50 1078 | Alive,No,58 1079 | Alive,No,32 1080 | Dead,No,77 1081 | Alive,Yes,23 1082 | Alive,Yes,36 1083 | Dead,No,18 1084 | Alive,No,47 1085 | Dead,No,70 1086 | Dead,Yes,60 1087 | Dead,No,71 1088 | Dead,Yes,56 1089 | Alive,Yes,32 1090 | Alive,No,28 1091 | Alive,No,51 1092 | Alive,No,37 1093 | Alive,No,30 1094 | Dead,No,77 1095 | Dead,No,70 1096 | Alive,No,41 1097 | Alive,No,32 1098 | Alive,Yes,48 1099 | Alive,No,53 1100 | Dead,Yes,65 1101 | Dead,No,66 1102 | Dead,Yes,59 1103 | Dead,No,60 1104 | Dead,Yes,58 1105 | Alive,No,64 1106 | Alive,Yes,61 1107 | Dead,No,62 1108 | Alive,No,58 1109 | Alive,No,37 1110 | Dead,Yes,53 1111 | Alive,No,65 1112 | Alive,No,27 1113 | Dead,No,72 1114 | Dead,Yes,60 1115 | Alive,Yes,45 1116 | Alive,Yes,63 1117 | Alive,No,32 1118 | Alive,No,18 1119 | Alive,Yes,49 1120 | Alive,No,34 1121 | Alive,No,23 1122 | Alive,No,61 1123 | Alive,No,44 1124 | Alive,No,66 1125 | Alive,No,28 1126 | Alive,Yes,44 1127 | Alive,Yes,29 1128 | Alive,Yes,38 1129 | Alive,Yes,52 1130 | Alive,Yes,54 1131 | Alive,Yes,63 1132 | Dead,Yes,45 1133 | Dead,No,74 1134 | Alive,No,24 1135 | Alive,No,40 1136 | Alive,No,28 1137 | Alive,Yes,30 1138 | Alive,Yes,43 1139 | Dead,Yes,63 1140 | Alive,Yes,33 1141 | Alive,Yes,41 1142 | Alive,No,60 1143 | Alive,Yes,31 1144 | Alive,Yes,47 1145 | Dead,No,81 1146 | Dead,No,78 1147 | Alive,No,56 1148 | Dead,No,73 1149 | Alive,No,56 1150 | Alive,Yes,28 1151 | Alive,Yes,22 1152 | Alive,No,34 1153 | Alive,No,48 1154 | Alive,No,27 1155 | Alive,Yes,47 1156 | Alive,No,31 1157 | Alive,No,47 1158 | Dead,No,60 1159 | Dead,No,59 1160 | Dead,No,80 1161 | Alive,Yes,41 1162 | Dead,Yes,46 1163 | Alive,Yes,50 1164 | Alive,No,48 1165 | Alive,No,37 1166 | Alive,Yes,50 1167 | Alive,Yes,25 1168 | Alive,No,25 1169 | Alive,No,41 1170 | Alive,No,24 1171 | Alive,Yes,57 1172 | Dead,No,71 1173 | Alive,Yes,26 1174 | Dead,No,32 1175 | Alive,No,25 1176 | Dead,No,84 1177 | Alive,Yes,19 1178 | Alive,No,26 1179 | Alive,Yes,58 1180 | Dead,Yes,67 1181 | Alive,No,24 1182 | Dead,Yes,74 1183 | Alive,Yes,58 1184 | Alive,No,57 1185 | Alive,Yes,52 1186 | Alive,Yes,42 1187 | Alive,No,42 1188 | Dead,No,69 1189 | Dead,Yes,39 1190 | Dead,Yes,84 1191 | Alive,Yes,57 1192 | Alive,No,42 1193 | Alive,No,35 1194 | Alive,No,23 1195 | Alive,No,65 1196 | Alive,Yes,28 1197 | Alive,No,39 1198 | Alive,Yes,36 1199 | Alive,No,40 1200 | Alive,No,39 1201 | Alive,No,19 1202 | Alive,No,61 1203 | Alive,Yes,37 1204 | Alive,Yes,52 1205 | Alive,Yes,62 1206 | Alive,No,30 1207 | Alive,Yes,23 1208 | Dead,Yes,72 1209 | Alive,Yes,46 1210 | Alive,No,33 1211 | Alive,No,32 1212 | Alive,No,27 1213 | Dead,Yes,72 1214 | Dead,Yes,53 1215 | Dead,Yes,58 1216 | Alive,Yes,32 1217 | Alive,No,25 1218 | Dead,No,71 1219 | Alive,Yes,37 1220 | Alive,Yes,41 1221 | Dead,No,75 1222 | Alive,No,29 1223 | Dead,Yes,62 1224 | Alive,Yes,30 1225 | Alive,No,20 1226 | Alive,Yes,26 1227 | Dead,No,60 1228 | Dead,No,65 1229 | Dead,Yes,63 1230 | Alive,No,19 1231 | Dead,Yes,55 1232 | Alive,Yes,45 1233 | Alive,No,28 1234 | Alive,Yes,21 1235 | Alive,No,35 1236 | Alive,No,24 1237 | Dead,No,74 1238 | Dead,No,63 1239 | Alive,No,44 1240 | Alive,Yes,26 1241 | Dead,No,84 1242 | Alive,Yes,27 1243 | Alive,No,28 1244 | Dead,Yes,60 1245 | Alive,No,44 1246 | Dead,No,82 1247 | Dead,No,29 1248 | Alive,Yes,52 1249 | Alive,Yes,42 1250 | Alive,Yes,18 1251 | Dead,No,61 1252 | Dead,No,60 1253 | Alive,Yes,19 1254 | Dead,No,84 1255 | Dead,No,72 1256 | Alive,No,59 1257 | Dead,No,78 1258 | Alive,No,41 1259 | Alive,No,21 1260 | Alive,No,31 1261 | Dead,No,83 1262 | Alive,No,62 1263 | Dead,No,76 1264 | Dead,Yes,63 1265 | Alive,No,44 1266 | Dead,No,65 1267 | Alive,No,33 1268 | Alive,Yes,40 1269 | Alive,No,50 1270 | Dead,No,74 1271 | Alive,No,19 1272 | Alive,No,50 1273 | Dead,Yes,62 1274 | Alive,Yes,25 1275 | Dead,Yes,66 1276 | Alive,No,67 1277 | Alive,Yes,18 1278 | Alive,Yes,28 1279 | Alive,Yes,49 1280 | Alive,Yes,39 1281 | Alive,Yes,33 1282 | Alive,No,44 1283 | Alive,No,48 1284 | Alive,Yes,37 1285 | Dead,Yes,71 1286 | Dead,No,66 1287 | Alive,Yes,34 1288 | Dead,Yes,77 1289 | Alive,No,48 1290 | Alive,Yes,60 1291 | Alive,No,41 1292 | Alive,No,55 1293 | Alive,Yes,46 1294 | Alive,No,55 1295 | Alive,No,20 1296 | Dead,Yes,80 1297 | Alive,Yes,57 1298 | Alive,No,52 1299 | Alive,Yes,48 1300 | Alive,No,30 1301 | Alive,Yes,57 1302 | Alive,Yes,37 1303 | Alive,No,27 1304 | Alive,No,37 1305 | Alive,No,36 1306 | Alive,No,23 1307 | Dead,No,55 1308 | Alive,No,40 1309 | Alive,No,33 1310 | Alive,No,23 1311 | Alive,Yes,35 1312 | Alive,No,33 1313 | Alive,Yes,21 1314 | Alive,No,46 1315 | Alive,Yes,41 1316 | --------------------------------------------------------------------------------