├── How it works - Bike Share Regression PyTorch Lightning.ipynb
├── How it works - Covariance, Correlation & Linear Regression.ipynb
├── How it works - GeoPandas basics.ipynb
├── How it works - GeoPandas detailed mapping.ipynb
├── How it works - GeoPandas spatial joins.ipynb
├── How it works - KMeans Clustering.ipynb
├── How it works - Multivariate regression.ipynb
├── How it works - Pandas, data manipulation.ipynb
├── How it works - Pandas, data selection.ipynb
├── How it works - Pandas, groupby method.ipynb
├── How it works - Pandas, mapping series values.ipynb
├── How it works - Pandas, merge method.ipynb
├── How it works - Pandas, pivot tables.ipynb
├── How it works - Polynomial Regression.ipynb
├── How it works - Principal Component Analysis.ipynb
├── How it works - basic lists.ipynb
├── How it works - data structures for deep learning.ipynb
├── How it works - labelling districts in GeoPandas.ipynb
├── How it works - list comprehensions.ipynb
├── How it works - lists vs arrays.ipynb
├── How it works - positive & negative indexation.ipynb
├── How it works - visualizing data overlaps.ipynb
├── Practice Run - Linear Regression Age vs Blood Pressure.ipynb
├── Practice Run - Linear Regression Fish Sizes.ipynb
├── README.md
├── customer_product_list.xlsx
├── data_structures.png
├── flattening.png
├── img_5.png
├── multiplication.png
├── sea_picture.jpg
├── sorted.jpg
├── take1.png
├── take2.png
├── tensor_shape_2.png
├── unsorted.jpg
└── weight_matrix_detail_2.png


/How it works - Covariance, Correlation & Linear Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Co-variance"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import numpy as np\n",
 17 |     "import matplotlib.pyplot as plt\n",
 18 |     "%matplotlib inline"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "markdown",
 23 |    "metadata": {},
 24 |    "source": [
 25 |     "<b>wikiHow</b> provides a very easy to understand example of how to calculate co-variance<br>\n",
 26 |     "https://www.wikihow.com/Calculate-Covariance <br>\n",
 27 |     "Using their sample data, below we see how to view the data, and then find the covariance using np.cov():"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 2,
 33 |    "metadata": {},
 34 |    "outputs": [
 35 |     {
 36 |      "data": {
 37 |       "text/plain": [
 38 |        "<matplotlib.collections.PathCollection at 0x114697cc0>"
 39 |       ]
 40 |      },
 41 |      "execution_count": 2,
 42 |      "metadata": {},
 43 |      "output_type": "execute_result"
 44 |     },
 45 |     {
 46 |      "data": {
 47 |       "image/png": 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\n",
 48 |       "text/plain": [
 49 |        "<matplotlib.figure.Figure at 0x10ec7c550>"
 50 |       ]
 51 |      },
 52 |      "metadata": {},
 53 |      "output_type": "display_data"
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "x = [1, 3, 2, 5, 8, 7, 12, 2, 4]\n",
 58 |     "y = [8, 6, 9, 4, 3, 3, 2, 7, 7]\n",
 59 |     "plt.scatter(x, y)"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "markdown",
 64 |    "metadata": {},
 65 |    "source": [
 66 |     "Note that as per https://machinelearningmastery.com/introduction-to-expected-value-variance-and-covariance/ np.cov() returns the correlation matrix, but the key value we are looking for is the <b>[0,1]</b> element, i.e. -8.07"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 3,
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "data": {
 76 |       "text/plain": [
 77 |        "array([[ 12.61111111,  -8.06944444],\n",
 78 |        "       [ -8.06944444,   6.27777778]])"
 79 |       ]
 80 |      },
 81 |      "execution_count": 3,
 82 |      "metadata": {},
 83 |      "output_type": "execute_result"
 84 |     }
 85 |    ],
 86 |    "source": [
 87 |     "np.cov(x, y)"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "markdown",
 92 |    "metadata": {},
 93 |    "source": [
 94 |     "Let's do another example which is fairly typical: the relationship between <b>heights and weights</b>:"
 95 |    ]
 96 |   },
 97 |   {
 98 |    "cell_type": "code",
 99 |    "execution_count": 14,
100 |    "metadata": {},
101 |    "outputs": [],
102 |    "source": [
103 |     "# First let's get a normally distributed sample of people heights\n",
104 |     "heights = np.random.normal(1.65, 0.10, 100)"
105 |    ]
106 |   },
107 |   {
108 |    "cell_type": "code",
109 |    "execution_count": 15,
110 |    "metadata": {},
111 |    "outputs": [],
112 |    "source": [
113 |     "# And a normally distributed sample of people weights\n",
114 |     "weights = np.random.normal(60, 8, 100)"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": 16,
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/plain": [
125 |        "<matplotlib.collections.PathCollection at 0x1148d3048>"
126 |       ]
127 |      },
128 |      "execution_count": 16,
129 |      "metadata": {},
130 |      "output_type": "execute_result"
131 |     },
132 |     {
133 |      "data": {
134 |       "image/png": 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\n",
135 |       "text/plain": [
136 |        "<matplotlib.figure.Figure at 0x1147cf710>"
137 |       ]
138 |      },
139 |      "metadata": {},
140 |      "output_type": "display_data"
141 |     }
142 |    ],
143 |    "source": [
144 |     "# Because we have not setup any kind of relationship between the 2 we can quickly see there is no real \n",
145 |     "# relationship between heights and weights if we visualize the data:\n",
146 |     "plt.scatter(heights, weights)"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 17,
152 |    "metadata": {},
153 |    "outputs": [],
154 |    "source": [
155 |     "# But we can change that by sorting the data:\n",
156 |     "heights.sort()\n",
157 |     "weights.sort()"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 18,
163 |    "metadata": {},
164 |    "outputs": [
165 |     {
166 |      "data": {
167 |       "text/plain": [
168 |        "<matplotlib.collections.PathCollection at 0x1149e1080>"
169 |       ]
170 |      },
171 |      "execution_count": 18,
172 |      "metadata": {},
173 |      "output_type": "execute_result"
174 |     },
175 |     {
176 |      "data": {
177 |       "image/png": 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\n",
178 |       "text/plain": [
179 |        "<matplotlib.figure.Figure at 0x1148b5f60>"
180 |       ]
181 |      },
182 |      "metadata": {},
183 |      "output_type": "display_data"
184 |     }
185 |    ],
186 |    "source": [
187 |     "# Having forced a relationship we can clearly see this in the data\n",
188 |     "plt.scatter(heights, weights)"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "code",
193 |    "execution_count": 20,
194 |    "metadata": {},
195 |    "outputs": [
196 |     {
197 |      "data": {
198 |       "text/plain": [
199 |        "array([[  7.87464896e-03,   7.10461347e-01],\n",
200 |        "       [  7.10461347e-01,   6.58789965e+01]])"
201 |       ]
202 |      },
203 |      "execution_count": 20,
204 |      "metadata": {},
205 |      "output_type": "execute_result"
206 |     }
207 |    ],
208 |    "source": [
209 |     "# Now let's find the co-variance...\n",
210 |     "np.cov(heights, weights)"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": 21,
216 |    "metadata": {
217 |     "scrolled": true
218 |    },
219 |    "outputs": [
220 |     {
221 |      "name": "stdout",
222 |      "output_type": "stream",
223 |      "text": [
224 |       "0.710461347301\n"
225 |      ]
226 |     }
227 |    ],
228 |    "source": [
229 |     "# We can also find this answer the long way based on the formula:\n",
230 |     "# ∑(xi - xavg)(yi - yavg) / n - 1\n",
231 |     "\n",
232 |     "mean_height = np.mean(heights)\n",
233 |     "mean_weight = np.mean(weights)\n",
234 |     "heights_array = np.array([hi - mean_height for hi in heights])\n",
235 |     "weights_array = np.array([wi - mean_weight for wi in weights])\n",
236 |     "numerator = heights_array @ weights_array\n",
237 |     "denominator = len(heights) - 1\n",
238 |     "covariance = numerator / denominator\n",
239 |     "print(covariance)"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "markdown",
244 |    "metadata": {},
245 |    "source": [
246 |     "Interpreting the result:<br>\n",
247 |     "- <b>Is it positive or negative?</b> If it's positive then as one variable increases so does the other (e.g. heights and weights). If it's negative then as one variable increases the other decreases (e.g. practice hours and math test scores)<br>\n",
248 |     "- <b>How big is the number in relation to the data?</b> If it's quite a big number then the relationship is quite a strong one. If it's quite a small number in relation to the data, say 0.00something then the relationship is probably negligible.<br>\n",
249 |     "- By looking at the spread of the data above we can <b>check this understanding</b>...<p>\n",
250 |     "But of course the difficulty here is \"what is a big number?\" since we have 2 different units of measure (meters and kilograms)!<br>"
251 |    ]
252 |   },
253 |   {
254 |    "cell_type": "markdown",
255 |    "metadata": {},
256 |    "source": [
257 |     "## Correlation\n",
258 |     "Maths is fun has a lovely article on how to calculate correlation: https://www.mathsisfun.com/data/correlation.html – also infinitely intelligible!<br>\n",
259 |     "Correlation will normalize the different units of measure to give a standardized value between -1 (perfect negative correlation) and 1 (perfect positive correlation) with 0 representing absolutely no correlation whatsoever."
260 |    ]
261 |   },
262 |   {
263 |    "cell_type": "code",
264 |    "execution_count": 22,
265 |    "metadata": {},
266 |    "outputs": [
267 |     {
268 |      "data": {
269 |       "text/plain": [
270 |        "array([[ 1.        ,  0.98639614],\n",
271 |        "       [ 0.98639614,  1.        ]])"
272 |       ]
273 |      },
274 |      "execution_count": 22,
275 |      "metadata": {},
276 |      "output_type": "execute_result"
277 |     }
278 |    ],
279 |    "source": [
280 |     "# Finding the correlation co-efficient is easy enough with numpy!\n",
281 |     "np.corrcoef(heights, weights)"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 23,
287 |    "metadata": {},
288 |    "outputs": [
289 |     {
290 |      "name": "stdout",
291 |      "output_type": "stream",
292 |      "text": [
293 |       "0.986396144201\n"
294 |      ]
295 |     }
296 |    ],
297 |    "source": [
298 |     "# We can also find this answer the long way by building on the values found for co-variance above. The formula is...\n",
299 |     "# ∑(xi - xavg).(yi - yavg) / sqrt(x.x * y.y)\n",
300 |     "heights_sq = heights_array @ heights_array\n",
301 |     "weights_sq = weights_array @ weights_array\n",
302 |     "correlation = numerator / np.sqrt(heights_sq * weights_sq)\n",
303 |     "print(correlation)"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "markdown",
308 |    "metadata": {},
309 |    "source": [
310 |     "So we DO indeed have a very high positive correlation, as could be seen in the plot!"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "markdown",
315 |    "metadata": {},
316 |    "source": [
317 |     "## Linear regression"
318 |    ]
319 |   },
320 |   {
321 |    "cell_type": "markdown",
322 |    "metadata": {},
323 |    "source": [
324 |     "So now that we have established that there is a high correlation between heights and weights, we would like to find the line that best fits this relationship. Why? because then given the height of any other random future person we could predict their probable weight, and vice-versa."
325 |    ]
326 |   },
327 |   {
328 |    "cell_type": "markdown",
329 |    "metadata": {},
330 |    "source": [
331 |     "Mathsisfun provides a very easy to understand lesson on how least squares regression is calculated:\n",
332 |     "https://www.mathsisfun.com/data/least-squares-regression.html. Essentially, given that the equation of a line is expressed as y = mx + b, we are trying to solve for the constants m and b so that we get the line that represents the best fit for our data (the one that minimizes the squared differences from the data points to the line in each case)."
333 |    ]
334 |   },
335 |   {
336 |    "cell_type": "code",
337 |    "execution_count": 36,
338 |    "metadata": {},
339 |    "outputs": [
340 |     {
341 |      "name": "stdout",
342 |      "output_type": "stream",
343 |      "text": [
344 |       "90.2213356483 -88.82225037 0.986396144201\n"
345 |      ]
346 |     }
347 |    ],
348 |    "source": [
349 |     "# We'll need the stats module from scipy\n",
350 |     "from scipy import stats\n",
351 |     "\n",
352 |     "# Using the linregress() function returns 5 values, which we name here for convenience:\n",
353 |     "slope, intercept, r_value, p_value, std_err = stats.linregress(heights, weights)\n",
354 |     "\n",
355 |     "# Slope = m from our equation, Intercept = b, r_value = our correlation co-efficient\n",
356 |     "print(slope, intercept, r_value)"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": 37,
362 |    "metadata": {},
363 |    "outputs": [
364 |     {
365 |      "data": {
366 |       "text/plain": [
367 |        "0.9729773532953242"
368 |       ]
369 |      },
370 |      "execution_count": 37,
371 |      "metadata": {},
372 |      "output_type": "execute_result"
373 |     }
374 |    ],
375 |    "source": [
376 |     "# By squaring r_value we get the co-efficient of determination\n",
377 |     "r_value ** 2"
378 |    ]
379 |   },
380 |   {
381 |    "cell_type": "markdown",
382 |    "metadata": {},
383 |    "source": [
384 |     "A quick note here - what is the difference between the co-efficient of correlation, and the co-efficient of determination?<br>\n",
385 |     "It is very nicely explained here: http://blog.uwgb.edu/bansalg/statistics-data-analytics/linear-regression/what-is-the-difference-between-coefficient-of-determination-and-coefficient-of-correlation/ but essentially:<br>\n",
386 |     "- The co-efficient of correlation is a number between -1 (perfect negative correlation) and 1 (perfect positive correlation) which is indicative of how the 2 sets of values are correlated<br>\n",
387 |     "- The co-efficient of determination is a number between 0 and 1 (of course because it has been squared!) and is indicative of how good a fit the line is to the original data - the higher the better"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 34,
393 |    "metadata": {
394 |     "scrolled": true
395 |    },
396 |    "outputs": [
397 |     {
398 |      "data": {
399 |       "image/png": 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\n",
400 |       "text/plain": [
401 |        "<matplotlib.figure.Figure at 0x115c6e470>"
402 |       ]
403 |      },
404 |      "metadata": {},
405 |      "output_type": "display_data"
406 |     }
407 |    ],
408 |    "source": [
409 |     "# Here we create a little function which takes heights as inputs in order to predict corresponding weights values:\n",
410 |     "def get_weights(heights):\n",
411 |     "    return slope * heights + intercept\n",
412 |     "\n",
413 |     "plt.scatter(heights, weights)\n",
414 |     "plt.plot(heights, get_weights(heights), c='r')\n",
415 |     "plt.show()"
416 |    ]
417 |   }
418 |  ],
419 |  "metadata": {
420 |   "kernelspec": {
421 |    "display_name": "Python 3",
422 |    "language": "python",
423 |    "name": "python3"
424 |   },
425 |   "language_info": {
426 |    "codemirror_mode": {
427 |     "name": "ipython",
428 |     "version": 3
429 |    },
430 |    "file_extension": ".py",
431 |    "mimetype": "text/x-python",
432 |    "name": "python",
433 |    "nbconvert_exporter": "python",
434 |    "pygments_lexer": "ipython3",
435 |    "version": "3.6.3"
436 |   }
437 |  },
438 |  "nbformat": 4,
439 |  "nbformat_minor": 2
440 | }
441 | 


