├── Economic_Visualizations.ipynb
├── Parliamentary-Voting-Working.ipynb
├── Plot-birthdays.ipynb
├── README.md
├── bls-cpi.ipynb
├── gun-violence-by-US-state.ipynb
├── gun_violence_by_country.ipynb
├── health_vs_gdp.ipynb
└── tropical_storm_data.ipynb


/Parliamentary-Voting-Working.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>Vote Number</th>\n",
 39 |        "      <th>Vote Respecting</th>\n",
 40 |        "      <th>Subject</th>\n",
 41 |        "      <th>Votes  (Yeas/ Nays / Paired)</th>\n",
 42 |        "      <th>Vote Result</th>\n",
 43 |        "      <th>Date</th>\n",
 44 |        "    </tr>\n",
 45 |        "  </thead>\n",
 46 |        "  <tbody>\n",
 47 |        "    <tr>\n",
 48 |        "      <th>0</th>\n",
 49 |        "      <td>No. 1379</td>\n",
 50 |        "      <td>House Government Bill</td>\n",
 51 |        "      <td>Motion respecting Senate amendments to Bill C-...</td>\n",
 52 |        "      <td>161 / 58 / 2</td>\n",
 53 |        "      <td>Agreed To</td>\n",
 54 |        "      <td>June 19, 2019</td>\n",
 55 |        "    </tr>\n",
 56 |        "    <tr>\n",
 57 |        "      <th>1</th>\n",
 58 |        "      <td>No. 1378</td>\n",
 59 |        "      <td>House Government Bill</td>\n",
 60 |        "      <td>Motion for closure</td>\n",
 61 |        "      <td>149 / 67 / 2</td>\n",
 62 |        "      <td>Agreed To</td>\n",
 63 |        "      <td>June 19, 2019</td>\n",
 64 |        "    </tr>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>2</th>\n",
 67 |        "      <td>No. 1374</td>\n",
 68 |        "      <td>House Government Bill</td>\n",
 69 |        "      <td>Motion respecting Senate amendments to Bill C-...</td>\n",
 70 |        "      <td>190 / 86 / 2</td>\n",
 71 |        "      <td>Agreed To</td>\n",
 72 |        "      <td>June 18, 2019</td>\n",
 73 |        "    </tr>\n",
 74 |        "    <tr>\n",
 75 |        "      <th>3</th>\n",
 76 |        "      <td>No. 1373</td>\n",
 77 |        "      <td>House Government Bill</td>\n",
 78 |        "      <td>Motion for closure</td>\n",
 79 |        "      <td>157 / 113 / 2</td>\n",
 80 |        "      <td>Agreed To</td>\n",
 81 |        "      <td>June 18, 2019</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>4</th>\n",
 85 |        "      <td>No. 1372</td>\n",
 86 |        "      <td>House Government Bill</td>\n",
 87 |        "      <td>3rd reading and adoption of Bill C-102, An Act...</td>\n",
 88 |        "      <td>167 / 123 / 2</td>\n",
 89 |        "      <td>Agreed To</td>\n",
 90 |        "      <td>June 18, 2019</td>\n",
 91 |        "    </tr>\n",
 92 |        "  </tbody>\n",
 93 |        "</table>\n",
 94 |        "</div>"
 95 |       ],
 96 |       "text/plain": [
 97 