--------------------------------------------------------------------------------
/How it works - Pandas, data manipulation.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 2,
  15 |    "metadata": {},
  16 |    "outputs": [
  17 |     {
  18 |      "data": {
  19 |       "text/html": [
  20 |        "<div>\n",
  21 |        "<style scoped>\n",
  22 |        "    .dataframe tbody tr th:only-of-type {\n",
  23 |        "        vertical-align: middle;\n",
  24 |        "    }\n",
  25 |        "\n",
  26 |        "    .dataframe tbody tr th {\n",
  27 |        "        vertical-align: top;\n",
  28 |        "    }\n",
  29 |        "\n",
  30 |        "    .dataframe thead th {\n",
  31 |        "        text-align: right;\n",
  32 |        "    }\n",
  33 |        "</style>\n",
  34 |        "<table border=\"1\" class=\"dataframe\">\n",
  35 |        "  <thead>\n",
  36 |        "    <tr style=\"text-align: right;\">\n",
  37 |        "      <th></th>\n",
  38 |        "      <th>Make</th>\n",
  39 |        "      <th>Color</th>\n",
  40 |        "      <th>Capacity</th>\n",
  41 |        "    </tr>\n",
  42 |        "  </thead>\n",
  43 |        "  <tbody>\n",
  44 |        "    <tr>\n",
  45 |        "      <th>Jane</th>\n",
  46 |        "      <td>Ford</td>\n",
  47 |        "      <td>Blue</td>\n",
  48 |        "      <td>1.6l</td>\n",
  49 |        "    </tr>\n",
  50 |        "    <tr>\n",
  51 |        "      <th>John</th>\n",
  52 |        "      <td>BMW</td>\n",
  53 |        "      <td>Grey</td>\n",
  54 |        "      <td>2.0l</td>\n",
  55 |        "    </tr>\n",
  56 |        "    <tr>\n",
  57 |        "      <th>June</th>\n",
  58 |        "      <td>Mini</td>\n",
  59 |        "      <td>Red</td>\n",
  60 |        "      <td>1.6l</td>\n",
  61 |        "    </tr>\n",
  62 |        "    <tr>\n",
  63 |        "      <th>Jim</th>\n",
  64 |        "      <td>Mercedes</td>\n",
  65 |        "      <td>White</td>\n",
  66 |        "      <td>2.2l</td>\n",
  67 |        "    </tr>\n",
  68 |        "    <tr>\n",
  69 |        "      <th>Jay</th>\n",
  70 |        "      <td>Toyota</td>\n",
  71 |        "      <td>White</td>\n",
  72 |        "      <td>1.2l</td>\n",
  73 |        "    </tr>\n",
  74 |        "  </tbody>\n",
  75 |        "</table>\n",
  76 |        "</div>"
  77 |       ],
  78 |       "text/plain": [
  79 |        "          Make  Color Capacity\n",
  80 |        "Jane      Ford   Blue     1.6l\n",
  81 |        "John       BMW   Grey     2.0l\n",
  82 |        "June      Mini    Red     1.6l\n",
  83 |        "Jim   Mercedes  White     2.2l\n",
  84 |        "Jay     Toyota  White     1.2l"
  85 |       ]
  86 |      },
  87 |      "execution_count": 2,
  88 |      "metadata": {},
  89 |      "output_type": "execute_result"
  90 |     }
  91 |    ],
  92 |    "source": [
  93 |     "list1 = [\"Jane\", \"John\", \"June\", \"Jim\", \"Jay\"]\n",
  94 |     "list2 = [\"Ford\", \"BMW\", \"Mini\", \"Mercedes\", \"Toyota\"]\n",
  95 |     "list3 = [\"Blue\", \"Grey\", \"Red\", \"White\", \"White\"]\n",
  96 |     "list4 = [\"1.6l\", \"2.0l\", \"1.6l\", \"2.2l\", \"1.2l\"]\n",
  97 |     "df1 = pd.DataFrame({\"Make\":list2, \"Color\":list3, \"Capacity\":list4}, index = list1)\n",
  98 |     "df1"
  99 |    ]
 100 |   },
 101 |   {
 102 |    "cell_type": "markdown",
 103 |    "metadata": {},
 104 |    "source": [
 105 |     "## Updating values via indexation"
 106 |    ]
 107 |   },
 108 |   {
 109 |    "cell_type": "code",
 110 |    "execution_count": 3,
 111 |    "metadata": {},
 112 |    "outputs": [
 113 |     {
 114 |      "data": {
 115 |       "text/html": [
 116 |        "<div>\n",
 117 |        "<style scoped>\n",
 118 |        "    .dataframe tbody tr th:only-of-type {\n",
 119 |        "        vertical-align: middle;\n",
 120 |        "    }\n",
 121 |        "\n",
 122 |        "    .dataframe tbody tr th {\n",
 123 |        "        vertical-align: top;\n",
 124 |        "    }\n",
 125 |        "\n",
 126 |        "    .dataframe thead th {\n",
 127 |        "        text-align: right;\n",
 128 |        "    }\n",
 129 |        "</style>\n",
 130 |        "<table border=\"1\" class=\"dataframe\">\n",
 131 |        "  <thead>\n",
 132 |        "    <tr style=\"text-align: right;\">\n",
 133 |        "      <th></th>\n",
 134 |        "      <th>Capacity</th>\n",
 135 |        "      <th>Color</th>\n",
 136 |        "      <th>Make</th>\n",
 137 |        "    </tr>\n",
 138 |        "  </thead>\n",
 139 |        "  <tbody>\n",
 140 |        "    <tr>\n",
 141 |        "      <th>Jane</th>\n",
 142 |        "      <td>1.6l</td>\n",
 143 |        "      <td>Orange</td>\n",
 144 |        "      <td>Ford</td>\n",
 145 |        "    </tr>\n",
 146 |        "    <tr>\n",
 147 |        "      <th>John</th>\n",
 148 |        "      <td>2.0l</td>\n",
 149 |        "      <td>Grey</td>\n",
 150 |        "      <td>BMW</td>\n",
 151 |        "    </tr>\n",
 152 |        "    <tr>\n",
 153 |        "      <th>June</th>\n",
 154 |        "      <td>1.6l</td>\n",
 155 |        "      <td>Red</td>\n",
 156 |        "      <td>Mini</td>\n",
 157 |        "    </tr>\n",
 158 |        "    <tr>\n",
 159 |        "      <th>Jim</th>\n",
 160 |        "      <td>2.2l</td>\n",
 161 |        "      <td>White</td>\n",
 162 |        "      <td>Mercedes</td>\n",
 163 |        "    </tr>\n",
 164 |        "    <tr>\n",
 165 |        "      <th>Jay</th>\n",
 166 |        "      <td>1.2l</td>\n",
 167 |        "      <td>White</td>\n",
 168 |        "      <td>Toyota</td>\n",
 169 |        "    </tr>\n",
 170 |        "  </tbody>\n",
 171 |        "</table>\n",
 172 |        "</div>"
 173 |       ],
 174 |       "text/plain": [
 175 |        "     Capacity   Color      Make\n",
 176 |        "Jane     1.6l  Orange      Ford\n",
 177 |        "John     2.0l    Grey       BMW\n",
 178 |        "June     1.6l     Red      Mini\n",
 179 |        "Jim      2.2l   White  Mercedes\n",
 180 |        "Jay      1.2l   White    Toyota"
 181 |       ]
 182 |      },
 183 |      "execution_count": 3,
 184 |      "metadata": {},
 185 |      "output_type": "execute_result"
 186 |     }
 187 |    ],
 188 |    "source": [
 189 |     "df1.loc[\"Jane\", \"Color\"] = \"Orange\" # Jane gives us the rows to update, \n",
 190 |     "                                    # Color tells us which column to update\n",
 191 |     "df1"
 192 |    ]
 193 |   },
 194 |   {
 195 |    "cell_type": "code",
 196 |    "execution_count": 4,
 197 |    "metadata": {},
 198 |    "outputs": [
 199 |     {
 200 |      "data": {
 201 |       "text/html": [
 202 |        "<div>\n",
 203 |        "<style scoped>\n",
 204 |        "    .dataframe tbody tr th:only-of-type {\n",
 205 |        "        vertical-align: middle;\n",
 206 |        "    }\n",
 207 |        "\n",
 208 |        "    .dataframe tbody tr th {\n",
 209 |        "        vertical-align: top;\n",
 210 |        "    }\n",
 211 |        "\n",
 212 |        "    .dataframe thead th {\n",
 213 |        "        text-align: right;\n",
 214 |        "    }\n",
 215 |        "</style>\n",
 216 |        "<table border=\"1\" class=\"dataframe\">\n",
 217 |        "  <thead>\n",
 218 |        "    <tr style=\"text-align: right;\">\n",
 219 |        "      <th></th>\n",
 220 |        "      <th>Capacity</th>\n",
 221 |        "      <th>Color</th>\n",
 222 |        "      <th>Make</th>\n",
 223 |        "    </tr>\n",
 224 |        "  </thead>\n",
 225 |        "  <tbody>\n",
 226 |        "    <tr>\n",
 227 |        "      <th>Jane</th>\n",
 228 |        "      <td>1.6l</td>\n",
 229 |        "      <td>Orange</td>\n",
 230 |        "      <td>Ford</td>\n",
 231 |        "    </tr>\n",
 232 |        "    <tr>\n",
 233 |        "      <th>John</th>\n",
 234 |        "      <td>2.0l</td>\n",
 235 |        "      <td>Grey</td>\n",
 236 |        "      <td>BMW</td>\n",
 237 |        "    </tr>\n",
 238 |        "    <tr>\n",
 239 |        "      <th>June</th>\n",
 240 |        "      <td>1.6l</td>\n",
 241 |        "      <td>Red</td>\n",
 242 |        "      <td>Mini</td>\n",
 243 |        "    </tr>\n",
 244 |        "    <tr>\n",
 245 |        "      <th>Jim</th>\n",
 246 |        "      <td>2.2l</td>\n",
 247 |        "      <td>Off-White</td>\n",
 248 |        "      <td>Mercedes</td>\n",
 249 |        "    </tr>\n",
 250 |        "    <tr>\n",
 251 |        "      <th>Jay</th>\n",
 252 |        "      <td>1.2l</td>\n",
 253 |        "      <td>Off-White</td>\n",
 254 |        "      <td>Toyota</td>\n",
 255 |        "    </tr>\n",
 256 |        "  </tbody>\n",
 257 |        "</table>\n",
 258 |        "</div>"
 259 |       ],
 260 |       "text/plain": [
 261 |        "     Capacity      Color      Make\n",
 262 |        "Jane     1.6l     Orange      Ford\n",
 263 |        "John     2.0l       Grey       BMW\n",
 264 |        "June     1.6l        Red      Mini\n",
 265 |        "Jim      2.2l  Off-White  Mercedes\n",
 266 |        "Jay      1.2l  Off-White    Toyota"
 267 |       ]
 268 |      },
 269 |      "execution_count": 4,
 270 |      "metadata": {},
 271 |      "output_type": "execute_result"
 272 |     }
 273 |    ],
 274 |    "source": [
 275 |     "df1.loc[df1[\"Color\"].isin([\"White\"]), \"Color\"] = \"Off-White\" # The first part gives us the rows to update,\n",
 276 |     "                                                             # The second part tells us which column to update\n",
 277 |     "df1"
 278 |    ]
 279 |   },
 280 |   {
 281 |    "cell_type": "markdown",
 282 |    "metadata": {},
 283 |    "source": [
 284 |     "## Replacing values"
 285 |    ]
 286 |   },
 287 |   {
 288 |    "cell_type": "markdown",
 289 |    "metadata": {},
 290 |    "source": [
 291 |     "### An example across the entire dataframe, replacing entire cell values"
 292 |    ]
 293 |   },
 294 |   {
 295 |    "cell_type": "code",
 296 |    "execution_count": 5,
 297 |    "metadata": {},
 298 |    "outputs": [
 299 |     {
 300 |      "data": {
 301 |       "text/html": [
 302 |        "<div>\n",
 303 |        "<style scoped>\n",
 304 |        "    .dataframe tbody tr th:only-of-type {\n",
 305 |        "        vertical-align: middle;\n",
 306 |        "    }\n",
 307 |        "\n",
 308 |        "    .dataframe tbody tr th {\n",
 309 |        "        vertical-align: top;\n",
 310 |        "    }\n",
 311 |        "\n",
 312 |        "    .dataframe thead th {\n",
 313 |        "        text-align: right;\n",
 314 |        "    }\n",
 315 |        "</style>\n",
 316 |        "<table border=\"1\" class=\"dataframe\">\n",
 317 |        "  <thead>\n",
 318 |        "    <tr style=\"text-align: right;\">\n",
 319 |        "      <th></th>\n",
 320 |        "      <th>Capacity</th>\n",
 321 |        "      <th>Color</th>\n",
 322 |        "      <th>Make</th>\n",
 323 |        "    </tr>\n",
 324 |        "  </thead>\n",
 325 |        "  <tbody>\n",
 326 |        "    <tr>\n",
 327 |        "      <th>Jane</th>\n",
 328 |        "      <td>1.6l</td>\n",
 329 |        "      <td>Orange</td>\n",
 330 |        "      <td>Ford</td>\n",
 331 |        "    </tr>\n",
 332 |        "    <tr>\n",
 333 |        "      <th>John</th>\n",
 334 |        "      <td>2.0l</td>\n",
 335 |        "      <td>Silver</td>\n",
 336 |        "      <td>BMW</td>\n",
 337 |        "    </tr>\n",
 338 |        "    <tr>\n",
 339 |        "      <th>June</th>\n",
 340 |        "      <td>1.6l</td>\n",
 341 |        "      <td>Red</td>\n",
 342 |        "      <td>Mini</td>\n",
 343 |        "    </tr>\n",
 344 |        "    <tr>\n",
 345 |        "      <th>Jim</th>\n",
 346 |        "      <td>2.2l</td>\n",
 347 |        "      <td>White</td>\n",
 348 |        "      <td>Mercedes</td>\n",
 349 |        "    </tr>\n",
 350 |        "    <tr>\n",
 351 |        "      <th>Jay</th>\n",
 352 |        "      <td>1.2l</td>\n",
 353 |        "      <td>White</td>\n",
 354 |        "      <td>Toyota</td>\n",
 355 |        "    </tr>\n",
 356 |        "  </tbody>\n",
 357 |        "</table>\n",
 358 |        "</div>"
 359 |       ],
 360 |       "text/plain": [
 361 |        "     Capacity   Color      Make\n",
 362 |        "Jane     1.6l  Orange      Ford\n",
 363 |        "John     2.0l  Silver       BMW\n",
 364 |        "June     1.6l     Red      Mini\n",
 365 |        "Jim      2.2l   White  Mercedes\n",
 366 |        "Jay      1.2l   White    Toyota"
 367 |       ]
 368 |      },
 369 |      "execution_count": 5,
 370 |      "metadata": {},
 371 |      "output_type": "execute_result"
 372 |     }
 373 |    ],
 374 |    "source": [
 375 |     "df1.replace([\"Off-White\", \"Grey\"], [\"White\", \"Silver\"], inplace = True)\n",
 376 |     "df1"
 377 |    ]
 378 |   },
 379 |   {
 380 |    "cell_type": "markdown",
 381 |    "metadata": {},
 382 |    "source": [
 383 |     "### An example for a single series, replacing partial cell values"
 384 |    ]
 385 |   },
 386 |   {
 387 |    "cell_type": "code",
 388 |    "execution_count": 6,
 389 |    "metadata": {},
 390 |    "outputs": [
 391 |     {
 392 |      "data": {
 393 |       "text/html": [
 394 |        "<div>\n",
 395 |        "<style scoped>\n",
 396 |        "    .dataframe tbody tr th:only-of-type {\n",
 397 |        "        vertical-align: middle;\n",
 398 |        "    }\n",
 399 |        "\n",
 400 |        "    .dataframe tbody tr th {\n",
 401 |        "        vertical-align: top;\n",
 402 |        "    }\n",
 403 |        "\n",
 404 |        "    .dataframe thead th {\n",
 405 |        "        text-align: right;\n",
 406 |        "    }\n",
 407 |        "</style>\n",
 408 |        "<table border=\"1\" class=\"dataframe\">\n",
 409 |        "  <thead>\n",
 410 |        "    <tr style=\"text-align: right;\">\n",
 411 |        "      <th></th>\n",
 412 |        "      <th>Capacity</th>\n",
 413 |        "      <th>Color</th>\n",
 414 |        "      <th>Make</th>\n",
 415 |        "    </tr>\n",
 416 |        "  </thead>\n",
 417 |        "  <tbody>\n",
 418 |        "    <tr>\n",
 419 |        "      <th>Jane</th>\n",
 420 |        "      <td>1.6</td>\n",
 421 |        "      <td>Orange</td>\n",
 422 |        "      <td>Ford</td>\n",
 423 |        "    </tr>\n",
 424 |        "    <tr>\n",
 425 |        "      <th>John</th>\n",
 426 |        "      <td>2.0</td>\n",
 427 |        "      <td>Silver</td>\n",
 428 |        "      <td>BMW</td>\n",
 429 |        "    </tr>\n",
 430 |        "    <tr>\n",
 431 |        "      <th>June</th>\n",
 432 |        "      <td>1.6</td>\n",
 433 |        "      <td>Red</td>\n",
 434 |        "      <td>Mini</td>\n",
 435 |        "    </tr>\n",
 436 |        "    <tr>\n",
 437 |        "      <th>Jim</th>\n",
 438 |        "      <td>2.2</td>\n",
 439 |        "      <td>White</td>\n",
 440 |        "      <td>Mercedes</td>\n",
 441 |        "    </tr>\n",
 442 |        "    <tr>\n",
 443 |        "      <th>Jay</th>\n",
 444 |        "      <td>1.2</td>\n",
 445 |        "      <td>White</td>\n",
 446 |        "      <td>Toyota</td>\n",
 447 |        "    </tr>\n",
 448 |        "  </tbody>\n",
 449 |        "</table>\n",
 450 |        "</div>"
 451 |       ],
 452 |       "text/plain": [
 453 |        "     Capacity   Color      Make\n",
 454 |        "Jane      1.6  Orange      Ford\n",
 455 |        "John      2.0  Silver       BMW\n",
 456 |        "June      1.6     Red      Mini\n",
 457 |        "Jim       2.2   White  Mercedes\n",
 458 |        "Jay       1.2   White    Toyota"
 459 |       ]
 460 |      },
 461 |      "execution_count": 6,
 462 |      "metadata": {},
 463 |      "output_type": "execute_result"
 464 |     }
 465 |    ],
 466 |    "source": [
 467 |     "df1[\"Capacity\"].replace({'l':''}, inplace = True, regex=True)\n",
 468 |     "df1"
 469 |    ]
 470 |   },
 471 |   {
 472 |    "cell_type": "markdown",
 473 |    "metadata": {},
 474 |    "source": [
 475 |     "## Changing the type of a series"
 476 |    ]
 477 |   },
 478 |   {
 479 |    "cell_type": "markdown",
 480 |    "metadata": {},
 481 |    "source": [
 482 |     "### Using astype()"
 483 |    ]
 484 |   },
 485 |   {
 486 |    "cell_type": "code",
 487 |    "execution_count": 7,
 488 |    "metadata": {},
 489 |    "outputs": [
 490 |     {
 491 |      "name": "stdout",
 492 |      "output_type": "stream",
 493 |      "text": [
 494 |       "<class 'pandas.core.frame.DataFrame'>\n",
 495 |       "Index: 5 entries, Jane to Jay\n",
 496 |       "Data columns (total 3 columns):\n",
 497 |       "Capacity    5 non-null object\n",
 498 |       "Color       5 non-null object\n",
 499 |       "Make        5 non-null object\n",
 500 |       "dtypes: object(3)\n",
 501 |       "memory usage: 320.0+ bytes\n"
 502 |      ]
 503 |     }
 504 |    ],
 505 |    "source": [
 506 |     "df1.info() # All 3 series currently classified as \"object\" aka \"string\""
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "code",
 511 |    "execution_count": 8,
 512 |    "metadata": {},
 513 |    "outputs": [
 514 |     {
 515 |      "name": "stdout",
 516 |      "output_type": "stream",
 517 |      "text": [
 518 |       "<class 'pandas.core.frame.DataFrame'>\n",
 519 |       "Index: 5 entries, Jane to Jay\n",
 520 |       "Data columns (total 3 columns):\n",
 521 |       "Capacity    5 non-null float64\n",
 522 |       "Color       5 non-null category\n",
 523 |       "Make        5 non-null object\n",
 524 |       "dtypes: category(1), float64(1), object(1)\n",
 525 |       "memory usage: 477.0+ bytes\n"
 526 |      ]
 527 |     }
 528 |    ],
 529 |    "source": [
 530 |     "df1[\"Color\"] = df1[\"Color\"].astype(\"category\")    # Convert color to a categorical variable\n",
 531 |     "df1[\"Capacity\"] = df1[\"Capacity\"].astype(\"float\") # Convert capacity to a float\n",
 532 |     "\n",
 533 |     "df1.info() # Here we see the results of the updates - the data didn't change\n",
 534 |     "           # but the format did, so that now we can e.g. perform calcs on Capacity\n",
 535 |     "           # which we could not have done while it was classified as an Object"
 536 |    ]
 537 |   },
 538 |   {
 539 |    "cell_type": "markdown",
 540 |    "metadata": {},
 541 |    "source": [
 542 |     "### Using pd.to_numeric()"
 543 |    ]
 544 |   },
 545 |   {
 546 |    "cell_type": "code",
 547 |    "execution_count": 9,
 548 |    "metadata": {},
 549 |    "outputs": [
 550 |     {
 551 |      "data": {
 552 |       "text/html": [
 553 |        "<div>\n",
 554 |        "<style scoped>\n",
 555 |        "    .dataframe tbody tr th:only-of-type {\n",
 556 |        "        vertical-align: middle;\n",
 557 |        "    }\n",
 558 |        "\n",
 559 |        "    .dataframe tbody tr th {\n",
 560 |        "        vertical-align: top;\n",
 561 |        "    }\n",
 562 |        "\n",
 563 |        "    .dataframe thead th {\n",
 564 |        "        text-align: right;\n",
 565 |        "    }\n",
 566 |        "</style>\n",
 567 |        "<table border=\"1\" class=\"dataframe\">\n",
 568 |        "  <thead>\n",
 569 |        "    <tr style=\"text-align: right;\">\n",
 570 |        "      <th></th>\n",
 571 |        "      <th>Capacity</th>\n",
 572 |        "      <th>Color</th>\n",
 573 |        "      <th>Make</th>\n",
 574 |        "    </tr>\n",
 575 |        "  </thead>\n",
 576 |        "  <tbody>\n",
 577 |        "    <tr>\n",
 578 |        "      <th>Jane</th>\n",
 579 |        "      <td>1.6l</td>\n",
 580 |        "      <td>Orange</td>\n",
 581 |        "      <td>Ford</td>\n",
 582 |        "    </tr>\n",
 583 |        "    <tr>\n",
 584 |        "      <th>John</th>\n",
 585 |        "      <td>2</td>\n",
 586 |        "      <td>Silver</td>\n",
 587 |        "      <td>BMW</td>\n",
 588 |        "    </tr>\n",
 589 |        "    <tr>\n",
 590 |        "      <th>June</th>\n",
 591 |        "      <td>1.6</td>\n",
 592 |        "      <td>Red</td>\n",
 593 |        "      <td>Mini</td>\n",
 594 |        "    </tr>\n",
 595 |        "    <tr>\n",
 596 |        "      <th>Jim</th>\n",
 597 |        "      <td>2.2</td>\n",
 598 |        "      <td>White</td>\n",
 599 |        "      <td>Mercedes</td>\n",
 600 |        "    </tr>\n",
 601 |        "    <tr>\n",
 602 |        "      <th>Jay</th>\n",
 603 |        "      <td>1.2</td>\n",
 604 |        "      <td>White</td>\n",
 605 |        "      <td>Toyota</td>\n",
 606 |        "    </tr>\n",
 607 |        "  </tbody>\n",
 608 |        "</table>\n",
 609 |        "</div>"
 610 |       ],
 611 |       "text/plain": [
 612 |        "     Capacity   Color      Make\n",
 613 |        "Jane     1.6l  Orange      Ford\n",
 614 |        "John        2  Silver       BMW\n",
 615 |        "June      1.6     Red      Mini\n",
 616 |        "Jim       2.2   White  Mercedes\n",
 617 |        "Jay       1.2   White    Toyota"
 618 |       ]
 619 |      },
 620 |      "execution_count": 9,
 621 |      "metadata": {},
 622 |      "output_type": "execute_result"
 623 |     }
 624 |    ],
 625 |    "source": [
 626 |     "df1[\"Capacity\"] = df1[\"Capacity\"].astype(\"object\") # Now let's put Capacity back\n",
 627 |     "df1.loc[\"Jane\", \"Capacity\"] = \"1.6l\"               # And then introduce a non-numeric value in one field\n",
 628 |     "df1"
 629 |    ]
 630 |   },
 631 |   {
 632 |    "cell_type": "code",
 633 |    "execution_count": null,
 634 |    "metadata": {},
 635 |    "outputs": [],
 636 |    "source": [
 637 |     "df1[\"Capacity\"] = df1[\"Capacity\"].astype(\"float\") # THIS script will now end in an error\n",
 638 |     "                                                  # because the l in 1.6l can't be converted "
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "code",
 643 |    "execution_count": 10,
 644 |    "metadata": {},
 645 |    "outputs": [
 646 |     {
 647 |      "data": {
 648 |       "text/html": [
 649 |        "<div>\n",
 650 |        "<style scoped>\n",
 651 |        "    .dataframe tbody tr th:only-of-type {\n",
 652 |        "        vertical-align: middle;\n",
 653 |        "    }\n",
 654 |        "\n",
 655 |        "    .dataframe tbody tr th {\n",
 656 |        "        vertical-align: top;\n",
 657 |        "    }\n",
 658 |        "\n",
 659 |        "    .dataframe thead th {\n",
 660 |        "        text-align: right;\n",
 661 |        "    }\n",
 662 |        "</style>\n",
 663 |        "<table border=\"1\" class=\"dataframe\">\n",
 664 |        "  <thead>\n",
 665 |        "    <tr style=\"text-align: right;\">\n",
 666 |        "      <th></th>\n",
 667 |        "      <th>Capacity</th>\n",
 668 |        "      <th>Color</th>\n",
 669 |        "      <th>Make</th>\n",
 670 |        "    </tr>\n",
 671 |        "  </thead>\n",
 672 |        "  <tbody>\n",
 673 |        "    <tr>\n",
 674 |        "      <th>Jane</th>\n",
 675 |        "      <td>NaN</td>\n",
 676 |        "      <td>Orange</td>\n",
 677 |        "      <td>Ford</td>\n",
 678 |        "    </tr>\n",
 679 |        "    <tr>\n",
 680 |        "      <th>John</th>\n",
 681 |        "      <td>2.0</td>\n",
 682 |        "      <td>Silver</td>\n",
 683 |        "      <td>BMW</td>\n",
 684 |        "    </tr>\n",
 685 |        "    <tr>\n",
 686 |        "      <th>June</th>\n",
 687 |        "      <td>1.6</td>\n",
 688 |        "      <td>Red</td>\n",
 689 |        "      <td>Mini</td>\n",
 690 |        "    </tr>\n",
 691 |        "    <tr>\n",
 692 |        "      <th>Jim</th>\n",
 693 |        "      <td>2.2</td>\n",
 694 |        "      <td>White</td>\n",
 695 |        "      <td>Mercedes</td>\n",
 696 |        "    </tr>\n",
 697 |        "    <tr>\n",
 698 |        "      <th>Jay</th>\n",
 699 |        "      <td>1.2</td>\n",
 700 |        "      <td>White</td>\n",
 701 |        "      <td>Toyota</td>\n",
 702 |        "    </tr>\n",
 703 |        "  </tbody>\n",
 704 |        "</table>\n",
 705 |        "</div>"
 706 |       ],
 707 |       "text/plain": [
 708 |        "      Capacity   Color      Make\n",
 709 |        "Jane       NaN  Orange      Ford\n",
 710 |        "John       2.0  Silver       BMW\n",
 711 |        "June       1.6     Red      Mini\n",
 712 |        "Jim        2.2   White  Mercedes\n",
 713 |        "Jay        1.2   White    Toyota"
 714 |       ]
 715 |      },
 716 |      "execution_count": 10,
 717 |      "metadata": {},
 718 |      "output_type": "execute_result"
 719 |     }
 720 |    ],
 721 |    "source": [
 722 |     "df1[\"Capacity\"] = pd.to_numeric(df1[\"Capacity\"], errors = \"coerce\")\n",
 723 |     "df1\n",
 724 |     "# This is an alternative method to convert data types - the argument errors = \"coerce\" \n",
 725 |     "# is pretty handy if your data has some exceptions in it, fills NaN where no conversion is possible\n",
 726 |     "# pd.to_datetime() also has errors = \"coerce\" which can be useful"
 727 |    ]
 728 |   },
 729 |   {
 730 |    "cell_type": "markdown",
 731 |    "metadata": {},
 732 |    "source": [
 733 |     "## Dealing with null values"
 734 |    ]
 735 |   },
 736 |   {
 737 |    "cell_type": "markdown",
 738 |    "metadata": {},
 739 |    "source": [
 740 |     "### Filling with another default value"
 741 |    ]
 742 |   },
 743 |   {
 744 |    "cell_type": "code",
 745 |    "execution_count": 11,
 746 |    "metadata": {},
 747 |    "outputs": [
 748 |     {
 749 |      "data": {
 750 |       "text/html": [
 751 |        "<div>\n",
 752 |        "<style scoped>\n",
 753 |        "    .dataframe tbody tr th:only-of-type {\n",
 754 |        "        vertical-align: middle;\n",
 755 |        "    }\n",
 756 |        "\n",
 757 |        "    .dataframe tbody tr th {\n",
 758 |        "        vertical-align: top;\n",
 759 |        "    }\n",
 760 |        "\n",
 761 |        "    .dataframe thead th {\n",
 762 |        "        text-align: right;\n",
 763 |        "    }\n",
 764 |        "</style>\n",
 765 |        "<table border=\"1\" class=\"dataframe\">\n",
 766 |        "  <thead>\n",
 767 |        "    <tr style=\"text-align: right;\">\n",
 768 |        "      <th></th>\n",
 769 |        "      <th>Capacity</th>\n",
 770 |        "      <th>Color</th>\n",
 771 |        "      <th>Make</th>\n",
 772 |        "    </tr>\n",
 773 |        "  </thead>\n",
 774 |        "  <tbody>\n",
 775 |        "    <tr>\n",
 776 |        "      <th>Jane</th>\n",
 777 |        "      <td>0.0</td>\n",
 778 |        "      <td>Orange</td>\n",
 779 |        "      <td>Ford</td>\n",
 780 |        "    </tr>\n",
 781 |        "    <tr>\n",
 782 |        "      <th>John</th>\n",
 783 |        "      <td>2.0</td>\n",
 784 |        "      <td>Silver</td>\n",
 785 |        "      <td>BMW</td>\n",
 786 |        "    </tr>\n",
 787 |        "    <tr>\n",
 788 |        "      <th>June</th>\n",
 789 |        "      <td>1.6</td>\n",
 790 |        "      <td>Red</td>\n",
 791 |        "      <td>Mini</td>\n",
 792 |        "    </tr>\n",
 793 |        "    <tr>\n",
 794 |        "      <th>Jim</th>\n",
 795 |        "      <td>2.2</td>\n",
 796 |        "      <td>White</td>\n",
 797 |        "      <td>Mercedes</td>\n",
 798 |        "    </tr>\n",
 799 |        "    <tr>\n",
 800 |        "      <th>Jay</th>\n",
 801 |        "      <td>1.2</td>\n",
 802 |        "      <td>White</td>\n",
 803 |        "      <td>Toyota</td>\n",
 804 |        "    </tr>\n",
 805 |        "  </tbody>\n",
 806 |        "</table>\n",
 807 |        "</div>"
 808 |       ],
 809 |       "text/plain": [
 810 |        "      Capacity   Color      Make\n",
 811 |        "Jane       0.0  Orange      Ford\n",
 812 |        "John       2.0  Silver       BMW\n",
 813 |        "June       1.6     Red      Mini\n",
 814 |        "Jim        2.2   White  Mercedes\n",
 815 |        "Jay        1.2   White    Toyota"
 816 |       ]
 817 |      },
 818 |      "execution_count": 11,
 819 |      "metadata": {},
 820 |      "output_type": "execute_result"
 821 |     }
 822 |    ],
 823 |    "source": [
 824 |     "df1[\"Capacity\"].fillna(0, inplace = True) # Fills the NaN values in the series with the specified value\n",
 825 |     "                                          # or indeed the entire dataframe (which would seldom make sense!)\n",
 826 |     "df1"
 827 |    ]
 828 |   },
 829 |   {
 830 |    "cell_type": "markdown",
 831 |    "metadata": {},
 832 |    "source": [
 833 |     "### Removing rows with NaN"
 834 |    ]
 835 |   },
 836 |   {
 837 |    "cell_type": "code",
 838 |    "execution_count": 12,
 839 |    "metadata": {},
 840 |    "outputs": [],
 841 |    "source": [
 842 |     "# Here are some additional ways to deal with them:\n",
 843 |     "# df.dropna()                          Removes ALL rows in the entire dataframe with \n",
 844 |     "#                                      one or more null values\n",
 845 |     "# df.dropna(how = 'all'                Removes only rows where all columns contain null values\n",
 846 |     "# df.dropna(subset = [“Column name”])  Removes rows only where there is a null value in the \n",
 847 |     "#                                      specified column name"
 848 |    ]
 849 |   },
 850 |   {
 851 |    "cell_type": "markdown",
 852 |    "metadata": {},
 853 |    "source": [
 854 |     "## Altering the shape of the dataframe"
 855 |    ]
 856 |   },
 857 |   {
 858 |    "cell_type": "markdown",
 859 |    "metadata": {},
 860 |    "source": [
 861 |     "### Adding columns quickly"
 862 |    ]
 863 |   },
 864 |   {
 865 |    "cell_type": "code",
 866 |    "execution_count": 13,
 867 |    "metadata": {},
 868 |    "outputs": [
 869 |     {
 870 |      "data": {
 871 |       "text/html": [
 872 |        "<div>\n",
 873 |        "<style scoped>\n",
 874 |        "    .dataframe tbody tr th:only-of-type {\n",
 875 |        "        vertical-align: middle;\n",
 876 |        "    }\n",
 877 |        "\n",
 878 |        "    .dataframe tbody tr th {\n",
 879 |        "        vertical-align: top;\n",
 880 |        "    }\n",
 881 |        "\n",
 882 |        "    .dataframe thead th {\n",
 883 |        "        text-align: right;\n",
 884 |        "    }\n",
 885 |        "</style>\n",
 886 |        "<table border=\"1\" class=\"dataframe\">\n",
 887 |        "  <thead>\n",
 888 |        "    <tr style=\"text-align: right;\">\n",
 889 |        "      <th></th>\n",
 890 |        "      <th>Capacity</th>\n",
 891 |        "      <th>Color</th>\n",
 892 |        "      <th>Make</th>\n",
 893 |        "      <th>Model</th>\n",
 894 |        "    </tr>\n",
 895 |        "  </thead>\n",
 896 |        "  <tbody>\n",
 897 |        "    <tr>\n",
 898 |        "      <th>Jane</th>\n",
 899 |        "      <td>0.0</td>\n",
 900 |        "      <td>Orange</td>\n",
 901 |        "      <td>Ford</td>\n",
 902 |        "      <td></td>\n",
 903 |        "    </tr>\n",
 904 |        "    <tr>\n",
 905 |        "      <th>John</th>\n",
 906 |        "      <td>2.0</td>\n",
 907 |        "      <td>Silver</td>\n",
 908 |        "      <td>BMW</td>\n",
 909 |        "      <td></td>\n",
 910 |        "    </tr>\n",
 911 |        "    <tr>\n",
 912 |        "      <th>June</th>\n",
 913 |        "      <td>1.6</td>\n",
 914 |        "      <td>Red</td>\n",
 915 |        "      <td>Mini</td>\n",
 916 |        "      <td></td>\n",
 917 |        "    </tr>\n",
 918 |        "    <tr>\n",
 919 |        "      <th>Jim</th>\n",
 920 |        "      <td>2.2</td>\n",
 921 |        "      <td>White</td>\n",
 922 |        "      <td>Mercedes</td>\n",
 923 |        "      <td></td>\n",
 924 |        "    </tr>\n",
 925 |        "    <tr>\n",
 926 |        "      <th>Jay</th>\n",
 927 |        "      <td>1.2</td>\n",
 928 |        "      <td>White</td>\n",
 929 |        "      <td>Toyota</td>\n",
 930 |        "      <td></td>\n",
 931 |        "    </tr>\n",
 932 |        "  </tbody>\n",
 933 |        "</table>\n",
 934 |        "</div>"
 935 |       ],
 936 |       "text/plain": [
 937 |        "      Capacity   Color      Make Model\n",
 938 |        "Jane       0.0  Orange      Ford      \n",
 939 |        "John       2.0  Silver       BMW      \n",
 940 |        "June       1.6     Red      Mini      \n",
 941 |        "Jim        2.2   White  Mercedes      \n",
 942 |        "Jay        1.2   White    Toyota      "
 943 |       ]
 944 |      },
 945 |      "execution_count": 13,
 946 |      "metadata": {},
 947 |      "output_type": "execute_result"
 948 |     }
 949 |    ],
 950 |    "source": [
 951 |     "df1[\"Model\"] = df[\"\"]\n",
 952 |     "df1"
 953 |    ]
 954 |   },
 955 |   {
 956 |    "cell_type": "markdown",
 957 |    "metadata": {},
 958 |    "source": [
 959 |     "### Adding columns at a specified location"
 960 |    ]
 961 |   },
 962 |   {
 963 |    "cell_type": "code",
 964 |    "execution_count": 14,
 965 |    "metadata": {},
 966 |    "outputs": [
 967 |     {
 968 |      "data": {
 969 |       "text/html": [
 970 |        "<div>\n",
 971 |        "<style scoped>\n",
 972 |        "    .dataframe tbody tr th:only-of-type {\n",
 973 |        "        vertical-align: middle;\n",
 974 |        "    }\n",
 975 |        "\n",
 976 |        "    .dataframe tbody tr th {\n",
 977 |        "        vertical-align: top;\n",
 978 |        "    }\n",
 979 |        "\n",
 980 |        "    .dataframe thead th {\n",
 981 |        "        text-align: right;\n",
 982 |        "    }\n",
 983 |        "</style>\n",
 984 |        "<table border=\"1\" class=\"dataframe\">\n",
 985 |        "  <thead>\n",
 986 |        "    <tr style=\"text-align: right;\">\n",
 987 |        "      <th></th>\n",
 988 |        "      <th>Capacity</th>\n",
 989 |        "      <th>Service Interval</th>\n",
 990 |        "      <th>Color</th>\n",
 991 |        "      <th>Make</th>\n",
 992 |        "      <th>Model</th>\n",
 993 |        "    </tr>\n",
 994 |        "  </thead>\n",
 995 |        "  <tbody>\n",
 996 |        "    <tr>\n",
 997 |        "      <th>Jane</th>\n",
 998 |        "      <td>0.0</td>\n",
 999 |        "      <td>20000km</td>\n",
1000 |        "      <td>Orange</td>\n",
1001 |        "      <td>Ford</td>\n",
1002 |        "      <td></td>\n",
1003 |        "    </tr>\n",
1004 |        "    <tr>\n",
1005 |        "      <th>John</th>\n",
1006 |        "      <td>2.0</td>\n",
1007 |        "      <td>20000km</td>\n",
1008 |        "      <td>Silver</td>\n",
1009 |        "      <td>BMW</td>\n",
1010 |        "      <td></td>\n",
1011 |        "    </tr>\n",
1012 |        "    <tr>\n",
1013 |        "      <th>June</th>\n",
1014 |        "      <td>1.6</td>\n",
1015 |        "      <td>20000km</td>\n",
1016 |        "      <td>Red</td>\n",
1017 |        "      <td>Mini</td>\n",
1018 |        "      <td></td>\n",
1019 |        "    </tr>\n",
1020 |        "    <tr>\n",
1021 |        "      <th>Jim</th>\n",
1022 |        "      <td>2.2</td>\n",
1023 |        "      <td>20000km</td>\n",
1024 |        "      <td>White</td>\n",
1025 |        "      <td>Mercedes</td>\n",
1026 |        "      <td></td>\n",
1027 |        "    </tr>\n",
1028 |        "    <tr>\n",
1029 |        "      <th>Jay</th>\n",
1030 |        "      <td>1.2</td>\n",
1031 |        "      <td>20000km</td>\n",
1032 |        "      <td>White</td>\n",
1033 |        "      <td>Toyota</td>\n",
1034 |        "      <td></td>\n",
1035 |        "    </tr>\n",
1036 |        "  </tbody>\n",
1037 |        "</table>\n",
1038 |        "</div>"
1039 |       ],
1040 |       "text/plain": [
1041 |        "      Capacity Service Interval   Color      Make Model\n",
1042 |        "Jane       0.0          20000km  Orange      Ford      \n",
1043 |        "John       2.0          20000km  Silver       BMW      \n",
1044 |        "June       1.6          20000km     Red      Mini      \n",
1045 |        "Jim        2.2          20000km   White  Mercedes      \n",
1046 |        "Jay        1.2          20000km   White    Toyota      "
1047 |       ]
1048 |      },
1049 |      "execution_count": 14,
1050 |      "metadata": {},
1051 |      "output_type": "execute_result"
1052 |     }
1053 |    ],
1054 |    "source": [
1055 |     "df1.insert(1, \"Service Interval\", \"20000km\")\n",
1056 |     "df1"
1057 |    ]
1058 |   },
1059 |   {
1060 |    "cell_type": "markdown",
1061 |    "metadata": {},
1062 |    "source": [
1063 |     "### Adding rows"
1064 |    ]
1065 |   },
1066 |   {
1067 |    "cell_type": "code",
1068 |    "execution_count": 15,
1069 |    "metadata": {},
1070 |    "outputs": [
1071 |     {
1072 |      "data": {
1073 |       "text/html": [
1074 |        "<div>\n",
1075 |        "<style scoped>\n",
1076 |        "    .dataframe tbody tr th:only-of-type {\n",
1077 |        "        vertical-align: middle;\n",
1078 |        "    }\n",
1079 |        "\n",
1080 |        "    .dataframe tbody tr th {\n",
1081 |        "        vertical-align: top;\n",
1082 |        "    }\n",
1083 |        "\n",
1084 |        "    .dataframe thead th {\n",
1085 |        "        text-align: right;\n",
1086 |        "    }\n",
1087 |        "</style>\n",
1088 |        "<table border=\"1\" class=\"dataframe\">\n",
1089 |        "  <thead>\n",
1090 |        "    <tr style=\"text-align: right;\">\n",
1091 |        "      <th></th>\n",
1092 |        "      <th>Capacity</th>\n",
1093 |        "      <th>Service Interval</th>\n",
1094 |        "      <th>Color</th>\n",
1095 |        "      <th>Make</th>\n",
1096 |        "      <th>Model</th>\n",
1097 |        "    </tr>\n",
1098 |        "  </thead>\n",
1099 |        "  <tbody>\n",
1100 |        "    <tr>\n",
1101 |        "      <th>Jane</th>\n",
1102 |        "      <td>0.0</td>\n",
1103 |        "      <td>20000km</td>\n",
1104 |        "      <td>Orange</td>\n",
1105 |        "      <td>Ford</td>\n",
1106 |        "      <td></td>\n",
1107 |        "    </tr>\n",
1108 |        "    <tr>\n",
1109 |        "      <th>John</th>\n",
1110 |        "      <td>2.0</td>\n",
1111 |        "      <td>20000km</td>\n",
1112 |        "      <td>Silver</td>\n",
1113 |        "      <td>BMW</td>\n",
1114 |        "      <td></td>\n",
1115 |        "    </tr>\n",
1116 |        "    <tr>\n",
1117 |        "      <th>June</th>\n",
1118 |        "      <td>1.6</td>\n",
1119 |        "      <td>20000km</td>\n",
1120 |        "      <td>Red</td>\n",
1121 |        "      <td>Mini</td>\n",
1122 |        "      <td></td>\n",
1123 |        "    </tr>\n",
1124 |        "    <tr>\n",
1125 |        "      <th>Jim</th>\n",
1126 |        "      <td>2.2</td>\n",
1127 |        "      <td>20000km</td>\n",
1128 |        "      <td>White</td>\n",
1129 |        "      <td>Mercedes</td>\n",
1130 |        "      <td></td>\n",
1131 |        "    </tr>\n",
1132 |        "    <tr>\n",
1133 |        "      <th>Jay</th>\n",
1134 |        "      <td>1.2</td>\n",
1135 |        "      <td>20000km</td>\n",
1136 |        "      <td>White</td>\n",
1137 |        "      <td>Toyota</td>\n",
1138 |        "      <td></td>\n",
1139 |        "    </tr>\n",
1140 |        "    <tr>\n",
1141 |        "      <th>James</th>\n",
1142 |        "      <td>1.6</td>\n",
1143 |        "      <td>NaN</td>\n",
1144 |        "      <td>Blue</td>\n",
1145 |        "      <td>Honda</td>\n",
1146 |        "      <td>Jazz</td>\n",
1147 |        "    </tr>\n",
1148 |        "  </tbody>\n",
1149 |        "</table>\n",
1150 |        "</div>"
1151 |       ],
1152 |       "text/plain": [
1153 |        "       Capacity Service Interval   Color      Make Model\n",
1154 |        "Jane        0.0          20000km  Orange      Ford      \n",
1155 |        "John        2.0          20000km  Silver       BMW      \n",
1156 |        "June        1.6          20000km     Red      Mini      \n",
1157 |        "Jim         2.2          20000km   White  Mercedes      \n",
1158 |        "Jay         1.2          20000km   White    Toyota      \n",
1159 |        "James       1.6              NaN    Blue     Honda  Jazz"
1160 |       ]
1161 |      },
1162 |      "execution_count": 15,
1163 |      "metadata": {},
1164 |      "output_type": "execute_result"
1165 |     }
1166 |    ],
1167 |    "source": [
1168 |     "extra_row = pd.Series({\"Capacity\": 1.6, \"Color\": \"Blue\", \"Make\": \"Honda\", \"Model\": \"Jazz\"})\n",
1169 |     "extra_row.name = \"James\"\n",
1170 |     "df1 = df1.append(extra_row)\n",
1171 |     "df1"
1172 |    ]
1173 |   },
1174 |   {
1175 |    "cell_type": "markdown",
1176 |    "metadata": {},
1177 |    "source": [
1178 |     "### Removing columns"
1179 |    ]
1180 |   },
1181 |   {
1182 |    "cell_type": "code",
1183 |    "execution_count": 16,
1184 |    "metadata": {},
1185 |    "outputs": [
1186 |     {
1187 |      "data": {
1188 |       "text/html": [
1189 |        "<div>\n",
1190 |        "<style scoped>\n",
1191 |        "    .dataframe tbody tr th:only-of-type {\n",
1192 |        "        vertical-align: middle;\n",
1193 |        "    }\n",
1194 |        "\n",
1195 |        "    .dataframe tbody tr th {\n",
1196 |        "        vertical-align: top;\n",
1197 |        "    }\n",
1198 |        "\n",
1199 |        "    .dataframe thead th {\n",
1200 |        "        text-align: right;\n",
1201 |        "    }\n",
1202 |        "</style>\n",
1203 |        "<table border=\"1\" class=\"dataframe\">\n",
1204 |        "  <thead>\n",
1205 |        "    <tr style=\"text-align: right;\">\n",
1206 |        "      <th></th>\n",
1207 |        "      <th>Capacity</th>\n",
1208 |        "      <th>Color</th>\n",
1209 |        "      <th>Make</th>\n",
1210 |        "      <th>Model</th>\n",
1211 |        "    </tr>\n",
1212 |        "  </thead>\n",
1213 |        "  <tbody>\n",
1214 |        "    <tr>\n",
1215 |        "      <th>Jane</th>\n",
1216 |        "      <td>0.0</td>\n",
1217 |        "      <td>Orange</td>\n",
1218 |        "      <td>Ford</td>\n",
1219 |        "      <td></td>\n",
1220 |        "    </tr>\n",
1221 |        "    <tr>\n",
1222 |        "      <th>John</th>\n",
1223 |        "      <td>2.0</td>\n",
1224 |        "      <td>Silver</td>\n",
1225 |        "      <td>BMW</td>\n",
1226 |        "      <td></td>\n",
1227 |        "    </tr>\n",
1228 |        "    <tr>\n",
1229 |        "      <th>June</th>\n",
1230 |        "      <td>1.6</td>\n",
1231 |        "      <td>Red</td>\n",
1232 |        "      <td>Mini</td>\n",
1233 |        "      <td></td>\n",
1234 |        "    </tr>\n",
1235 |        "    <tr>\n",
1236 |        "      <th>Jim</th>\n",
1237 |        "      <td>2.2</td>\n",
1238 |        "      <td>White</td>\n",
1239 |        "      <td>Mercedes</td>\n",
1240 |        "      <td></td>\n",
1241 |        "    </tr>\n",
1242 |        "    <tr>\n",
1243 |        "      <th>Jay</th>\n",
1244 |        "      <td>1.2</td>\n",
1245 |        "      <td>White</td>\n",
1246 |        "      <td>Toyota</td>\n",
1247 |        "      <td></td>\n",
1248 |        "    </tr>\n",
1249 |        "    <tr>\n",
1250 |        "      <th>James</th>\n",
1251 |        "      <td>1.6</td>\n",
1252 |        "      <td>Blue</td>\n",
1253 |        "      <td>Honda</td>\n",
1254 |        "      <td>Jazz</td>\n",
1255 |        "    </tr>\n",
1256 |        "  </tbody>\n",
1257 |        "</table>\n",
1258 |        "</div>"
1259 |       ],
1260 |       "text/plain": [
1261 |        "       Capacity   Color      Make Model\n",
1262 |        "Jane        0.0  Orange      Ford      \n",
1263 |        "John        2.0  Silver       BMW      \n",
1264 |        "June        1.6     Red      Mini      \n",
1265 |        "Jim         2.2   White  Mercedes      \n",
1266 |        "Jay         1.2   White    Toyota      \n",
1267 |        "James       1.6    Blue     Honda  Jazz"
1268 |       ]
1269 |      },
1270 |      "execution_count": 16,
1271 |      "metadata": {},
1272 |      "output_type": "execute_result"
1273 |     }
1274 |    ],
1275 |    "source": [
1276 |     "df1.drop(\"Service Interval\", axis = 1, inplace = True) # axis = 1 says you're looking a columns\n",
1277 |     "df1"
1278 |    ]
1279 |   },
1280 |   {
1281 |    "cell_type": "markdown",
1282 |    "metadata": {},
1283 |    "source": [
1284 |     "### Removing rows"
1285 |    ]
1286 |   },
1287 |   {
1288 |    "cell_type": "code",
1289 |    "execution_count": 17,
1290 |    "metadata": {},
1291 |    "outputs": [
1292 |     {
1293 |      "data": {
1294 |       "text/html": [
1295 |        "<div>\n",
1296 |        "<style scoped>\n",
1297 |        "    .dataframe tbody tr th:only-of-type {\n",
1298 |        "        vertical-align: middle;\n",
1299 |        "    }\n",
1300 |        "\n",
1301 |        "    .dataframe tbody tr th {\n",
1302 |        "        vertical-align: top;\n",
1303 |        "    }\n",
1304 |        "\n",
1305 |        "    .dataframe thead th {\n",
1306 |        "        text-align: right;\n",
1307 |        "    }\n",
1308 |        "</style>\n",
1309 |        "<table border=\"1\" class=\"dataframe\">\n",
1310 |        "  <thead>\n",
1311 |        "    <tr style=\"text-align: right;\">\n",
1312 |        "      <th></th>\n",
1313 |        "      <th>Capacity</th>\n",
1314 |        "      <th>Color</th>\n",
1315 |        "      <th>Make</th>\n",
1316 |        "      <th>Model</th>\n",
1317 |        "    </tr>\n",
1318 |        "  </thead>\n",
1319 |        "  <tbody>\n",
1320 |        "    <tr>\n",
1321 |        "      <th>Jane</th>\n",
1322 |        "      <td>0.0</td>\n",
1323 |        "      <td>Orange</td>\n",
1324 |        "      <td>Ford</td>\n",
1325 |        "      <td></td>\n",
1326 |        "    </tr>\n",
1327 |        "    <tr>\n",
1328 |        "      <th>June</th>\n",
1329 |        "      <td>1.6</td>\n",
1330 |        "      <td>Red</td>\n",
1331 |        "      <td>Mini</td>\n",
1332 |        "      <td></td>\n",
1333 |        "    </tr>\n",
1334 |        "    <tr>\n",
1335 |        "      <th>Jim</th>\n",
1336 |        "      <td>2.2</td>\n",
1337 |        "      <td>White</td>\n",
1338 |        "      <td>Mercedes</td>\n",
1339 |        "      <td></td>\n",
1340 |        "    </tr>\n",
1341 |        "    <tr>\n",
1342 |        "      <th>Jay</th>\n",
1343 |        "      <td>1.2</td>\n",
1344 |        "      <td>White</td>\n",
1345 |        "      <td>Toyota</td>\n",
1346 |        "      <td></td>\n",
1347 |        "    </tr>\n",
1348 |        "    <tr>\n",
1349 |        "      <th>James</th>\n",
1350 |        "      <td>1.6</td>\n",
1351 |        "      <td>Blue</td>\n",
1352 |        "      <td>Honda</td>\n",
1353 |        "      <td>Jazz</td>\n",
1354 |        "    </tr>\n",
1355 |        "  </tbody>\n",
1356 |        "</table>\n",
1357 |        "</div>"
1358 |       ],
1359 |       "text/plain": [
1360 |        "       Capacity   Color      Make Model\n",
1361 |        "Jane        0.0  Orange      Ford      \n",
1362 |        "June        1.6     Red      Mini      \n",
1363 |        "Jim         2.2   White  Mercedes      \n",
1364 |        "Jay         1.2   White    Toyota      \n",
1365 |        "James       1.6    Blue     Honda  Jazz"
1366 |       ]
1367 |      },
1368 |      "execution_count": 17,
1369 |      "metadata": {},
1370 |      "output_type": "execute_result"
1371 |     }
1372 |    ],
1373 |    "source": [
1374 |     "df1.drop(\"John\", axis = 0, inplace = True) # axis = 0 says you're looking a rows\n",
1375 |     "df1"
1376 |    ]
1377 |   },
1378 |   {
1379 |    "cell_type": "code",
1380 |    "execution_count": 18,
1381 |    "metadata": {},
1382 |    "outputs": [
1383 |     {
1384 |      "data": {
1385 |       "text/html": [
1386 |        "<div>\n",
1387 |        "<style scoped>\n",
1388 |        "    .dataframe tbody tr th:only-of-type {\n",
1389 |        "        vertical-align: middle;\n",
1390 |        "    }\n",
1391 |        "\n",
1392 |        "    .dataframe tbody tr th {\n",
1393 |        "        vertical-align: top;\n",
1394 |        "    }\n",
1395 |        "\n",
1396 |        "    .dataframe thead th {\n",
1397 |        "        text-align: right;\n",
1398 |        "    }\n",
1399 |        "</style>\n",
1400 |        "<table border=\"1\" class=\"dataframe\">\n",
1401 |        "  <thead>\n",
1402 |        "    <tr style=\"text-align: right;\">\n",
1403 |        "      <th></th>\n",
1404 |        "      <th>Capacity</th>\n",
1405 |        "      <th>Color</th>\n",
1406 |        "      <th>Make</th>\n",
1407 |        "      <th>Model</th>\n",
1408 |        "    </tr>\n",
1409 |        "  </thead>\n",
1410 |        "  <tbody>\n",
1411 |        "    <tr>\n",
1412 |        "      <th>Jane</th>\n",
1413 |        "      <td>0.0</td>\n",
1414 |        "      <td>Orange</td>\n",
1415 |        "      <td>Ford</td>\n",
1416 |        "      <td></td>\n",
1417 |        "    </tr>\n",
1418 |        "    <tr>\n",
1419 |        "      <th>June</th>\n",
1420 |        "      <td>1.6</td>\n",
1421 |        "      <td>Red</td>\n",
1422 |        "      <td>Mini</td>\n",
1423 |        "      <td></td>\n",
1424 |        "    </tr>\n",
1425 |        "    <tr>\n",
1426 |        "      <th>James</th>\n",
1427 |        "      <td>1.6</td>\n",
1428 |        "      <td>Blue</td>\n",
1429 |        "      <td>Honda</td>\n",
1430 |        "      <td>Jazz</td>\n",
1431 |        "    </tr>\n",
1432 |        "  </tbody>\n",
1433 |        "</table>\n",
1434 |        "</div>"
1435 |       ],
1436 |       "text/plain": [
1437 |        "       Capacity   Color   Make Model\n",
1438 |        "Jane        0.0  Orange   Ford      \n",
1439 |        "June        1.6     Red   Mini      \n",
1440 |        "James       1.6    Blue  Honda  Jazz"
1441 |       ]
1442 |      },
1443 |      "execution_count": 18,
1444 |      "metadata": {},
1445 |      "output_type": "execute_result"
1446 |     }
1447 |    ],
1448 |    "source": [
1449 |     "df1 = df1[df1[\"Color\"] != \"White\"] # a simple way to remove rows based on a condition\n",
1450 |     "df1"
1451 |    ]
1452 |   },
1453 |   {
1454 |    "cell_type": "markdown",
1455 |    "metadata": {},
1456 |    "source": [
1457 |     "### Transposing the data"
1458 |    ]
1459 |   },
1460 |   {
1461 |    "cell_type": "code",
1462 |    "execution_count": 19,
1463 |    "metadata": {},
1464 |    "outputs": [
1465 |     {
1466 |      "data": {
1467 |       "text/html": [
1468 |        "<div>\n",
1469 |        "<style scoped>\n",
1470 |        "    .dataframe tbody tr th:only-of-type {\n",
1471 |        "        vertical-align: middle;\n",
1472 |        "    }\n",
1473 |        "\n",
1474 |        "    .dataframe tbody tr th {\n",
1475 |        "        vertical-align: top;\n",
1476 |        "    }\n",
1477 |        "\n",
1478 |        "    .dataframe thead th {\n",
1479 |        "        text-align: right;\n",
1480 |        "    }\n",
1481 |        "</style>\n",
1482 |        "<table border=\"1\" class=\"dataframe\">\n",
1483 |        "  <thead>\n",
1484 |        "    <tr style=\"text-align: right;\">\n",
1485 |        "      <th></th>\n",
1486 |        "      <th>Jane</th>\n",
1487 |        "      <th>June</th>\n",
1488 |        "      <th>James</th>\n",
1489 |        "    </tr>\n",
1490 |        "  </thead>\n",
1491 |        "  <tbody>\n",
1492 |        "    <tr>\n",
1493 |        "      <th>Capacity</th>\n",
1494 |        "      <td>0</td>\n",
1495 |        "      <td>1.6</td>\n",
1496 |        "      <td>1.6</td>\n",
1497 |        "    </tr>\n",
1498 |        "    <tr>\n",
1499 |        "      <th>Color</th>\n",
1500 |        "      <td>Orange</td>\n",
1501 |        "      <td>Red</td>\n",
1502 |        "      <td>Blue</td>\n",
1503 |        "    </tr>\n",
1504 |        "    <tr>\n",
1505 |        "      <th>Make</th>\n",
1506 |        "      <td>Ford</td>\n",
1507 |        "      <td>Mini</td>\n",
1508 |        "      <td>Honda</td>\n",
1509 |        "    </tr>\n",
1510 |        "    <tr>\n",
1511 |        "      <th>Model</th>\n",
1512 |        "      <td></td>\n",
1513 |        "      <td></td>\n",
1514 |        "      <td>Jazz</td>\n",
1515 |        "    </tr>\n",
1516 |        "  </tbody>\n",
1517 |        "</table>\n",
1518 |        "</div>"
1519 |       ],
1520 |       "text/plain": [
1521 |        "            Jane  June  James\n",
1522 |        "Capacity       0   1.6    1.6\n",
1523 |        "Color     Orange   Red   Blue\n",
1524 |        "Make        Ford  Mini  Honda\n",
1525 |        "Model                    Jazz"
1526 |       ]
1527 |      },
1528 |      "execution_count": 19,
1529 |      "metadata": {},
1530 |      "output_type": "execute_result"
1531 |     }
1532 |    ],
1533 |    "source": [
1534 |     "df1 = df1.transpose() # flips the data around if it's more convenient\n",
1535 |     "df1"
1536 |    ]
1537 |   }
1538 |  ],
1539 |  "metadata": {
1540 |   "kernelspec": {
1541 |    "display_name": "Python 3",
1542 |    "language": "python",
1543 |    "name": "python3"
1544 |   },
1545 |   "language_info": {
1546 |    "codemirror_mode": {
1547 |     "name": "ipython",
1548 |     "version": 3
1549 |    },
1550 |    "file_extension": ".py",
1551 |    "mimetype": "text/x-python",
1552 |    "name": "python",
1553 |    "nbconvert_exporter": "python",
1554 |    "pygments_lexer": "ipython3",
1555 |    "version": "3.6.5"
1556 |   }
1557 |  },
1558 |  "nbformat": 4,
1559 |  "nbformat_minor": 2
1560 | }
1561 | 


--------------------------------------------------------------------------------
/How it works - Pandas, data selection.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# How it works - Pandas, data selection"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "code",
  12 |    "execution_count": 1,
  13 |    "metadata": {},
  14 |    "outputs": [],
  15 |    "source": [
  16 |     "import pandas as pd"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": 2,
  22 |    "metadata": {},
  23 |    "outputs": [],
  24 |    "source": [
  25 |     "list1 = [\"Jane\", \"John\", \"June\", \"Jim\", \"Jay\"]\n",
  26 |     "list2 = [\"Ford\", \"BMW\", \"Mini\", \"Mercedes\", \"Toyota\"]\n",
  27 |     "list3 = [\"Blue\", \"Grey\", \"Red\", \"White\", \"White\"]\n",
  28 |     "list4 = [\"1.6l\", \"2.0l\", \"1.6l\", \"2.2l\", \"1.2l\"]\n",
  29 |     "df = pd.DataFrame({\"Make\":list2, \"Color\":list3, \"Capacity\":list4}, \n",
  30 |     "                  index = list1)"
  31 |    ]
  32 |   },
  33 |   {
  34 |    "cell_type": "code",
  35 |    "execution_count": 3,
  36 |    "metadata": {},
  37 |    "outputs": [
  38 |     {
  39 |      "data": {
  40 |       "text/html": [
  41 |        "<div>\n",
  42 |        "<style scoped>\n",
  43 |        "    .dataframe tbody tr th:only-of-type {\n",
  44 |        "        vertical-align: middle;\n",
  45 |        "    }\n",
  46 |        "\n",
  47 |        "    .dataframe tbody tr th {\n",
  48 |        "        vertical-align: top;\n",
  49 |        "    }\n",
  50 |        "\n",
  51 |        "    .dataframe thead th {\n",
  52 |        "        text-align: right;\n",
  53 |        "    }\n",
  54 |        "</style>\n",
  55 |        "<table border=\"1\" class=\"dataframe\">\n",
  56 |        "  <thead>\n",
  57 |        "    <tr style=\"text-align: right;\">\n",
  58 |        "      <th></th>\n",
  59 |        "      <th>Make</th>\n",
  60 |        "      <th>Color</th>\n",
  61 |        "      <th>Capacity</th>\n",
  62 |        "    </tr>\n",
  63 |        "  </thead>\n",
  64 |        "  <tbody>\n",
  65 |        "    <tr>\n",
  66 |        "      <th>Jane</th>\n",
  67 |        "      <td>Ford</td>\n",
  68 |        "      <td>Blue</td>\n",
  69 |        "      <td>1.6l</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>John</th>\n",
  73 |        "      <td>BMW</td>\n",
  74 |        "      <td>Grey</td>\n",
  75 |        "      <td>2.0l</td>\n",
  76 |        "    </tr>\n",
  77 |        "    <tr>\n",
  78 |        "      <th>June</th>\n",
  79 |        "      <td>Mini</td>\n",
  80 |        "      <td>Red</td>\n",
  81 |        "      <td>1.6l</td>\n",
  82 |        "    </tr>\n",
  83 |        "    <tr>\n",
  84 |        "      <th>Jim</th>\n",
  85 |        "      <td>Mercedes</td>\n",
  86 |        "      <td>White</td>\n",
  87 |        "      <td>2.2l</td>\n",
  88 |        "    </tr>\n",
  89 |        "    <tr>\n",
  90 |        "      <th>Jay</th>\n",
  91 |        "      <td>Toyota</td>\n",
  92 |        "      <td>White</td>\n",
  93 |        "      <td>1.2l</td>\n",
  94 |        "    </tr>\n",
  95 |        "  </tbody>\n",
  96 |        "</table>\n",
  97 |        "</div>"
  98 |       ],
  99 |       "text/plain": [
 100 |        "          Make  Color Capacity\n",
 101 |        "Jane      Ford   Blue     1.6l\n",
 102 |        "John       BMW   Grey     2.0l\n",
 103 |        "June      Mini    Red     1.6l\n",
 104 |        "Jim   Mercedes  White     2.2l\n",
 105 |        "Jay     Toyota  White     1.2l"
 106 |       ]
 107 |      },
 108 |      "execution_count": 3,
 109 |      "metadata": {},
 110 |      "output_type": "execute_result"
 111 |     }
 112 |    ],
 113 |    "source": [
 114 |     "df"
 115 |    ]
 116 |   },
 117 |   {
 118 |    "cell_type": "markdown",
 119 |    "metadata": {},
 120 |    "source": [
 121 |     "## Selection by column"
 122 |    ]
 123 |   },
 124 |   {
 125 |    "cell_type": "code",
 126 |    "execution_count": 4,
 127 |    "metadata": {},
 128 |    "outputs": [
 129 |     {
 130 |      "data": {
 131 |       "text/plain": [
 132 |        "Jane    1.6l\n",
 133 |        "John    2.0l\n",
 134 |        "June    1.6l\n",
 135 |        "Jim     2.2l\n",
 136 |        "Jay     1.2l\n",
 137 |        "Name: Capacity, dtype: object"
 138 |       ]
 139 |      },
 140 |      "execution_count": 4,
 141 |      "metadata": {},
 142 |      "output_type": "execute_result"
 143 |     }
 144 |    ],
 145 |    "source": [
 146 |     "df[\"Capacity\"] # Selection of the SERIES by the column name (note the single bracket)"
 147 |    ]
 148 |   },
 149 |   {
 150 |    "cell_type": "code",
 151 |    "execution_count": 5,
 152 |    "metadata": {},
 153 |    "outputs": [
 154 |     {
 155 |      "data": {
 156 |       "text/html": [
 157 |        "<div>\n",
 158 |        "<style scoped>\n",
 159 |        "    .dataframe tbody tr th:only-of-type {\n",
 160 |        "        vertical-align: middle;\n",
 161 |        "    }\n",
 162 |        "\n",
 163 |        "    .dataframe tbody tr th {\n",
 164 |        "        vertical-align: top;\n",
 165 |        "    }\n",
 166 |        "\n",
 167 |        "    .dataframe thead th {\n",
 168 |        "        text-align: right;\n",
 169 |        "    }\n",
 170 |        "</style>\n",
 171 |        "<table border=\"1\" class=\"dataframe\">\n",
 172 |        "  <thead>\n",
 173 |        "    <tr style=\"text-align: right;\">\n",
 174 |        "      <th></th>\n",
 175 |        "      <th>Capacity</th>\n",
 176 |        "    </tr>\n",
 177 |        "  </thead>\n",
 178 |        "  <tbody>\n",
 179 |        "    <tr>\n",
 180 |        "      <th>Jane</th>\n",
 181 |        "      <td>1.6l</td>\n",
 182 |        "    </tr>\n",
 183 |        "    <tr>\n",
 184 |        "      <th>John</th>\n",
 185 |        "      <td>2.0l</td>\n",
 186 |        "    </tr>\n",
 187 |        "    <tr>\n",
 188 |        "      <th>June</th>\n",
 189 |        "      <td>1.6l</td>\n",
 190 |        "    </tr>\n",
 191 |        "    <tr>\n",
 192 |        "      <th>Jim</th>\n",
 193 |        "      <td>2.2l</td>\n",
 194 |        "    </tr>\n",
 195 |        "    <tr>\n",
 196 |        "      <th>Jay</th>\n",
 197 |        "      <td>1.2l</td>\n",
 198 |        "    </tr>\n",
 199 |        "  </tbody>\n",
 200 |        "</table>\n",
 201 |        "</div>"
 202 |       ],
 203 |       "text/plain": [
 204 |        "     Capacity\n",
 205 |        "Jane     1.6l\n",
 206 |        "John     2.0l\n",
 207 |        "June     1.6l\n",
 208 |        "Jim      2.2l\n",
 209 |        "Jay      1.2l"
 210 |       ]
 211 |      },
 212 |      "execution_count": 5,
 213 |      "metadata": {},
 214 |      "output_type": "execute_result"
 215 |     }
 216 |    ],
 217 |    "source": [
 218 |     "df[[\"Capacity\"]] # Selection of the DATAFRAME by column name (note the double bracket)"
 219 |    ]
 220 |   },
 221 |   {
 222 |    "cell_type": "code",
 223 |    "execution_count": 6,
 224 |    "metadata": {},
 225 |    "outputs": [
 226 |     {
 227 |      "data": {
 228 |       "text/html": [
 229 |        "<div>\n",
 230 |        "<style scoped>\n",
 231 |        "    .dataframe tbody tr th:only-of-type {\n",
 232 |        "        vertical-align: middle;\n",
 233 |        "    }\n",
 234 |        "\n",
 235 |        "    .dataframe tbody tr th {\n",
 236 |        "        vertical-align: top;\n",
 237 |        "    }\n",
 238 |        "\n",
 239 |        "    .dataframe thead th {\n",
 240 |        "        text-align: right;\n",
 241 |        "    }\n",
 242 |        "</style>\n",
 243 |        "<table border=\"1\" class=\"dataframe\">\n",
 244 |        "  <thead>\n",
 245 |        "    <tr style=\"text-align: right;\">\n",
 246 |        "      <th></th>\n",
 247 |        "      <th>Color</th>\n",
 248 |        "      <th>Capacity</th>\n",
 249 |        "    </tr>\n",
 250 |        "  </thead>\n",
 251 |        "  <tbody>\n",
 252 |        "    <tr>\n",
 253 |        "      <th>Jane</th>\n",
 254 |        "      <td>Blue</td>\n",
 255 |        "      <td>1.6l</td>\n",
 256 |        "    </tr>\n",
 257 |        "    <tr>\n",
 258 |        "      <th>John</th>\n",
 259 |        "      <td>Grey</td>\n",
 260 |        "      <td>2.0l</td>\n",
 261 |        "    </tr>\n",
 262 |        "    <tr>\n",
 263 |        "      <th>June</th>\n",
 264 |        "      <td>Red</td>\n",
 265 |        "      <td>1.6l</td>\n",
 266 |        "    </tr>\n",
 267 |        "    <tr>\n",
 268 |        "      <th>Jim</th>\n",
 269 |        "      <td>White</td>\n",
 270 |        "      <td>2.2l</td>\n",
 271 |        "    </tr>\n",
 272 |        "    <tr>\n",
 273 |        "      <th>Jay</th>\n",
 274 |        "      <td>White</td>\n",
 275 |        "      <td>1.2l</td>\n",
 276 |        "    </tr>\n",
 277 |        "  </tbody>\n",
 278 |        "</table>\n",
 279 |        "</div>"
 280 |       ],
 281 |       "text/plain": [
 282 |        "      Color Capacity\n",
 283 |        "Jane   Blue     1.6l\n",
 284 |        "John   Grey     2.0l\n",
 285 |        "June    Red     1.6l\n",
 286 |        "Jim   White     2.2l\n",
 287 |        "Jay   White     1.2l"
 288 |       ]
 289 |      },
 290 |      "execution_count": 6,
 291 |      "metadata": {},
 292 |      "output_type": "execute_result"
 293 |     }
 294 |    ],
 295 |    "source": [
 296 |     "df[[\"Color\", \"Capacity\"]] # Selection by multiple column names (note the new listed order)"
 297 |    ]
 298 |   },
 299 |   {
 300 |    "cell_type": "code",
 301 |    "execution_count": 7,
 302 |    "metadata": {},
 303 |    "outputs": [
 304 |     {
 305 |      "data": {
 306 |       "text/html": [
 307 |        "<div>\n",
 308 |        "<style scoped>\n",
 309 |        "    .dataframe tbody tr th:only-of-type {\n",
 310 |        "        vertical-align: middle;\n",
 311 |        "    }\n",
 312 |        "\n",
 313 |        "    .dataframe tbody tr th {\n",
 314 |        "        vertical-align: top;\n",
 315 |        "    }\n",
 316 |        "\n",
 317 |        "    .dataframe thead th {\n",
 318 |        "        text-align: right;\n",
 319 |        "    }\n",
 320 |        "</style>\n",
 321 |        "<table border=\"1\" class=\"dataframe\">\n",
 322 |        "  <thead>\n",
 323 |        "    <tr style=\"text-align: right;\">\n",
 324 |        "      <th></th>\n",
 325 |        "      <th>Color</th>\n",
 326 |        "      <th>Capacity</th>\n",
 327 |        "    </tr>\n",
 328 |        "  </thead>\n",
 329 |        "  <tbody>\n",
 330 |        "    <tr>\n",
 331 |        "      <th>Jane</th>\n",
 332 |        "      <td>Blue</td>\n",
 333 |        "      <td>1.6l</td>\n",
 334 |        "    </tr>\n",
 335 |        "    <tr>\n",
 336 |        "      <th>John</th>\n",
 337 |        "      <td>Grey</td>\n",
 338 |        "      <td>2.0l</td>\n",
 339 |        "    </tr>\n",
 340 |        "    <tr>\n",
 341 |        "      <th>June</th>\n",
 342 |        "      <td>Red</td>\n",
 343 |        "      <td>1.6l</td>\n",
 344 |        "    </tr>\n",
 345 |        "    <tr>\n",
 346 |        "      <th>Jim</th>\n",
 347 |        "      <td>White</td>\n",
 348 |        "      <td>2.2l</td>\n",
 349 |        "    </tr>\n",
 350 |        "    <tr>\n",
 351 |        "      <th>Jay</th>\n",
 352 |        "      <td>White</td>\n",
 353 |        "      <td>1.2l</td>\n",
 354 |        "    </tr>\n",
 355 |        "  </tbody>\n",
 356 |        "</table>\n",
 357 |        "</div>"
 358 |       ],
 359 |       "text/plain": [
 360 |        "      Color Capacity\n",
 361 |        "Jane   Blue     1.6l\n",
 362 |        "John   Grey     2.0l\n",
 363 |        "June    Red     1.6l\n",
 364 |        "Jim   White     2.2l\n",
 365 |        "Jay   White     1.2l"
 366 |       ]
 367 |      },
 368 |      "execution_count": 7,
 369 |      "metadata": {},
 370 |      "output_type": "execute_result"
 371 |     }
 372 |    ],
 373 |    "source": [
 374 |     "df.loc[:,[\"Color\", \"Capacity\"]] # Selection by column names using the .loc method (columns is the 2nd argument)"
 375 |    ]
 376 |   },
 377 |   {
 378 |    "cell_type": "code",
 379 |    "execution_count": 8,
 380 |    "metadata": {},
 381 |    "outputs": [
 382 |     {
 383 |      "data": {
 384 |       "text/html": [
 385 |        "<div>\n",
 386 |        "<style scoped>\n",
 387 |        "    .dataframe tbody tr th:only-of-type {\n",
 388 |        "        vertical-align: middle;\n",
 389 |        "    }\n",
 390 |        "\n",
 391 |        "    .dataframe tbody tr th {\n",
 392 |        "        vertical-align: top;\n",
 393 |        "    }\n",
 394 |        "\n",
 395 |        "    .dataframe thead th {\n",
 396 |        "        text-align: right;\n",
 397 |        "    }\n",
 398 |        "</style>\n",
 399 |        "<table border=\"1\" class=\"dataframe\">\n",
 400 |        "  <thead>\n",
 401 |        "    <tr style=\"text-align: right;\">\n",
 402 |        "      <th></th>\n",
 403 |        "      <th>Color</th>\n",
 404 |        "      <th>Make</th>\n",
 405 |        "    </tr>\n",
 406 |        "  </thead>\n",
 407 |        "  <tbody>\n",
 408 |        "    <tr>\n",
 409 |        "      <th>Jane</th>\n",
 410 |        "      <td>Blue</td>\n",
 411 |        "      <td>Ford</td>\n",
 412 |        "    </tr>\n",
 413 |        "    <tr>\n",
 414 |        "      <th>John</th>\n",
 415 |        "      <td>Grey</td>\n",
 416 |        "      <td>BMW</td>\n",
 417 |        "    </tr>\n",
 418 |        "    <tr>\n",
 419 |        "      <th>June</th>\n",
 420 |        "      <td>Red</td>\n",
 421 |        "      <td>Mini</td>\n",
 422 |        "    </tr>\n",
 423 |        "    <tr>\n",
 424 |        "      <th>Jim</th>\n",
 425 |        "      <td>White</td>\n",
 426 |        "      <td>Mercedes</td>\n",
 427 |        "    </tr>\n",
 428 |        "    <tr>\n",
 429 |        "      <th>Jay</th>\n",
 430 |        "      <td>White</td>\n",
 431 |        "      <td>Toyota</td>\n",
 432 |        "    </tr>\n",
 433 |        "  </tbody>\n",
 434 |        "</table>\n",
 435 |        "</div>"
 436 |       ],
 437 |       "text/plain": [
 438 |        "      Color      Make\n",
 439 |        "Jane   Blue      Ford\n",
 440 |        "John   Grey       BMW\n",
 441 |        "June    Red      Mini\n",
 442 |        "Jim   White  Mercedes\n",
 443 |        "Jay   White    Toyota"
 444 |       ]
 445 |      },
 446 |      "execution_count": 8,
 447 |      "metadata": {},
 448 |      "output_type": "execute_result"
 449 |     }
 450 |    ],
 451 |    "source": [
 452 |     "df.iloc[:,[1, 0]] # Selection by column indices using the .iloc method (columns is the 2nd argument)"
 453 |    ]
 454 |   },
 455 |   {
 456 |    "cell_type": "markdown",
 457 |    "metadata": {},
 458 |    "source": [
 459 |     "## Selection by row"
 460 |    ]
 461 |   },
 462 |   {
 463 |    "cell_type": "code",
 464 |    "execution_count": 9,
 465 |    "metadata": {},
 466 |    "outputs": [
 467 |     {
 468 |      "data": {
 469 |       "text/html": [
 470 |        "<div>\n",
 471 |        "<style scoped>\n",
 472 |        "    .dataframe tbody tr th:only-of-type {\n",
 473 |        "        vertical-align: middle;\n",
 474 |        "    }\n",
 475 |        "\n",
 476 |        "    .dataframe tbody tr th {\n",
 477 |        "        vertical-align: top;\n",
 478 |        "    }\n",
 479 |        "\n",
 480 |        "    .dataframe thead th {\n",
 481 |        "        text-align: right;\n",
 482 |        "    }\n",
 483 |        "</style>\n",
 484 |        "<table border=\"1\" class=\"dataframe\">\n",
 485 |        "  <thead>\n",
 486 |        "    <tr style=\"text-align: right;\">\n",
 487 |        "      <th></th>\n",
 488 |        "      <th>Make</th>\n",
 489 |        "      <th>Color</th>\n",
 490 |        "      <th>Capacity</th>\n",
 491 |        "    </tr>\n",
 492 |        "  </thead>\n",
 493 |        "  <tbody>\n",
 494 |        "    <tr>\n",
 495 |        "      <th>Jane</th>\n",
 496 |        "      <td>Ford</td>\n",
 497 |        "      <td>Blue</td>\n",
 498 |        "      <td>1.6l</td>\n",
 499 |        "    </tr>\n",
 500 |        "    <tr>\n",
 501 |        "      <th>June</th>\n",
 502 |        "      <td>Mini</td>\n",
 503 |        "      <td>Red</td>\n",
 504 |        "      <td>1.6l</td>\n",
 505 |        "    </tr>\n",
 506 |        "  </tbody>\n",
 507 |        "</table>\n",
 508 |        "</div>"
 509 |       ],
 510 |       "text/plain": [
 511 |        "      Make Color Capacity\n",
 512 |        "Jane  Ford  Blue     1.6l\n",
 513 |        "June  Mini   Red     1.6l"
 514 |       ]
 515 |      },
 516 |      "execution_count": 9,
 517 |      "metadata": {},
 518 |      "output_type": "execute_result"
 519 |     }
 520 |    ],
 521 |    "source": [
 522 |     "df.loc[[\"Jane\", \"June\"],:] # Selection by row names using the .loc method (rows is the 1st argument)"
 523 |    ]
 524 |   },
 525 |   {
 526 |    "cell_type": "code",
 527 |    "execution_count": 10,
 528 |    "metadata": {},
 529 |    "outputs": [
 530 |     {
 531 |      "data": {
 532 |       "text/html": [
 533 |        "<div>\n",
 534 |        "<style scoped>\n",
 535 |        "    .dataframe tbody tr th:only-of-type {\n",
 536 |        "        vertical-align: middle;\n",
 537 |        "    }\n",
 538 |        "\n",
 539 |        "    .dataframe tbody tr th {\n",
 540 |        "        vertical-align: top;\n",
 541 |        "    }\n",
 542 |        "\n",
 543 |        "    .dataframe thead th {\n",
 544 |        "        text-align: right;\n",
 545 |        "    }\n",
 546 |        "</style>\n",
 547 |        "<table border=\"1\" class=\"dataframe\">\n",
 548 |        "  <thead>\n",
 549 |        "    <tr style=\"text-align: right;\">\n",
 550 |        "      <th></th>\n",
 551 |        "      <th>Make</th>\n",
 552 |        "      <th>Color</th>\n",
 553 |        "      <th>Capacity</th>\n",
 554 |        "    </tr>\n",
 555 |        "  </thead>\n",
 556 |        "  <tbody>\n",
 557 |        "    <tr>\n",
 558 |        "      <th>Jane</th>\n",
 559 |        "      <td>Ford</td>\n",
 560 |        "      <td>Blue</td>\n",
 561 |        "      <td>1.6l</td>\n",
 562 |        "    </tr>\n",
 563 |        "    <tr>\n",
 564 |        "      <th>June</th>\n",
 565 |        "      <td>Mini</td>\n",
 566 |        "      <td>Red</td>\n",
 567 |        "      <td>1.6l</td>\n",
 568 |        "    </tr>\n",
 569 |        "  </tbody>\n",
 570 |        "</table>\n",
 571 |        "</div>"
 572 |       ],
 573 |       "text/plain": [
 574 |        "      Make Color Capacity\n",
 575 |        "Jane  Ford  Blue     1.6l\n",
 576 |        "June  Mini   Red     1.6l"
 577 |       ]
 578 |      },
 579 |      "execution_count": 10,
 580 |      "metadata": {},
 581 |      "output_type": "execute_result"
 582 |     }
 583 |    ],
 584 |    "source": [
 585 |     "df.iloc[[0,2],:] # Selection by row indices using the .iloc method (rows is the 1st argument)"
 586 |    ]
 587 |   },
 588 |   {
 589 |    "cell_type": "markdown",
 590 |    "metadata": {},
 591 |    "source": [
 592 |     "## Selection by row and column"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "code",
 597 |    "execution_count": 11,
 598 |    "metadata": {},
 599 |    "outputs": [
 600 |     {
 601 |      "data": {
 602 |       "text/html": [
 603 |        "<div>\n",
 604 |        "<style scoped>\n",
 605 |        "    .dataframe tbody tr th:only-of-type {\n",
 606 |        "        vertical-align: middle;\n",
 607 |        "    }\n",
 608 |        "\n",
 609 |        "    .dataframe tbody tr th {\n",
 610 |        "        vertical-align: top;\n",
 611 |        "    }\n",
 612 |        "\n",
 613 |        "    .dataframe thead th {\n",
 614 |        "        text-align: right;\n",
 615 |        "    }\n",
 616 |        "</style>\n",
 617 |        "<table border=\"1\" class=\"dataframe\">\n",
 618 |        "  <thead>\n",
 619 |        "    <tr style=\"text-align: right;\">\n",
 620 |        "      <th></th>\n",
 621 |        "      <th>Capacity</th>\n",
 622 |        "      <th>Make</th>\n",
 623 |        "    </tr>\n",
 624 |        "  </thead>\n",
 625 |        "  <tbody>\n",
 626 |        "    <tr>\n",
 627 |        "      <th>Jane</th>\n",
 628 |        "      <td>1.6l</td>\n",
 629 |        "      <td>Ford</td>\n",
 630 |        "    </tr>\n",
 631 |        "    <tr>\n",
 632 |        "      <th>June</th>\n",
 633 |        "      <td>1.6l</td>\n",
 634 |        "      <td>Mini</td>\n",
 635 |        "    </tr>\n",
 636 |        "  </tbody>\n",
 637 |        "</table>\n",
 638 |        "</div>"
 639 |       ],
 640 |       "text/plain": [
 641 |        "     Capacity  Make\n",
 642 |        "Jane     1.6l  Ford\n",
 643 |        "June     1.6l  Mini"
 644 |       ]
 645 |      },
 646 |      "execution_count": 11,
 647 |      "metadata": {},
 648 |      "output_type": "execute_result"
 649 |     }
 650 |    ],
 651 |    "source": [
 652 |     "df.loc[[\"Jane\", \"June\"],[\"Capacity\", \"Make\"]] # Selection using the .loc method would be preferred!"
 653 |    ]
 654 |   },
 655 |   {
 656 |    "cell_type": "code",
 657 |    "execution_count": 12,
 658 |    "metadata": {},
 659 |    "outputs": [
 660 |     {
 661 |      "data": {
 662 |       "text/html": [
 663 |        "<div>\n",
 664 |        "<style scoped>\n",
 665 |        "    .dataframe tbody tr th:only-of-type {\n",
 666 |        "        vertical-align: middle;\n",
 667 |        "    }\n",
 668 |        "\n",
 669 |        "    .dataframe tbody tr th {\n",
 670 |        "        vertical-align: top;\n",
 671 |        "    }\n",
 672 |        "\n",
 673 |        "    .dataframe thead th {\n",
 674 |        "        text-align: right;\n",
 675 |        "    }\n",
 676 |        "</style>\n",
 677 |        "<table border=\"1\" class=\"dataframe\">\n",
 678 |        "  <thead>\n",
 679 |        "    <tr style=\"text-align: right;\">\n",
 680 |        "      <th></th>\n",
 681 |        "      <th>Make</th>\n",
 682 |        "      <th>Capacity</th>\n",
 683 |        "    </tr>\n",
 684 |        "  </thead>\n",
 685 |        "  <tbody>\n",
 686 |        "    <tr>\n",
 687 |        "      <th>Jane</th>\n",
 688 |        "      <td>Ford</td>\n",
 689 |        "      <td>1.6l</td>\n",
 690 |        "    </tr>\n",
 691 |        "    <tr>\n",
 692 |        "      <th>John</th>\n",
 693 |        "      <td>BMW</td>\n",
 694 |        "      <td>2.0l</td>\n",
 695 |        "    </tr>\n",
 696 |        "  </tbody>\n",
 697 |        "</table>\n",
 698 |        "</div>"
 699 |       ],
 700 |       "text/plain": [
 701 |        "      Make Capacity\n",
 702 |        "Jane  Ford     1.6l\n",
 703 |        "John   BMW     2.0l"
 704 |       ]
 705 |      },
 706 |      "execution_count": 12,
 707 |      "metadata": {},
 708 |      "output_type": "execute_result"
 709 |     }
 710 |    ],
 711 |    "source": [
 712 |     "df.iloc[[0,1],[0,2]] # Selection using the .iloc method would be preferred!"
 713 |    ]
 714 |   },
 715 |   {
 716 |    "cell_type": "markdown",
 717 |    "metadata": {},
 718 |    "source": [
 719 |     "## Selection by filter"
 720 |    ]
 721 |   },
 722 |   {
 723 |    "cell_type": "code",
 724 |    "execution_count": 13,
 725 |    "metadata": {},
 726 |    "outputs": [
 727 |     {
 728 |      "data": {
 729 |       "text/html": [
 730 |        "<div>\n",
 731 |        "<style scoped>\n",
 732 |        "    .dataframe tbody tr th:only-of-type {\n",
 733 |        "        vertical-align: middle;\n",
 734 |        "    }\n",
 735 |        "\n",
 736 |        "    .dataframe tbody tr th {\n",
 737 |        "        vertical-align: top;\n",
 738 |        "    }\n",
 739 |        "\n",
 740 |        "    .dataframe thead th {\n",
 741 |        "        text-align: right;\n",
 742 |        "    }\n",
 743 |        "</style>\n",
 744 |        "<table border=\"1\" class=\"dataframe\">\n",
 745 |        "  <thead>\n",
 746 |        "    <tr style=\"text-align: right;\">\n",
 747 |        "      <th></th>\n",
 748 |        "      <th>Make</th>\n",
 749 |        "      <th>Color</th>\n",
 750 |        "      <th>Capacity</th>\n",
 751 |        "    </tr>\n",
 752 |        "  </thead>\n",
 753 |        "  <tbody>\n",
 754 |        "    <tr>\n",
 755 |        "      <th>Jim</th>\n",
 756 |        "      <td>Mercedes</td>\n",
 757 |        "      <td>White</td>\n",
 758 |        "      <td>2.2l</td>\n",
 759 |        "    </tr>\n",
 760 |        "    <tr>\n",
 761 |        "      <th>Jay</th>\n",
 762 |        "      <td>Toyota</td>\n",
 763 |        "      <td>White</td>\n",
 764 |        "      <td>1.2l</td>\n",
 765 |        "    </tr>\n",
 766 |        "  </tbody>\n",
 767 |        "</table>\n",
 768 |        "</div>"
 769 |       ],
 770 |       "text/plain": [
 771 |        "         Make  Color Capacity\n",
 772 |        "Jim  Mercedes  White     2.2l\n",
 773 |        "Jay    Toyota  White     1.2l"
 774 |       ]
 775 |      },
 776 |      "execution_count": 13,
 777 |      "metadata": {},
 778 |      "output_type": "execute_result"
 779 |     }
 780 |    ],
 781 |    "source": [
 782 |     "df[df[\"Color\"] == \"White\"] # Notice that we essentially filter on a series and then apply the result to the df\n",
 783 |     "                           # df[\"Color] is the series\n",
 784 |     "                           # we look for values == \"White\" in that series\n",
 785 |     "                           # apply the result to the df\n",
 786 |     "                           # When in doubt... build the code from the inside out!"
 787 |    ]
 788 |   },
 789 |   {
 790 |    "cell_type": "code",
 791 |    "execution_count": 14,
 792 |    "metadata": {},
 793 |    "outputs": [
 794 |     {
 795 |      "data": {
 796 |       "text/html": [
 797 |        "<div>\n",
 798 |        "<style scoped>\n",
 799 |        "    .dataframe tbody tr th:only-of-type {\n",
 800 |        "        vertical-align: middle;\n",
 801 |        "    }\n",
 802 |        "\n",
 803 |        "    .dataframe tbody tr th {\n",
 804 |        "        vertical-align: top;\n",
 805 |        "    }\n",
 806 |        "\n",
 807 |        "    .dataframe thead th {\n",
 808 |        "        text-align: right;\n",
 809 |        "    }\n",
 810 |        "</style>\n",
 811 |        "<table border=\"1\" class=\"dataframe\">\n",
 812 |        "  <thead>\n",
 813 |        "    <tr style=\"text-align: right;\">\n",
 814 |        "      <th></th>\n",
 815 |        "      <th>Make</th>\n",
 816 |        "      <th>Color</th>\n",
 817 |        "      <th>Capacity</th>\n",
 818 |        "    </tr>\n",
 819 |        "  </thead>\n",
 820 |        "  <tbody>\n",
 821 |        "    <tr>\n",
 822 |        "      <th>Jane</th>\n",
 823 |        "      <td>Ford</td>\n",
 824 |        "      <td>Blue</td>\n",
 825 |        "      <td>1.6l</td>\n",
 826 |        "    </tr>\n",
 827 |        "    <tr>\n",
 828 |        "      <th>Jim</th>\n",
 829 |        "      <td>Mercedes</td>\n",
 830 |        "      <td>White</td>\n",
 831 |        "      <td>2.2l</td>\n",
 832 |        "    </tr>\n",
 833 |        "    <tr>\n",
 834 |        "      <th>Jay</th>\n",
 835 |        "      <td>Toyota</td>\n",
 836 |        "      <td>White</td>\n",
 837 |        "      <td>1.2l</td>\n",
 838 |        "    </tr>\n",
 839 |        "  </tbody>\n",
 840 |        "</table>\n",
 841 |        "</div>"
 842 |       ],
 843 |       "text/plain": [
 844 |        "          Make  Color Capacity\n",
 845 |        "Jane      Ford   Blue     1.6l\n",
 846 |        "Jim   Mercedes  White     2.2l\n",
 847 |        "Jay     Toyota  White     1.2l"
 848 |       ]
 849 |      },
 850 |      "execution_count": 14,
 851 |      "metadata": {},
 852 |      "output_type": "execute_result"
 853 |     }
 854 |    ],
 855 |    "source": [
 856 |     "df[(df[\"Color\"] == \"White\") | (df[\"Color\"] == \"Blue\")] # One can apply this technique with boolean operators too"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "markdown",
 861 |    "metadata": {},
 862 |    "source": [
 863 |     "## Selection by mask\n",
 864 |     "This mechanism will be quite handy for re-usability"
 865 |    ]
 866 |   },
 867 |   {
 868 |    "cell_type": "code",
 869 |    "execution_count": 15,
 870 |    "metadata": {},
 871 |    "outputs": [],
 872 |    "source": [
 873 |     "mask = df[\"Color\"] == \"White\""
 874 |    ]
 875 |   },
 876 |   {
 877 |    "cell_type": "code",
 878 |    "execution_count": 16,
 879 |    "metadata": {},
 880 |    "outputs": [
 881 |     {
 882 |      "data": {
 883 |       "text/html": [
 884 |        "<div>\n",
 885 |        "<style scoped>\n",
 886 |        "    .dataframe tbody tr th:only-of-type {\n",
 887 |        "        vertical-align: middle;\n",
 888 |        "    }\n",
 889 |        "\n",
 890 |        "    .dataframe tbody tr th {\n",
 891 |        "        vertical-align: top;\n",
 892 |        "    }\n",
 893 |        "\n",
 894 |        "    .dataframe thead th {\n",
 895 |        "        text-align: right;\n",
 896 |        "    }\n",
 897 |        "</style>\n",
 898 |        "<table border=\"1\" class=\"dataframe\">\n",
 899 |        "  <thead>\n",
 900 |        "    <tr style=\"text-align: right;\">\n",
 901 |        "      <th></th>\n",
 902 |        "      <th>Make</th>\n",
 903 |        "      <th>Color</th>\n",
 904 |        "      <th>Capacity</th>\n",
 905 |        "    </tr>\n",
 906 |        "  </thead>\n",
 907 |        "  <tbody>\n",
 908 |        "    <tr>\n",
 909 |        "      <th>Jim</th>\n",
 910 |        "      <td>Mercedes</td>\n",
 911 |        "      <td>White</td>\n",
 912 |        "      <td>2.2l</td>\n",
 913 |        "    </tr>\n",
 914 |        "    <tr>\n",
 915 |        "      <th>Jay</th>\n",
 916 |        "      <td>Toyota</td>\n",
 917 |        "      <td>White</td>\n",
 918 |        "      <td>1.2l</td>\n",
 919 |        "    </tr>\n",
 920 |        "  </tbody>\n",
 921 |        "</table>\n",
 922 |        "</div>"
 923 |       ],
 924 |       "text/plain": [
 925 |        "         Make  Color Capacity\n",
 926 |        "Jim  Mercedes  White     2.2l\n",
 927 |        "Jay    Toyota  White     1.2l"
 928 |       ]
 929 |      },
 930 |      "execution_count": 16,
 931 |      "metadata": {},
 932 |      "output_type": "execute_result"
 933 |     }
 934 |    ],
 935 |    "source": [
 936 |     "df[mask]"
 937 |    ]
 938 |   },
 939 |   {
 940 |    "cell_type": "markdown",
 941 |    "metadata": {},
 942 |    "source": [
 943 |     "## Selection using the .isin() method\n",
 944 |     "This is pretty handy where you have larger lists of values that you want to check for"
 945 |    ]
 946 |   },
 947 |   {
 948 |    "cell_type": "code",
 949 |    "execution_count": 17,
 950 |    "metadata": {},
 951 |    "outputs": [
 952 |     {
 953 |      "data": {
 954 |       "text/html": [
 955 |        "<div>\n",
 956 |        "<style scoped>\n",
 957 |        "    .dataframe tbody tr th:only-of-type {\n",
 958 |        "        vertical-align: middle;\n",
 959 |        "    }\n",
 960 |        "\n",
 961 |        "    .dataframe tbody tr th {\n",
 962 |        "        vertical-align: top;\n",
 963 |        "    }\n",
 964 |        "\n",
 965 |        "    .dataframe thead th {\n",
 966 |        "        text-align: right;\n",
 967 |        "    }\n",
 968 |        "</style>\n",
 969 |        "<table border=\"1\" class=\"dataframe\">\n",
 970 |        "  <thead>\n",
 971 |        "    <tr style=\"text-align: right;\">\n",
 972 |        "      <th></th>\n",
 973 |        "      <th>Make</th>\n",
 974 |        "      <th>Color</th>\n",
 975 |        "      <th>Capacity</th>\n",
 976 |        "    </tr>\n",
 977 |        "  </thead>\n",
 978 |        "  <tbody>\n",
 979 |        "    <tr>\n",
 980 |        "      <th>Jane</th>\n",
 981 |        "      <td>Ford</td>\n",
 982 |        "      <td>Blue</td>\n",
 983 |        "      <td>1.6l</td>\n",
 984 |        "    </tr>\n",
 985 |        "    <tr>\n",
 986 |        "      <th>June</th>\n",
 987 |        "      <td>Mini</td>\n",
 988 |        "      <td>Red</td>\n",
 989 |        "      <td>1.6l</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <th>Jim</th>\n",
 993 |        "      <td>Mercedes</td>\n",
 994 |        "      <td>White</td>\n",
 995 |        "      <td>2.2l</td>\n",
 996 |        "    </tr>\n",
 997 |        "    <tr>\n",
 998 |        "      <th>Jay</th>\n",
 999 |        "      <td>Toyota</td>\n",
1000 |        "      <td>White</td>\n",
1001 |        "      <td>1.2l</td>\n",
1002 |        "    </tr>\n",
1003 |        "  </tbody>\n",
1004 |        "</table>\n",
1005 |        "</div>"
1006 |       ],
1007 |       "text/plain": [
1008 |        "          Make  Color Capacity\n",
1009 |        "Jane      Ford   Blue     1.6l\n",
1010 |        "June      Mini    Red     1.6l\n",
1011 |        "Jim   Mercedes  White     2.2l\n",
1012 |        "Jay     Toyota  White     1.2l"
1013 |       ]
1014 |      },
1015 |      "execution_count": 17,
1016 |      "metadata": {},
1017 |      "output_type": "execute_result"
1018 |     }
1019 |    ],
1020 |    "source": [
1021 |     "required_vals = [\"White\", \"Blue\", \"Red\"] # Make a list of all the values you want included\n",
1022 |     "df[df[\"Color\"].isin(required_vals)]      # Use this list as a filter with the .isin() method"
1023 |    ]
1024 |   },
1025 |   {
1026 |    "cell_type": "code",
1027 |    "execution_count": 18,
1028 |    "metadata": {},
1029 |    "outputs": [
1030 |     {
1031 |      "data": {
1032 |       "text/html": [
1033 |        "<div>\n",
1034 |        "<style scoped>\n",
1035 |        "    .dataframe tbody tr th:only-of-type {\n",
1036 |        "        vertical-align: middle;\n",
1037 |        "    }\n",
1038 |        "\n",
1039 |        "    .dataframe tbody tr th {\n",
1040 |        "        vertical-align: top;\n",
1041 |        "    }\n",
1042 |        "\n",
1043 |        "    .dataframe thead th {\n",
1044 |        "        text-align: right;\n",
1045 |        "    }\n",
1046 |        "</style>\n",
1047 |        "<table border=\"1\" class=\"dataframe\">\n",
1048 |        "  <thead>\n",
1049 |        "    <tr style=\"text-align: right;\">\n",
1050 |        "      <th></th>\n",
1051 |        "      <th>Make</th>\n",
1052 |        "      <th>Color</th>\n",
1053 |        "      <th>Capacity</th>\n",
1054 |        "    </tr>\n",
1055 |        "  </thead>\n",
1056 |        "  <tbody>\n",
1057 |        "    <tr>\n",
1058 |        "      <th>John</th>\n",
1059 |        "      <td>BMW</td>\n",
1060 |        "      <td>Grey</td>\n",
1061 |        "      <td>2.0l</td>\n",
1062 |        "    </tr>\n",
1063 |        "  </tbody>\n",
1064 |        "</table>\n",
1065 |        "</div>"
1066 |       ],
1067 |       "text/plain": [
1068 |        "     Make Color Capacity\n",
1069 |        "John  BMW  Grey     2.0l"
1070 |       ]
1071 |      },
1072 |      "execution_count": 18,
1073 |      "metadata": {},
1074 |      "output_type": "execute_result"
1075 |     }
1076 |    ],
1077 |    "source": [
1078 |     "df[~df[\"Color\"].isin(required_vals)]   # Use the handy ~ notation to change it to \"isnotin\"!"
1079 |    ]
1080 |   },
1081 |   {
1082 |    "cell_type": "code",
1083 |    "execution_count": 25,
1084 |    "metadata": {},
1085 |    "outputs": [
1086 |     {
1087 |      "data": {
1088 |       "text/html": [
1089 |        "<div>\n",
1090 |        "<style scoped>\n",
1091 |        "    .dataframe tbody tr th:only-of-type {\n",
1092 |        "        vertical-align: middle;\n",
1093 |        "    }\n",
1094 |        "\n",
1095 |        "    .dataframe tbody tr th {\n",
1096 |        "        vertical-align: top;\n",
1097 |        "    }\n",
1098 |        "\n",
1099 |        "    .dataframe thead th {\n",
1100 |        "        text-align: right;\n",
1101 |        "    }\n",
1102 |        "</style>\n",
1103 |        "<table border=\"1\" class=\"dataframe\">\n",
1104 |        "  <thead>\n",
1105 |        "    <tr style=\"text-align: right;\">\n",
1106 |        "      <th></th>\n",
1107 |        "      <th>Make</th>\n",
1108 |        "      <th>Color</th>\n",
1109 |        "      <th>Capacity</th>\n",
1110 |        "    </tr>\n",
1111 |        "  </thead>\n",
1112 |        "  <tbody>\n",
1113 |        "    <tr>\n",
1114 |        "      <th>Jim</th>\n",
1115 |        "      <td>Mercedes</td>\n",
1116 |        "      <td>White</td>\n",
1117 |        "      <td>2.2l</td>\n",
1118 |        "    </tr>\n",
1119 |        "    <tr>\n",
1120 |        "      <th>Jay</th>\n",
1121 |        "      <td>Toyota</td>\n",
1122 |        "      <td>White</td>\n",
1123 |        "      <td>1.2l</td>\n",
1124 |        "    </tr>\n",
1125 |        "  </tbody>\n",
1126 |        "</table>\n",
1127 |        "</div>"
1128 |       ],
1129 |       "text/plain": [
1130 |        "         Make  Color Capacity\n",
1131 |        "Jim  Mercedes  White     2.2l\n",
1132 |        "Jay    Toyota  White     1.2l"
1133 |       ]
1134 |      },
1135 |      "execution_count": 25,
1136 |      "metadata": {},
1137 |      "output_type": "execute_result"
1138 |     }
1139 |    ],
1140 |    "source": [
1141 |     "df[df.Color.str.contains(\"te\")]"
1142 |    ]
1143 |   }
1144 |  ],
1145 |  "metadata": {
1146 |   "kernelspec": {
1147 |    "display_name": "Python 3",
1148 |    "language": "python",
1149 |    "name": "python3"
1150 |   },
1151 |   "language_info": {
1152 |    "codemirror_mode": {
1153 |     "name": "ipython",
1154 |     "version": 3
1155 |    },
1156 |    "file_extension": ".py",
1157 |    "mimetype": "text/x-python",
1158 |    "name": "python",
1159 |    "nbconvert_exporter": "python",
1160 |    "pygments_lexer": "ipython3",
1161 |    "version": "3.6.5"
1162 |   }
1163 |  },
1164 |  "nbformat": 4,
1165 |  "nbformat_minor": 2
1166 | }
1167 | 


--------------------------------------------------------------------------------
/How it works - Pandas, groupby method.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### import pandas as pd"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 2,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "names = [\"Jane\", \"June\", \"Jenny\", \"Jacky\", \"Johnny\", \"Jack\", \"Jeremy\"]\n",
 17 |     "cars = [\"Ford\", \"Fiat\", \"Ford\", \"Ford\", \"BMW\", \"Mercedes\", \"Honda\"]\n",
 18 |     "colors = [\"black\", \"blue\", \"white\", \"white\", \"white\", \"white\", \"blue\"]"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 3,
 24 |    "metadata": {
 25 |     "scrolled": true
 26 |    },
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "df = pd.DataFrame(data = {\"Names\": names, \"Cars\": cars, \"Colours\": colors})\n",
 30 |     "df = df[[\"Names\", \"Cars\", \"Colours\"]]"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 4,
 36 |    "metadata": {},
 37 |    "outputs": [
 38 |     {
 39 |      "data": {
 40 |       "text/html": [
 41 |        "<div>\n",
 42 |        "<style scoped>\n",
 43 |        "    .dataframe tbody tr th:only-of-type {\n",
 44 |        "        vertical-align: middle;\n",
 45 |        "    }\n",
 46 |        "\n",
 47 |        "    .dataframe tbody tr th {\n",
 48 |        "        vertical-align: top;\n",
 49 |        "    }\n",
 50 |        "\n",
 51 |        "    .dataframe thead th {\n",
 52 |        "        text-align: right;\n",
 53 |        "    }\n",
 54 |        "</style>\n",
 55 |        "<table border=\"1\" class=\"dataframe\">\n",
 56 |        "  <thead>\n",
 57 |        "    <tr style=\"text-align: right;\">\n",
 58 |        "      <th></th>\n",
 59 |        "      <th>Names</th>\n",
 60 |        "      <th>Cars</th>\n",
 61 |        "      <th>Colours</th>\n",
 62 |        "    </tr>\n",
 63 |        "  </thead>\n",
 64 |        "  <tbody>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>0</th>\n",
 67 |        "      <td>Jane</td>\n",
 68 |        "      <td>Ford</td>\n",
 69 |        "      <td>black</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>1</th>\n",
 73 |        "      <td>June</td>\n",
 74 |        "      <td>Fiat</td>\n",
 75 |        "      <td>blue</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>2</th>\n",
 79 |        "      <td>Jenny</td>\n",
 80 |        "      <td>Ford</td>\n",
 81 |        "      <td>white</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>3</th>\n",
 85 |        "      <td>Jacky</td>\n",
 86 |        "      <td>Ford</td>\n",
 87 |        "      <td>white</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>4</th>\n",
 91 |        "      <td>Johnny</td>\n",
 92 |        "      <td>BMW</td>\n",
 93 |        "      <td>white</td>\n",
 94 |        "    </tr>\n",
 95 |        "    <tr>\n",
 96 |        "      <th>5</th>\n",
 97 |        "      <td>Jack</td>\n",
 98 |        "      <td>Mercedes</td>\n",
 99 |        "      <td>white</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>6</th>\n",
103 |        "      <td>Jeremy</td>\n",
104 |        "      <td>Honda</td>\n",
105 |        "      <td>blue</td>\n",
106 |        "    </tr>\n",
107 |        "  </tbody>\n",
108 |        "</table>\n",
109 |        "</div>"
110 |       ],
111 |       "text/plain": [
112 |        "    Names      Cars Colours\n",
113 |        "0    Jane      Ford   black\n",
114 |        "1    June      Fiat    blue\n",
115 |        "2   Jenny      Ford   white\n",
116 |        "3   Jacky      Ford   white\n",
117 |        "4  Johnny       BMW   white\n",
118 |        "5    Jack  Mercedes   white\n",
119 |        "6  Jeremy     Honda    blue"
120 |       ]
121 |      },
122 |      "execution_count": 4,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     }
126 |    ],
127 |    "source": [
128 |     "df"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 5,
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": [
137 |     "summary = df.groupby(by = [\"Cars\", \"Colours\"])[\"Names\"].count()"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 11,
143 |    "metadata": {},
144 |    "outputs": [
145 |     {
146 |      "data": {
147 |       "text/plain": [
148 |        "Cars      Colours\n",
149 |        "BMW       white      1\n",
150 |        "Fiat      blue       1\n",
151 |        "Ford      black      1\n",
152 |        "          white      2\n",
153 |        "Honda     blue       1\n",
154 |        "Mercedes  white      1\n",
155 |        "Name: Names, dtype: int64"
156 |       ]
157 |      },
158 |      "execution_count": 11,
159 |      "metadata": {},
160 |      "output_type": "execute_result"
161 |     }
162 |    ],
163 |    "source": [
164 |     "summary"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 13,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "data": {
174 |       "text/plain": [
175 |        "Cars  Colours\n",
176 |        "BMW   white      1\n",
177 |        "Ford  white      2\n",
178 |        "Name: Names, dtype: int64"
179 |       ]
180 |      },
181 |      "execution_count": 13,
182 |      "metadata": {},
183 |      "output_type": "execute_result"
184 |     }
185 |    ],
186 |    "source": [
187 |     "summary.loc[[('BMW', 'white'), ('Ford', 'white')]]"
188 |    ]
189 |   },
190 |   {
191 |    "cell_type": "code",
192 |    "execution_count": 36,
193 |    "metadata": {},
194 |    "outputs": [
195 |     {
196 |      "data": {
197 |       "text/html": [
198 |        "<div>\n",
199 |        "<style scoped>\n",
200 |        "    .dataframe tbody tr th:only-of-type {\n",
201 |        "        vertical-align: middle;\n",
202 |        "    }\n",
203 |        "\n",
204 |        "    .dataframe tbody tr th {\n",
205 |        "        vertical-align: top;\n",
206 |        "    }\n",
207 |        "\n",
208 |        "    .dataframe thead th {\n",
209 |        "        text-align: right;\n",
210 |        "    }\n",
211 |        "</style>\n",
212 |        "<table border=\"1\" class=\"dataframe\">\n",
213 |        "  <thead>\n",
214 |        "    <tr style=\"text-align: right;\">\n",
215 |        "      <th></th>\n",
216 |        "      <th>Cars</th>\n",
217 |        "      <th>Colours</th>\n",
218 |        "      <th>Names</th>\n",
219 |        "    </tr>\n",
220 |        "  </thead>\n",
221 |        "  <tbody>\n",
222 |        "    <tr>\n",
223 |        "      <th>0</th>\n",
224 |        "      <td>BMW</td>\n",
225 |        "      <td>white</td>\n",
226 |        "      <td>1</td>\n",
227 |        "    </tr>\n",
228 |        "    <tr>\n",
229 |        "      <th>1</th>\n",
230 |        "      <td>Fiat</td>\n",
231 |        "      <td>blue</td>\n",
232 |        "      <td>1</td>\n",
233 |        "    </tr>\n",
234 |        "    <tr>\n",
235 |        "      <th>2</th>\n",
236 |        "      <td>Ford</td>\n",
237 |        "      <td>black</td>\n",
238 |        "      <td>1</td>\n",
239 |        "    </tr>\n",
240 |        "    <tr>\n",
241 |        "      <th>3</th>\n",
242 |        "      <td>Ford</td>\n",
243 |        "      <td>white</td>\n",
244 |        "      <td>2</td>\n",
245 |        "    </tr>\n",
246 |        "    <tr>\n",
247 |        "      <th>4</th>\n",
248 |        "      <td>Honda</td>\n",
249 |        "      <td>blue</td>\n",
250 |        "      <td>1</td>\n",
251 |        "    </tr>\n",
252 |        "    <tr>\n",
253 |        "      <th>5</th>\n",
254 |        "      <td>Mercedes</td>\n",
255 |        "      <td>white</td>\n",
256 |        "      <td>1</td>\n",
257 |        "    </tr>\n",
258 |        "  </tbody>\n",
259 |        "</table>\n",
260 |        "</div>"
261 |       ],
262 |       "text/plain": [
263 |        "       Cars Colours  Names\n",
264 |        "0       BMW   white      1\n",
265 |        "1      Fiat    blue      1\n",
266 |        "2      Ford   black      1\n",
267 |        "3      Ford   white      2\n",
268 |        "4     Honda    blue      1\n",
269 |        "5  Mercedes   white      1"
270 |       ]
271 |      },
272 |      "execution_count": 36,
273 |      "metadata": {},
274 |      "output_type": "execute_result"
275 |     }
276 |    ],
277 |    "source": [
278 |     "summary.reset_index(level=[\"Cars\", \"Colours\"])"
279 |    ]
280 |   }
281 |  ],
282 |  "metadata": {
283 |   "kernelspec": {
284 |    "display_name": "Python 3",
285 |    "language": "python",
286 |    "name": "python3"
287 |   },
288 |   "language_info": {
289 |    "codemirror_mode": {
290 |     "name": "ipython",
291 |     "version": 3
292 |    },
293 |    "file_extension": ".py",
294 |    "mimetype": "text/x-python",
295 |    "name": "python",
296 |    "nbconvert_exporter": "python",
297 |    "pygments_lexer": "ipython3",
298 |    "version": "3.6.5"
299 |   }
300 |  },
301 |  "nbformat": 4,
302 |  "nbformat_minor": 2
303 | }
304 | 


--------------------------------------------------------------------------------
/How it works - Pandas, mapping series values.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 2,
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "names = [\"Liza\", \"Lisa\", \"Lizzy\", \"Lynne\", \"Lisbeth\", \"Lana\"]\n",
 19 |     "sizes = [16, 12, 14, 10, 8, 14]"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 3,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "s_name = pd.Series(names)"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 4,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/plain": [
 39 |        "0       Liza\n",
 40 |        "1       Lisa\n",
 41 |        "2      Lizzy\n",
 42 |        "3      Lynne\n",
 43 |        "4    Lisbeth\n",
 44 |        "5       Lana\n",
 45 |        "dtype: object"
 46 |       ]
 47 |      },
 48 |      "execution_count": 4,
 49 |      "metadata": {},
 50 |      "output_type": "execute_result"
 51 |     }
 52 |    ],
 53 |    "source": [
 54 |     "s_name"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 5,
 60 |    "metadata": {},
 61 |    "outputs": [],
 62 |    "source": [
 63 |     "s_sizes = pd.Series(sizes, names)"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 6,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "data": {
 73 |       "text/plain": [
 74 |        "Liza       16\n",
 75 |        "Lisa       12\n",
 76 |        "Lizzy      14\n",
 77 |        "Lynne      10\n",
 78 |        "Lisbeth     8\n",
 79 |        "Lana       14\n",
 80 |        "dtype: int64"
 81 |       ]
 82 |      },
 83 |      "execution_count": 6,
 84 |      "metadata": {},
 85 |      "output_type": "execute_result"
 86 |     }
 87 |    ],
 88 |    "source": [
 89 |     "s_sizes"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 10,
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "text/plain": [
100 |        "0    16\n",
101 |        "1    12\n",
102 |        "2    14\n",
103 |        "3    10\n",
104 |        "4     8\n",
105 |        "5    14\n",
106 |        "dtype: int64"
107 |       ]
108 |      },
109 |      "execution_count": 10,
110 |      "metadata": {},
111 |      "output_type": "execute_result"
112 |     }
113 |    ],
114 |    "source": [
115 |     "s_name.map(s_sizes)"
116 |    ]
117 |   }
118 |  ],
119 |  "metadata": {
120 |   "kernelspec": {
121 |    "display_name": "Python 3",
122 |    "language": "python",
123 |    "name": "python3"
124 |   },
125 |   "language_info": {
126 |    "codemirror_mode": {
127 |     "name": "ipython",
128 |     "version": 3
129 |    },
130 |    "file_extension": ".py",
131 |    "mimetype": "text/x-python",
132 |    "name": "python",
133 |    "nbconvert_exporter": "python",
134 |    "pygments_lexer": "ipython3",
135 |    "version": "3.6.5"
136 |   }
137 |  },
138 |  "nbformat": 4,
139 |  "nbformat_minor": 2
140 | }
141 | 


--------------------------------------------------------------------------------
/How it works - Pandas, merge method.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 2,
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "mothers = [\"Evelyn\", \"Eve\", \"Eugenia\", \"Elizabeth\"]\n",
 19 |     "daughters = [\"Jenny\", \"Joan\", \"June\", \"Julia\"]"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 3,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "kids1 = [\"Jenny\", \"Jenny\", \"Joan\", \"June\", \"Julia\", \"Julia\", \"Julia\"]\n",
 29 |     "kids2 = [\"Freddy\", \"Johnny\", \"Michael\", \"Robin\", \"Nadia\", \"Freddy\", \"Mark\"]"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 5,
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "senior = pd.DataFrame(data = {\"Grandmothers\": mothers, \"Mothers\": daughters})"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 16,
 44 |    "metadata": {},
 45 |    "outputs": [],
 46 |    "source": [
 47 |     "junior = pd.DataFrame(data = {\"Mothers\": kids1, \"Children\": kids2})\n",
 48 |     "junior = junior[[\"Mothers\", \"Children\"]]"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": 17,
 54 |    "metadata": {},
 55 |    "outputs": [
 56 |     {
 57 |      "data": {
 58 |       "text/html": [
 59 |        "<div>\n",
 60 |        "<style scoped>\n",
 61 |        "    .dataframe tbody tr th:only-of-type {\n",
 62 |        "        vertical-align: middle;\n",
 63 |        "    }\n",
 64 |        "\n",
 65 |        "    .dataframe tbody tr th {\n",
 66 |        "        vertical-align: top;\n",
 67 |        "    }\n",
 68 |        "\n",
 69 |        "    .dataframe thead th {\n",
 70 |        "        text-align: right;\n",
 71 |        "    }\n",
 72 |        "</style>\n",
 73 |        "<table border=\"1\" class=\"dataframe\">\n",
 74 |        "  <thead>\n",
 75 |        "    <tr style=\"text-align: right;\">\n",
 76 |        "      <th></th>\n",
 77 |        "      <th>Grandmothers</th>\n",
 78 |        "      <th>Mothers</th>\n",
 79 |        "    </tr>\n",
 80 |        "  </thead>\n",
 81 |        "  <tbody>\n",
 82 |        "    <tr>\n",
 83 |        "      <th>0</th>\n",
 84 |        "      <td>Evelyn</td>\n",
 85 |        "      <td>Jenny</td>\n",
 86 |        "    </tr>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>1</th>\n",
 89 |        "      <td>Eve</td>\n",
 90 |        "      <td>Joan</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>2</th>\n",
 94 |        "      <td>Eugenia</td>\n",
 95 |        "      <td>June</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>3</th>\n",
 99 |        "      <td>Elizabeth</td>\n",
100 |        "      <td>Julia</td>\n",
101 |        "    </tr>\n",
102 |        "  </tbody>\n",
103 |        "</table>\n",
104 |        "</div>"
105 |       ],
106 |       "text/plain": [
107 |        "  Grandmothers Mothers\n",
108 |        "0       Evelyn   Jenny\n",
109 |        "1          Eve    Joan\n",
110 |        "2      Eugenia    June\n",
111 |        "3    Elizabeth   Julia"
112 |       ]
113 |      },
114 |      "execution_count": 17,
115 |      "metadata": {},
116 |      "output_type": "execute_result"
117 |     }
118 |    ],
119 |    "source": [
120 |     "senior"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 18,
126 |    "metadata": {},
127 |    "outputs": [
128 |     {
129 |      "data": {
130 |       "text/html": [
131 |        "<div>\n",
132 |        "<style scoped>\n",
133 |        "    .dataframe tbody tr th:only-of-type {\n",
134 |        "        vertical-align: middle;\n",
135 |        "    }\n",
136 |        "\n",
137 |        "    .dataframe tbody tr th {\n",
138 |        "        vertical-align: top;\n",
139 |        "    }\n",
140 |        "\n",
141 |        "    .dataframe thead th {\n",
142 |        "        text-align: right;\n",
143 |        "    }\n",
144 |        "</style>\n",
145 |        "<table border=\"1\" class=\"dataframe\">\n",
146 |        "  <thead>\n",
147 |        "    <tr style=\"text-align: right;\">\n",
148 |        "      <th></th>\n",
149 |        "      <th>Mothers</th>\n",
150 |        "      <th>Children</th>\n",
151 |        "    </tr>\n",
152 |        "  </thead>\n",
153 |        "  <tbody>\n",
154 |        "    <tr>\n",
155 |        "      <th>0</th>\n",
156 |        "      <td>Jenny</td>\n",
157 |        "      <td>Freddy</td>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <th>1</th>\n",
161 |        "      <td>Jenny</td>\n",
162 |        "      <td>Johnny</td>\n",
163 |        "    </tr>\n",
164 |        "    <tr>\n",
165 |        "      <th>2</th>\n",
166 |        "      <td>Joan</td>\n",
167 |        "      <td>Michael</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>3</th>\n",
171 |        "      <td>June</td>\n",
172 |        "      <td>Robin</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>4</th>\n",
176 |        "      <td>Julia</td>\n",
177 |        "      <td>Nadia</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>5</th>\n",
181 |        "      <td>Julia</td>\n",
182 |        "      <td>Freddy</td>\n",
183 |        "    </tr>\n",
184 |        "    <tr>\n",
185 |        "      <th>6</th>\n",
186 |        "      <td>Julia</td>\n",
187 |        "      <td>Mark</td>\n",
188 |        "    </tr>\n",
189 |        "  </tbody>\n",
190 |        "</table>\n",
191 |        "</div>"
192 |       ],
193 |       "text/plain": [
194 |        "  Mothers Children\n",
195 |        "0   Jenny   Freddy\n",
196 |        "1   Jenny   Johnny\n",
197 |        "2    Joan  Michael\n",
198 |        "3    June    Robin\n",
199 |        "4   Julia    Nadia\n",
200 |        "5   Julia   Freddy\n",
201 |        "6   Julia     Mark"
202 |       ]
203 |      },
204 |      "execution_count": 18,
205 |      "metadata": {},
206 |      "output_type": "execute_result"
207 |     }
208 |    ],
209 |    "source": [
210 |     "junior"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": 19,
216 |    "metadata": {},
217 |    "outputs": [],
218 |    "source": [
219 |     "master = pd.merge(senior, junior, how = \"right\")"
220 |    ]
221 |   },
222 |   {
223 |    "cell_type": "code",
224 |    "execution_count": 21,
225 |    "metadata": {},
226 |    "outputs": [
227 |     {
228 |      "data": {
229 |       "text/html": [
230 |        "<div>\n",
231 |        "<style scoped>\n",
232 |        "    .dataframe tbody tr th:only-of-type {\n",
233 |        "        vertical-align: middle;\n",
234 |        "    }\n",
235 |        "\n",
236 |        "    .dataframe tbody tr th {\n",
237 |        "        vertical-align: top;\n",
238 |        "    }\n",
239 |        "\n",
240 |        "    .dataframe thead th {\n",
241 |        "        text-align: right;\n",
242 |        "    }\n",
243 |        "</style>\n",
244 |        "<table border=\"1\" class=\"dataframe\">\n",
245 |        "  <thead>\n",
246 |        "    <tr style=\"text-align: right;\">\n",
247 |        "      <th></th>\n",
248 |        "      <th>Grandmothers</th>\n",
249 |        "      <th>Mothers</th>\n",
250 |        "      <th>Children</th>\n",
251 |        "    </tr>\n",
252 |        "  </thead>\n",
253 |        "  <tbody>\n",
254 |        "    <tr>\n",
255 |        "      <th>0</th>\n",
256 |        "      <td>Evelyn</td>\n",
257 |        "      <td>Jenny</td>\n",
258 |        "      <td>Freddy</td>\n",
259 |        "    </tr>\n",
260 |        "    <tr>\n",
261 |        "      <th>1</th>\n",
262 |        "      <td>Evelyn</td>\n",
263 |        "      <td>Jenny</td>\n",
264 |        "      <td>Johnny</td>\n",
265 |        "    </tr>\n",
266 |        "    <tr>\n",
267 |        "      <th>2</th>\n",
268 |        "      <td>Eve</td>\n",
269 |        "      <td>Joan</td>\n",
270 |        "      <td>Michael</td>\n",
271 |        "    </tr>\n",
272 |        "    <tr>\n",
273 |        "      <th>3</th>\n",
274 |        "      <td>Eugenia</td>\n",
275 |        "      <td>June</td>\n",
276 |        "      <td>Robin</td>\n",
277 |        "    </tr>\n",
278 |        "    <tr>\n",
279 |        "      <th>4</th>\n",
280 |        "      <td>Elizabeth</td>\n",
281 |        "      <td>Julia</td>\n",
282 |        "      <td>Nadia</td>\n",
283 |        "    </tr>\n",
284 |        "    <tr>\n",
285 |        "      <th>5</th>\n",
286 |        "      <td>Elizabeth</td>\n",
287 |        "      <td>Julia</td>\n",
288 |        "      <td>Freddy</td>\n",
289 |        "    </tr>\n",
290 |        "    <tr>\n",
291 |        "      <th>6</th>\n",
292 |        "      <td>Elizabeth</td>\n",
293 |        "      <td>Julia</td>\n",
294 |        "      <td>Mark</td>\n",
295 |        "    </tr>\n",
296 |        "  </tbody>\n",
297 |        "</table>\n",
298 |        "</div>"
299 |       ],
300 |       "text/plain": [
301 |        "  Grandmothers Mothers Children\n",
302 |        "0       Evelyn   Jenny   Freddy\n",
303 |        "1       Evelyn   Jenny   Johnny\n",
304 |        "2          Eve    Joan  Michael\n",
305 |        "3      Eugenia    June    Robin\n",
306 |        "4    Elizabeth   Julia    Nadia\n",
307 |        "5    Elizabeth   Julia   Freddy\n",
308 |        "6    Elizabeth   Julia     Mark"
309 |       ]
310 |      },
311 |      "execution_count": 21,
312 |      "metadata": {},
313 |      "output_type": "execute_result"
314 |     }
315 |    ],
316 |    "source": [
317 |     "master"
318 |    ]
319 |   },
320 |   {
321 |    "cell_type": "code",
322 |    "execution_count": null,
323 |    "metadata": {},
324 |    "outputs": [],
325 |    "source": []
326 |   }
327 |  ],
328 |  "metadata": {
329 |   "kernelspec": {
330 |    "display_name": "Python 3",
331 |    "language": "python",
332 |    "name": "python3"
333 |   },
334 |   "language_info": {
335 |    "codemirror_mode": {
336 |     "name": "ipython",
337 |     "version": 3
338 |    },
339 |    "file_extension": ".py",
340 |    "mimetype": "text/x-python",
341 |    "name": "python",
342 |    "nbconvert_exporter": "python",
343 |    "pygments_lexer": "ipython3",
344 |    "version": "3.6.5"
345 |   }
346 |  },
347 |  "nbformat": 4,
348 |  "nbformat_minor": 2
349 | }
350 | 


--------------------------------------------------------------------------------
/How it works - basic lists.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "List = [3, 4, 6, 8, 9, 11, 12, 14, 15, 16, 17, 20, 21]"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stdout",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "[3, 4, 6, 8, 9, 11, 12, 14, 15, 16, 17, 20, 21]\n"
 24 |      ]
 25 |     }
 26 |    ],
 27 |    "source": [
 28 |     "print(List)"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 8,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "name": "stdout",
 38 |      "output_type": "stream",
 39 |      "text": [
 40 |       "3 is a Multiple of 3\n",
 41 |       "4\n",
 42 |       "6 is a Multiple of 3\n",
 43 |       "8\n",
 44 |       "9 is a Multiple of 3\n",
 45 |       "11\n",
 46 |       "12 is a Multiple of 3\n",
 47 |       "14\n",
 48 |       "15 is a Multiple of 3\n",
 49 |       "16\n",
 50 |       "17\n",
 51 |       "20\n",
 52 |       "21 is a Multiple of 3\n"
 53 |      ]
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "for i in List:\n",
 58 |     "    if i % 3 == 0:\n",
 59 |     "        print(i, \"is a Multiple of 3\")\n",
 60 |     "    else:\n",
 61 |     "        print(i)"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 10,
 67 |    "metadata": {},
 68 |    "outputs": [
 69 |     {
 70 |      "name": "stdout",
 71 |      "output_type": "stream",
 72 |      "text": [
 73 |       "[3, 6, 9, 12, 15, 21]\n"
 74 |      ]
 75 |     }
 76 |    ],
 77 |    "source": [
 78 |     "List_of_3 = []\n",
 79 |     "for i in List:\n",
 80 |     "    if i % 3 == 0:\n",
 81 |     "        List_of_3.append(i)\n",
 82 |     "print(List_of_3)"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 11,
 88 |    "metadata": {},
 89 |    "outputs": [
 90 |     {
 91 |      "name": "stdout",
 92 |      "output_type": "stream",
 93 |      "text": [
 94 |       "o\n"
 95 |      ]
 96 |     }
 97 |    ],
 98 |    "source": [
 99 |     "words = \"Hello world\"\n",
100 |     "print(words[4])"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "code",
105 |    "execution_count": 12,
106 |    "metadata": {},
107 |    "outputs": [
108 |     {
109 |      "name": "stdout",
110 |      "output_type": "stream",
111 |      "text": [
112 |       "Hello worldHello worldHello world\n"
113 |      ]
114 |     }
115 |    ],
116 |    "source": [
117 |     "print(words * 3)"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": 13,
123 |    "metadata": {},
124 |    "outputs": [
125 |     {
126 |      "data": {
127 |       "text/plain": [
128 |        "True"
129 |       ]
130 |      },
131 |      "execution_count": 13,
132 |      "metadata": {},
133 |      "output_type": "execute_result"
134 |     }
135 |    ],
136 |    "source": [
137 |     "\"Hello\" in words"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 14,
143 |    "metadata": {},
144 |    "outputs": [
145 |     {
146 |      "data": {
147 |       "text/plain": [
148 |        "False"
149 |       ]
150 |      },
151 |      "execution_count": 14,
152 |      "metadata": {},
153 |      "output_type": "execute_result"
154 |     }
155 |    ],
156 |    "source": [
157 |     "\"Goodbye\" in words"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 15,
163 |    "metadata": {},
164 |    "outputs": [
165 |     {
166 |      "data": {
167 |       "text/plain": [
168 |        "False"
169 |       ]
170 |      },
171 |      "execution_count": 15,
172 |      "metadata": {},
173 |      "output_type": "execute_result"
174 |     }
175 |    ],
176 |    "source": [
177 |     "not \"Hello\" in words"
178 |    ]
179 |   },
180 |   {
181 |    "cell_type": "code",
182 |    "execution_count": 16,
183 |    "metadata": {},
184 |    "outputs": [
185 |     {
186 |      "data": {
187 |       "text/plain": [
188 |        "True"
189 |       ]
190 |      },
191 |      "execution_count": 16,
192 |      "metadata": {},
193 |      "output_type": "execute_result"
194 |     }
195 |    ],
196 |    "source": [
197 |     "not \"Goodbye\" in words"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": null,
203 |    "metadata": {
204 |     "collapsed": true
205 |    },
206 |    "outputs": [],
207 |    "source": []
208 |   }
209 |  ],
210 |  "metadata": {
211 |   "kernelspec": {
212 |    "display_name": "Python 3",
213 |    "language": "python",
214 |    "name": "python3"
215 |   },
216 |   "language_info": {
217 |    "codemirror_mode": {
218 |     "name": "ipython",
219 |     "version": 3
220 |    },
221 |    "file_extension": ".py",
222 |    "mimetype": "text/x-python",
223 |    "name": "python",
224 |    "nbconvert_exporter": "python",
225 |    "pygments_lexer": "ipython3",
226 |    "version": "3.6.5"
227 |   }
228 |  },
229 |  "nbformat": 4,
230 |  "nbformat_minor": 2
231 | }
232 | 


--------------------------------------------------------------------------------
/How it works - list comprehensions.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# Tips from https://www.kaggle.com/colinmorris/learn-python-challenge-day-5\n",
 10 |     "# Thank you https://www.kaggle.com/colinmorris :)"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 8,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "name": "stdout",
 20 |      "output_type": "stream",
 21 |      "text": [
 22 |       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
 23 |      ]
 24 |     }
 25 |    ],
 26 |    "source": [
 27 |     "# Basically creating a list by specifying some rule for its construction\n",
 28 |     "squares = [n**2 for n in range(10)]\n",
 29 |     "print(squares)\n",
 30 |     "# One can see the beauty of this by comparing it to less satisfactory methods below..."
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 9,
 36 |    "metadata": {},
 37 |    "outputs": [],
 38 |    "source": [
 39 |     "# At worst we could have done this\n",
 40 |     "squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 10,
 46 |    "metadata": {},
 47 |    "outputs": [
 48 |     {
 49 |      "name": "stdout",
 50 |      "output_type": "stream",
 51 |      "text": [
 52 |       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
 53 |      ]
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "# And more tediously we could have done this\n",
 58 |     "squares = []\n",
 59 |     "for n in range(10):\n",
 60 |     "    squares.append(n**2)\n",
 61 |     "print(squares)"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 17,
 67 |    "metadata": {},
 68 |    "outputs": [],
 69 |    "source": [
 70 |     "# You can do some nice fancy footwork - let's get an arbitrary list of integers\n",
 71 |     "my_list = [5, -1, -2, 0, 3]"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "code",
 76 |    "execution_count": 24,
 77 |    "metadata": {},
 78 |    "outputs": [
 79 |     {
 80 |      "data": {
 81 |       "text/plain": [
 82 |        "2"
 83 |       ]
 84 |      },
 85 |      "execution_count": 24,
 86 |      "metadata": {},
 87 |      "output_type": "execute_result"
 88 |     }
 89 |    ],
 90 |    "source": [
 91 |     "# I can easily find the negative numbers now - without a loop!\n",
 92 |     "len([num for num in my_list if num < 0])"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 26,
 98 |    "metadata": {},
 99 |    "outputs": [
100 |     {
101 |      "data": {
102 |       "text/plain": [
103 |        "2"
104 |       ]
105 |      },
106 |      "execution_count": 26,
107 |      "metadata": {},
108 |      "output_type": "execute_result"
109 |     }
110 |    ],
111 |    "source": [
112 |     "# There are a couple of options...\n",
113 |     "sum([num < 0 for num in my_list])"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 27,
119 |    "metadata": {},
120 |    "outputs": [],
121 |    "source": [
122 |     "# Some more examples\n",
123 |     "planets = [\"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\", \"Pluto\"]"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "code",
128 |    "execution_count": 28,
129 |    "metadata": {},
130 |    "outputs": [
131 |     {
132 |      "data": {
133 |       "text/plain": [
134 |        "['VENUS!', 'EARTH!', 'MARS!', 'PLUTO!']"
135 |       ]
136 |      },
137 |      "execution_count": 28,
138 |      "metadata": {},
139 |      "output_type": "execute_result"
140 |     }
141 |    ],
142 |    "source": [
143 |     "# Notice how you can write it quite nice and readably\n",
144 |     "[\n",
145 |     "    planet.upper() + '!' \n",
146 |     "    for planet in planets \n",
147 |     "    if len(planet) < 6\n",
148 |     "]"
149 |    ]
150 |   }
151 |  ],
152 |  "metadata": {
153 |   "kernelspec": {
154 |    "display_name": "Python 3",
155 |    "language": "python",
156 |    "name": "python3"
157 |   },
158 |   "language_info": {
159 |    "codemirror_mode": {
160 |     "name": "ipython",
161 |     "version": 3
162 |    },
163 |    "file_extension": ".py",
164 |    "mimetype": "text/x-python",
165 |    "name": "python",
166 |    "nbconvert_exporter": "python",
167 |    "pygments_lexer": "ipython3",
168 |    "version": "3.6.5"
169 |   }
170 |  },
171 |  "nbformat": 4,
172 |  "nbformat_minor": 2
173 | }
174 | 


--------------------------------------------------------------------------------
/How it works - lists vs arrays.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 2,
 15 |    "metadata": {},
 16 |    "outputs": [
 17 |     {
 18 |      "name": "stdout",
 19 |      "output_type": "stream",
 20 |      "text": [
 21 |       "[12, 24, 65, 19, 242]\n"
 22 |      ]
 23 |     }
 24 |    ],
 25 |    "source": [
 26 |     "List1 = [12, 24, 65 , 19, 242]\n",
 27 |     "print(List1)"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 3,
 33 |    "metadata": {},
 34 |    "outputs": [
 35 |     {
 36 |      "name": "stdout",
 37 |      "output_type": "stream",
 38 |      "text": [
 39 |       "[12, 24, 65, 19]\n"
 40 |      ]
 41 |     }
 42 |    ],
 43 |    "source": [
 44 |     "List2 = List1[0:4]\n",
 45 |     "print(List2)"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 4,
 51 |    "metadata": {},
 52 |    "outputs": [],
 53 |    "source": [
 54 |     "List2[0] = 15"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 5,
 60 |    "metadata": {},
 61 |    "outputs": [
 62 |     {
 63 |      "name": "stdout",
 64 |      "output_type": "stream",
 65 |      "text": [
 66 |       "List1 contains:  [12, 24, 65, 19, 242]\n",
 67 |       "List2 contains:  [15, 24, 65, 19]\n"
 68 |      ]
 69 |     }
 70 |    ],
 71 |    "source": [
 72 |     "# Notice that while we changed a value in List2, the value in List2 remains the same\n",
 73 |     "# The 2 lists are independent of one another\n",
 74 |     "print(\"List1 contains: \", List1)\n",
 75 |     "print(\"List2 contains: \", List2)"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 6,
 81 |    "metadata": {},
 82 |    "outputs": [
 83 |     {
 84 |      "name": "stdout",
 85 |      "output_type": "stream",
 86 |      "text": [
 87 |       "[ 12  24  65  19 242]\n"
 88 |      ]
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "Array1 = np.array(List1)\n",
 93 |     "print(Array1)"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 7,
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "name": "stdout",
103 |      "output_type": "stream",
104 |      "text": [
105 |       "[12 24 65 19]\n"
106 |      ]
107 |     }
108 |    ],
109 |    "source": [
110 |     "Array2 = Array1[0:4]\n",
111 |     "print(Array2)"
112 |    ]
113 |   },
114 |   {
115 |    "cell_type": "code",
116 |    "execution_count": 8,
117 |    "metadata": {},
118 |    "outputs": [],
119 |    "source": [
120 |     "Array2[0] = 15"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 9,
126 |    "metadata": {},
127 |    "outputs": [
128 |     {
129 |      "name": "stdout",
130 |      "output_type": "stream",
131 |      "text": [
132 |       "Array1 contains:  [ 15  24  65  19 242]\n",
133 |       "Array2 contains:  [15 24 65 19]\n"
134 |      ]
135 |     }
136 |    ],
137 |    "source": [
138 |     "# Notice that because Array2 came from Array1, they are joined at the hip, so when you change\n",
139 |     "# the value in one, the value in the other immediately changes too!\n",
140 |     "print(\"Array1 contains: \", Array1)\n",
141 |     "print(\"Array2 contains: \", Array2)"
142 |    ]
143 |   },
144 |   {
145 |    "cell_type": "code",
146 |    "execution_count": 10,
147 |    "metadata": {},
148 |    "outputs": [
149 |     {
150 |      "name": "stdout",
151 |      "output_type": "stream",
152 |      "text": [
153 |       "[ 12  24  65  19 242]\n"
154 |      ]
155 |     }
156 |    ],
157 |    "source": [
158 |     "Array1 = np.array(List1)\n",
159 |     "print(Array1)"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 11,
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "name": "stdout",
169 |      "output_type": "stream",
170 |      "text": [
171 |       "[12 24 65 19]\n"
172 |      ]
173 |     }
174 |    ],
175 |    "source": [
176 |     "# If this is not the desired effect use .copy() to make an independent copy\n",
177 |     "Array2 = Array1[0:4].copy()\n",
178 |     "print(Array2)"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 12,
184 |    "metadata": {},
185 |    "outputs": [],
186 |    "source": [
187 |     "Array2[0] = 15"
188 |    ]
189 |   },
190 |   {
191 |    "cell_type": "code",
192 |    "execution_count": 13,
193 |    "metadata": {},
194 |    "outputs": [
195 |     {
196 |      "name": "stdout",
197 |      "output_type": "stream",
198 |      "text": [
199 |       "Array1 contains:  [ 12  24  65  19 242]\n",
200 |       "Array2 contains:  [15 24 65 19]\n"
201 |      ]
202 |     }
203 |    ],
204 |    "source": [
205 |     "# Notice now that although we changed a value in Array2, Array1 has remained unaffected\n",
206 |     "print(\"Array1 contains: \", Array1)\n",
207 |     "print(\"Array2 contains: \", Array2)"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 14,
213 |    "metadata": {},
214 |    "outputs": [],
215 |    "source": [
216 |     "List1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "code",
221 |    "execution_count": 15,
222 |    "metadata": {},
223 |    "outputs": [
224 |     {
225 |      "name": "stdout",
226 |      "output_type": "stream",
227 |      "text": [
228 |       "[[1 2 3]\n",
229 |       " [4 5 6]\n",
230 |       " [7 8 9]]\n",
231 |       "<class 'numpy.ndarray'>\n"
232 |      ]
233 |     }
234 |    ],
235 |    "source": [
236 |     "List_Table = np.reshape(List1, (3,3))\n",
237 |     "print(List_Table)\n",
238 |     "print(type(List_Table))"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": 16,
244 |    "metadata": {},
245 |    "outputs": [
246 |     {
247 |      "name": "stdout",
248 |      "output_type": "stream",
249 |      "text": [
250 |       "3\n",
251 |       "[2 5 8]\n"
252 |      ]
253 |     }
254 |    ],
255 |    "source": [
256 |     "print(List_Table[0,2])\n",
257 |     "print(List_Table[:,1])"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "code",
262 |    "execution_count": 17,
263 |    "metadata": {},
264 |    "outputs": [],
265 |    "source": [
266 |     "#Object oriented programming"
267 |    ]
268 |   },
269 |   {
270 |    "cell_type": "code",
271 |    "execution_count": 18,
272 |    "metadata": {},
273 |    "outputs": [
274 |     {
275 |      "data": {
276 |       "text/plain": [
277 |        "array([[1, 2, 3],\n",
278 |        "       [4, 5, 6],\n",
279 |        "       [7, 8, 9]])"
280 |       ]
281 |      },
282 |      "execution_count": 18,
283 |      "metadata": {},
284 |      "output_type": "execute_result"
285 |     }
286 |    ],
287 |    "source": [
288 |     "List1.reshape(3,3)"
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "code",
293 |    "execution_count": 19,
294 |    "metadata": {},
295 |    "outputs": [
296 |     {
297 |      "name": "stdout",
298 |      "output_type": "stream",
299 |      "text": [
300 |       "[[1 2 3]\n",
301 |       " [4 5 6]\n",
302 |       " [7 8 9]]\n"
303 |      ]
304 |     }
305 |    ],
306 |    "source": [
307 |     "print(List1.reshape(3,3))"
308 |    ]
309 |   },
310 |   {
311 |    "cell_type": "code",
312 |    "execution_count": 20,
313 |    "metadata": {},
314 |    "outputs": [],
315 |    "source": [
316 |     "#We played Scrabble in Jan, Feb & Mar, here are the scores:"
317 |    ]
318 |   },
319 |   {
320 |    "cell_type": "code",
321 |    "execution_count": 21,
322 |    "metadata": {},
323 |    "outputs": [],
324 |    "source": [
325 |     "Lisa_Mitford = [108, 215, 99]\n",
326 |     "Barry_Benjamin = [260, 212, 220]\n",
327 |     "Geoff_Louw = [176, 98, 232]\n",
328 |     "Denise_Louw = [102, 89, 276]"
329 |    ]
330 |   },
331 |   {
332 |    "cell_type": "code",
333 |    "execution_count": 22,
334 |    "metadata": {},
335 |    "outputs": [],
336 |    "source": [
337 |     "#Insert this lot into an array"
338 |    ]
339 |   },
340 |   {
341 |    "cell_type": "code",
342 |    "execution_count": 23,
343 |    "metadata": {},
344 |    "outputs": [
345 |     {
346 |      "name": "stdout",
347 |      "output_type": "stream",
348 |      "text": [
349 |       "[[108 215  99]\n",
350 |       " [260 212 220]\n",
351 |       " [176  98 232]\n",
352 |       " [102  89 276]]\n"
353 |      ]
354 |     }
355 |    ],
356 |    "source": [
357 |     "Scores = np.array([Lisa_Mitford, Barry_Benjamin, Geoff_Louw, Denise_Louw])\n",
358 |     "print(Scores)"
359 |    ]
360 |   },
361 |   {
362 |    "cell_type": "code",
363 |    "execution_count": 24,
364 |    "metadata": {},
365 |    "outputs": [],
366 |    "source": [
367 |     "#Create a player dictionary"
368 |    ]
369 |   },
370 |   {
371 |    "cell_type": "code",
372 |    "execution_count": 25,
373 |    "metadata": {},
374 |    "outputs": [],
375 |    "source": [
376 |     "PDict = {'Lisa_Mitford':0, 'Barry_Benjamin':1, 'Geoff_Louw':2, 'Denise_Louw':3}"
377 |    ]
378 |   },
379 |   {
380 |    "cell_type": "code",
381 |    "execution_count": 26,
382 |    "metadata": {},
383 |    "outputs": [],
384 |    "source": [
385 |     "#Create a months dictionary\n",
386 |     "MDict = {'Jan':0, 'Feb':1, 'Mar':2}"
387 |    ]
388 |   },
389 |   {
390 |    "cell_type": "code",
391 |    "execution_count": 27,
392 |    "metadata": {},
393 |    "outputs": [],
394 |    "source": [
395 |     "#To retrieve an individual item from the matrix"
396 |    ]
397 |   },
398 |   {
399 |    "cell_type": "code",
400 |    "execution_count": 28,
401 |    "metadata": {},
402 |    "outputs": [
403 |     {
404 |      "name": "stdout",
405 |      "output_type": "stream",
406 |      "text": [
407 |       "232\n"
408 |      ]
409 |     }
410 |    ],
411 |    "source": [
412 |     "print(Scores[PDict['Geoff_Louw'], MDict['Mar']])"
413 |    ]
414 |   },
415 |   {
416 |    "cell_type": "code",
417 |    "execution_count": 29,
418 |    "metadata": {},
419 |    "outputs": [],
420 |    "source": [
421 |     "#To retrieve a whole row from the matrix"
422 |    ]
423 |   },
424 |   {
425 |    "cell_type": "code",
426 |    "execution_count": 30,
427 |    "metadata": {},
428 |    "outputs": [
429 |     {
430 |      "name": "stdout",
431 |      "output_type": "stream",
432 |      "text": [
433 |       "[176  98 232]\n"
434 |      ]
435 |     }
436 |    ],
437 |    "source": [
438 |     "print(Scores[PDict['Geoff_Louw']])"
439 |    ]
440 |   },
441 |   {
442 |    "cell_type": "code",
443 |    "execution_count": 31,
444 |    "metadata": {},
445 |    "outputs": [
446 |     {
447 |      "name": "stdout",
448 |      "output_type": "stream",
449 |      "text": [
450 |       "[ 99 220 232 276]\n"
451 |      ]
452 |     }
453 |    ],
454 |    "source": [
455 |     "#Or to retrieve a whole column from the matrix\n",
456 |     "print(Scores[:,MDict['Mar']])"
457 |    ]
458 |   }
459 |  ],
460 |  "metadata": {
461 |   "kernelspec": {
462 |    "display_name": "Python 3",
463 |    "language": "python",
464 |    "name": "python3"
465 |   },
466 |   "language_info": {
467 |    "codemirror_mode": {
468 |     "name": "ipython",
469 |     "version": 3
470 |    },
471 |    "file_extension": ".py",
472 |    "mimetype": "text/x-python",
473 |    "name": "python",
474 |    "nbconvert_exporter": "python",
475 |    "pygments_lexer": "ipython3",
476 |    "version": "3.6.5"
477 |   }
478 |  },
479 |  "nbformat": 4,
480 |  "nbformat_minor": 2
481 | }
482 | 


--------------------------------------------------------------------------------
/How it works - positive & negative indexation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# Let's start with the basics"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 2,
 15 |    "metadata": {},
 16 |    "outputs": [
 17 |     {
 18 |      "data": {
 19 |       "text/plain": [
 20 |        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]"
 21 |       ]
 22 |      },
 23 |      "execution_count": 2,
 24 |      "metadata": {},
 25 |      "output_type": "execute_result"
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "simple = list(range(1,19))\n",
 30 |     "simple"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 3,
 36 |    "metadata": {},
 37 |    "outputs": [
 38 |     {
 39 |      "data": {
 40 |       "text/plain": [
 41 |        "1"
 42 |       ]
 43 |      },
 44 |      "execution_count": 3,
 45 |      "metadata": {},
 46 |      "output_type": "execute_result"
 47 |     }
 48 |    ],
 49 |    "source": [
 50 |     "# Select the 1st item using positive indexation\n",
 51 |     "simple[0]"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 4,
 57 |    "metadata": {},
 58 |    "outputs": [
 59 |     {
 60 |      "data": {
 61 |       "text/plain": [
 62 |        "1"
 63 |       ]
 64 |      },
 65 |      "execution_count": 4,
 66 |      "metadata": {},
 67 |      "output_type": "execute_result"
 68 |     }
 69 |    ],
 70 |    "source": [
 71 |     "# Select the 1st item using negative indexation\n",
 72 |     "simple[-18]"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 5,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "data": {
 82 |       "text/plain": [
 83 |        "18"
 84 |       ]
 85 |      },
 86 |      "execution_count": 5,
 87 |      "metadata": {},
 88 |      "output_type": "execute_result"
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "# Select the last item using positive indexation\n",
 93 |     "simple[17]"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 6,
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "data": {
103 |       "text/plain": [
104 |        "18"
105 |       ]
106 |      },
107 |      "execution_count": 6,
108 |      "metadata": {},
109 |      "output_type": "execute_result"
110 |     }
111 |    ],
112 |    "source": [
113 |     "# Select the last item using negative indexation\n",
114 |     "simple[-1]"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": 7,
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/plain": [
125 |        "[1, 2, 3, 4, 5, 6, 7]"
126 |       ]
127 |      },
128 |      "execution_count": 7,
129 |      "metadata": {},
130 |      "output_type": "execute_result"
131 |     }
132 |    ],
133 |    "source": [
134 |     "# Select a range of items with positive indexation\n",
135 |     "simple[0:7]"
136 |    ]
137 |   },
138 |   {
139 |    "cell_type": "code",
140 |    "execution_count": 8,
141 |    "metadata": {},
142 |    "outputs": [
143 |     {
144 |      "data": {
145 |       "text/plain": [
146 |        "[1, 2, 3, 4, 5, 6, 7]"
147 |       ]
148 |      },
149 |      "execution_count": 8,
150 |      "metadata": {},
151 |      "output_type": "execute_result"
152 |     }
153 |    ],
154 |    "source": [
155 |     "# Select a range of items with negative indexation\n",
156 |     "simple[-18:-11]"
157 |    ]
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": 9,
162 |    "metadata": {},
163 |    "outputs": [
164 |     {
165 |      "data": {
166 |       "text/plain": [
167 |        "[2, 4, 6]"
168 |       ]
169 |      },
170 |      "execution_count": 9,
171 |      "metadata": {},
172 |      "output_type": "execute_result"
173 |     }
174 |    ],
175 |    "source": [
176 |     "# Select a range of items between 1 and 7 in increments of 2\n",
177 |     "simple[1:7:2]"
178 |    ]
179 |   },
180 |   {
181 |    "cell_type": "code",
182 |    "execution_count": 10,
183 |    "metadata": {},
184 |    "outputs": [
185 |     {
186 |      "data": {
187 |       "text/plain": [
188 |        "[6, 4, 2]"
189 |       ]
190 |      },
191 |      "execution_count": 10,
192 |      "metadata": {},
193 |      "output_type": "execute_result"
194 |     }
195 |    ],
196 |    "source": [
197 |     "# Select the same range of items between 1 and 7 in increments of -2 (backwards)\n",
198 |     "simple[-13:-18:-2]"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "code",
203 |    "execution_count": 11,
204 |    "metadata": {},
205 |    "outputs": [
206 |     {
207 |      "data": {
208 |       "text/plain": [
209 |        "[]"
210 |       ]
211 |      },
212 |      "execution_count": 11,
213 |      "metadata": {},
214 |      "output_type": "execute_result"
215 |     }
216 |    ],
217 |    "source": [
218 |     "# Note how the step increment makes a difference to the order - this doesn't work because it says start at 1, \n",
219 |     "# go on until 7 and use increments of negative 2 but if we do negative 2 from 1 we get immediately outside\n",
220 |     "# the bounds of our list\n",
221 |     "simple[1:7:-2]"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": 12,
227 |    "metadata": {},
228 |    "outputs": [
229 |     {
230 |      "data": {
231 |       "text/plain": [
232 |        "[]"
233 |       ]
234 |      },
235 |      "execution_count": 12,
236 |      "metadata": {},
237 |      "output_type": "execute_result"
238 |     }
239 |    ],
240 |    "source": [
241 |     "# Similarly here we are saying start at -18 and go forwards by 2 which again puts us immediately\n",
242 |     "# outside the bounds of our list\n",
243 |     "simple[-13:-18:2]"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": 13,
249 |    "metadata": {},
250 |    "outputs": [],
251 |    "source": [
252 |     "# Now replace a list item with a new value (6 > 99)\n",
253 |     "simple[-13] = 99"
254 |    ]
255 |   },
256 |   {
257 |    "cell_type": "code",
258 |    "execution_count": 14,
259 |    "metadata": {},
260 |    "outputs": [
261 |     {
262 |      "data": {
263 |       "text/plain": [
264 |        "[99, 4, 2]"
265 |       ]
266 |      },
267 |      "execution_count": 14,
268 |      "metadata": {},
269 |      "output_type": "execute_result"
270 |     }
271 |    ],
272 |    "source": [
273 |     "# And check what it looks like now\n",
274 |     "simple[-13:-18:-2]"
275 |    ]
276 |   },
277 |   {
278 |    "cell_type": "code",
279 |    "execution_count": 15,
280 |    "metadata": {},
281 |    "outputs": [
282 |     {
283 |      "data": {
284 |       "text/plain": [
285 |        "[1, 2, 3, 4, 5, 99, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 909]"
286 |       ]
287 |      },
288 |      "execution_count": 15,
289 |      "metadata": {},
290 |      "output_type": "execute_result"
291 |     }
292 |    ],
293 |    "source": [
294 |     "# Add a number at the end of the list\n",
295 |     "simple.append(909)\n",
296 |     "simple"
297 |    ]
298 |   },
299 |   {
300 |    "cell_type": "code",
301 |    "execution_count": 16,
302 |    "metadata": {},
303 |    "outputs": [
304 |     {
305 |      "data": {
306 |       "text/plain": [
307 |        "[1, 2, 3, 4, 5, 6, 99, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 909]"
308 |       ]
309 |      },
310 |      "execution_count": 16,
311 |      "metadata": {},
312 |      "output_type": "execute_result"
313 |     }
314 |    ],
315 |    "source": [
316 |     "# Add a number in the middle of the list (add number 6 just before position 5)\n",
317 |     "simple.insert(5, 6)\n",
318 |     "simple"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "code",
323 |    "execution_count": 17,
324 |    "metadata": {},
325 |    "outputs": [
326 |     {
327 |      "data": {
328 |       "text/plain": [
329 |        "True"
330 |       ]
331 |      },
332 |      "execution_count": 17,
333 |      "metadata": {},
334 |      "output_type": "execute_result"
335 |     }
336 |    ],
337 |    "source": [
338 |     "# Quickly check if a number is somewhere in the list\n",
339 |     "99 in simple"
340 |    ]
341 |   },
342 |   {
343 |    "cell_type": "code",
344 |    "execution_count": 18,
345 |    "metadata": {},
346 |    "outputs": [
347 |     {
348 |      "data": {
349 |       "text/plain": [
350 |        "6"
351 |       ]
352 |      },
353 |      "execution_count": 18,
354 |      "metadata": {},
355 |      "output_type": "execute_result"
356 |     }
357 |    ],
358 |    "source": [
359 |     "# And then check which index position it occurs in the list\n",
360 |     "simple.index(99)"
361 |    ]
362 |   },
363 |   {
364 |    "cell_type": "code",
365 |    "execution_count": 19,
366 |    "metadata": {},
367 |    "outputs": [
368 |     {
369 |      "data": {
370 |       "text/plain": [
371 |        "909"
372 |       ]
373 |      },
374 |      "execution_count": 19,
375 |      "metadata": {},
376 |      "output_type": "execute_result"
377 |     }
378 |    ],
379 |    "source": [
380 |     "# What is the biggest number in the list?\n",
381 |     "max(simple)"
382 |    ]
383 |   },
384 |   {
385 |    "cell_type": "code",
386 |    "execution_count": 20,
387 |    "metadata": {},
388 |    "outputs": [
389 |     {
390 |      "data": {
391 |       "text/plain": [
392 |        "1"
393 |       ]
394 |      },
395 |      "execution_count": 20,
396 |      "metadata": {},
397 |      "output_type": "execute_result"
398 |     }
399 |    ],
400 |    "source": [
401 |     "# And the smallest?\n",
402 |     "min(simple)"
403 |    ]
404 |   },
405 |   {
406 |    "cell_type": "code",
407 |    "execution_count": 21,
408 |    "metadata": {},
409 |    "outputs": [],
410 |    "source": [
411 |     "# Some more finicky examples"
412 |    ]
413 |   },
414 |   {
415 |    "cell_type": "code",
416 |    "execution_count": 22,
417 |    "metadata": {},
418 |    "outputs": [
419 |     {
420 |      "name": "stdout",
421 |      "output_type": "stream",
422 |      "text": [
423 |       "Fortune 500\n",
424 |       "Think Bike\n",
425 |       "Make Love\n",
426 |       "Be Careful\n"
427 |      ]
428 |     }
429 |    ],
430 |    "source": [
431 |     "List_1 = [\"Be\", \"Make\",\"Think\", \"Fortune\"]\n",
432 |     "List_2 = [\"Careful\", \"Love\", \"Bike\", \"500\"]\n",
433 |     "if len(List_1) < len(List_2):\n",
434 |     "    x = len(List_1) + 1\n",
435 |     "else:\n",
436 |     "    x = len(List_2) + 1\n",
437 |     "for i in range(1,x):\n",
438 |     "    print(List_1[-i] + \" \" + List_2[-i])"
439 |    ]
440 |   },
441 |   {
442 |    "cell_type": "code",
443 |    "execution_count": 23,
444 |    "metadata": {},
445 |    "outputs": [
446 |     {
447 |      "name": "stdout",
448 |      "output_type": "stream",
449 |      "text": [
450 |       "Be Careful\n",
451 |       "Fortune 500\n",
452 |       "Think Bike\n",
453 |       "Make Love\n"
454 |      ]
455 |     }
456 |    ],
457 |    "source": [
458 |     "List_1 = [\"Be\", \"Make\",\"Think\", \"Fortune\"]\n",
459 |     "List_2 = [\"Careful\", \"Love\", \"Bike\", \"500\"]\n",
460 |     "if len(List_1) < len(List_2):\n",
461 |     "    x = len(List_1)\n",
462 |     "else:\n",
463 |     "    x = len(List_2)\n",
464 |     "for i in range(x):\n",
465 |     "    print(List_1[-i] + \" \" + List_2[-i])"
466 |    ]
467 |   },
468 |   {
469 |    "cell_type": "code",
470 |    "execution_count": 24,
471 |    "metadata": {
472 |     "scrolled": true
473 |    },
474 |    "outputs": [
475 |     {
476 |      "name": "stdout",
477 |      "output_type": "stream",
478 |      "text": [
479 |       "Fortune 500\n",
480 |       "Think Bike\n",
481 |       "Make Love\n",
482 |       "Be Careful\n"
483 |      ]
484 |     }
485 |    ],
486 |    "source": [
487 |     "List_1 = [\"Be\", \"Make\",\"Think\", \"Fortune\"]\n",
488 |     "List_2 = [\"Careful\", \"Love\", \"Bike\", \"500\"]\n",
489 |     "if len(List_1) < len(List_2):\n",
490 |     "    x = len(List_1)\n",
491 |     "else:\n",
492 |     "    x = len(List_2)\n",
493 |     "for i in range(-1,(-x-1),-1):\n",
494 |     "    print(List_1[i] + \" \" + List_2[i])"
495 |    ]
496 |   }
497 |  ],
498 |  "metadata": {
499 |   "kernelspec": {
500 |    "display_name": "Python 3",
501 |    "language": "python",
502 |    "name": "python3"
503 |   },
504 |   "language_info": {
505 |    "codemirror_mode": {
506 |     "name": "ipython",
507 |     "version": 3
508 |    },
509 |    "file_extension": ".py",
510 |    "mimetype": "text/x-python",
511 |    "name": "python",
512 |    "nbconvert_exporter": "python",
513 |    "pygments_lexer": "ipython3",
514 |    "version": "3.6.5"
515 |   }
516 |  },
517 |  "nbformat": 4,
518 |  "nbformat_minor": 2
519 | }
520 | 


--------------------------------------------------------------------------------
/Practice Run - Linear Regression Age vs Blood Pressure.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "Small dataset for linear regression from:<br>\n",
  8 |     "http://people.sc.fsu.edu/~jburkardt/datasets/regression/x03.txt"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": 1,
 14 |    "metadata": {},
 15 |    "outputs": [],
 16 |    "source": [
 17 |     "import numpy as np\n",
 18 |     "from scipy import stats\n",
 19 |     "import pandas as pd\n",
 20 |     "import matplotlib.pyplot as plt\n",
 21 |     "%matplotlib inline"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 2,
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "x03 = pd.read_excel(\"x03.xlsx\")\n",
 31 |     "#      I,  the index;\n",
 32 |     "#      A0, 1,\n",
 33 |     "#      A1, the age;\n",
 34 |     "#      B,  the systolic blood pressure."
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 3,
 40 |    "metadata": {},
 41 |    "outputs": [
 42 |     {
 43 |      "data": {
 44 |       "text/html": [
 45 |        "<div>\n",
 46 |        "<style scoped>\n",
 47 |        "    .dataframe tbody tr th:only-of-type {\n",
 48 |        "        vertical-align: middle;\n",
 49 |        "    }\n",
 50 |        "\n",
 51 |        "    .dataframe tbody tr th {\n",
 52 |        "        vertical-align: top;\n",
 53 |        "    }\n",
 54 |        "\n",
 55 |        "    .dataframe thead th {\n",
 56 |        "        text-align: right;\n",
 57 |        "    }\n",
 58 |        "</style>\n",
 59 |        "<table border=\"1\" class=\"dataframe\">\n",
 60 |        "  <thead>\n",
 61 |        "    <tr style=\"text-align: right;\">\n",
 62 |        "      <th></th>\n",
 63 |        "      <th>I</th>\n",
 64 |        "      <th>A0</th>\n",
 65 |        "      <th>A1</th>\n",
 66 |        "      <th>B</th>\n",
 67 |        "    </tr>\n",
 68 |        "  </thead>\n",
 69 |        "  <tbody>\n",
 70 |        "    <tr>\n",
 71 |        "      <th>0</th>\n",
 72 |        "      <td>1</td>\n",
 73 |        "      <td>1</td>\n",
 74 |        "      <td>39</td>\n",
 75 |        "      <td>144</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>1</th>\n",
 79 |        "      <td>2</td>\n",
 80 |        "      <td>1</td>\n",
 81 |        "      <td>47</td>\n",
 82 |        "      <td>220</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>2</th>\n",
 86 |        "      <td>3</td>\n",
 87 |        "      <td>1</td>\n",
 88 |        "      <td>45</td>\n",
 89 |        "      <td>138</td>\n",
 90 |        "    </tr>\n",
 91 |        "  </tbody>\n",
 92 |        "</table>\n",
 93 |        "</div>"
 94 |       ],
 95 |       "text/plain": [
 96 |        "   I  A0  A1    B\n",
 97 |        "0  1   1  39  144\n",
 98 |        "1  2   1  47  220\n",
 99 |        "2  3   1  45  138"
100 |       ]
101 |      },
102 |      "execution_count": 3,
103 |      "metadata": {},
104 |      "output_type": "execute_result"
105 |     }
106 |    ],
107 |    "source": [
108 |     "x03.head(3)"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 4,
114 |    "metadata": {},
115 |    "outputs": [
116 |     {
117 |      "data": {
118 |       "text/plain": [
119 |        "<matplotlib.collections.PathCollection at 0x116a59da0>"
120 |       ]
121 |      },
122 |      "execution_count": 4,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     },
126 |     {
127 |      "data": {
128 |       "image/png": 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\n",
129 |       "text/plain": [
130 |        "<Figure size 432x288 with 1 Axes>"
131 |       ]
132 |      },
133 |      "metadata": {},
134 |      "output_type": "display_data"
135 |     }
136 |    ],
137 |    "source": [
138 |     "plt.scatter(x03[\"A1\"], x03[\"B\"])"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 5,
144 |    "metadata": {},
145 |    "outputs": [
146 |     {
147 |      "data": {
148 |       "text/plain": [
149 |        "array([[1.        , 0.65756728],\n",
150 |        "       [0.65756728, 1.        ]])"
151 |       ]
152 |      },
153 |      "execution_count": 5,
154 |      "metadata": {},
155 |      "output_type": "execute_result"
156 |     }
157 |    ],
158 |    "source": [
159 |     "np.corrcoef(x03[\"A1\"], x03[\"B\"])"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 6,
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "name": "stdout",
169 |      "output_type": "stream",
170 |      "text": [
171 |       "0.9708703514427236 98.71471813821842 0.4323947319275954\n"
172 |      ]
173 |     }
174 |    ],
175 |    "source": [
176 |     "# Using the linregress() function returns 5 values, which we name here for convenience:\n",
177 |     "slope, intercept, r_value, p_value, std_err = stats.linregress(x03[\"A1\"], x03[\"B\"])\n",
178 |     "\n",
179 |     "# Slope = m from our equation, Intercept = b, r_value = our correlation co-efficient\n",
180 |     "print(slope, intercept, r_value**2)"
181 |    ]
182 |   },
183 |   {
184 |    "cell_type": "code",
185 |    "execution_count": 7,
186 |    "metadata": {},
187 |    "outputs": [
188 |     {
189 |      "data": {
190 |       "image/png": 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\n",
191 |       "text/plain": [
192 |        "<Figure size 432x288 with 1 Axes>"
193 |       ]
194 |      },
195 |      "metadata": {},
196 |      "output_type": "display_data"
197 |     }
198 |    ],
199 |    "source": [
200 |     "def get_B():\n",
201 |     "    return slope * x03[\"A1\"] + intercept\n",
202 |     "\n",
203 |     "plt.scatter(x03[\"A1\"], x03[\"B\"])\n",
204 |     "plt.plot(x03[\"A1\"], get_B(), c='r')\n",
205 |     "plt.show()"
206 |    ]
207 |   }
208 |  ],
209 |  "metadata": {
210 |   "kernelspec": {
211 |    "display_name": "Python 3",
212 |    "language": "python",
213 |    "name": "python3"
214 |   },
215 |   "language_info": {
216 |    "codemirror_mode": {
217 |     "name": "ipython",
218 |     "version": 3
219 |    },
220 |    "file_extension": ".py",
221 |    "mimetype": "text/x-python",
222 |    "name": "python",
223 |    "nbconvert_exporter": "python",
224 |    "pygments_lexer": "ipython3",
225 |    "version": "3.6.5"
226 |   }
227 |  },
228 |  "nbformat": 4,
229 |  "nbformat_minor": 2
230 | }
231 | 


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/README.md:
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1 | # how-to-python
2 | Knowledge is often hard-won, and then quickly forgotten! This repository aims to explain simply, and then serve as an aid to memory.
3 | 


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/tensor_shape_2.png:
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https://raw.githubusercontent.com/shotleft/how-to-python/9b71a76742b3896d0017a1fcc6cdbd64ad421b1b/tensor_shape_2.png


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/unsorted.jpg:
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https://raw.githubusercontent.com/shotleft/how-to-python/9b71a76742b3896d0017a1fcc6cdbd64ad421b1b/unsorted.jpg


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/weight_matrix_detail_2.png:
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https://raw.githubusercontent.com/shotleft/how-to-python/9b71a76742b3896d0017a1fcc6cdbd64ad421b1b/weight_matrix_detail_2.png


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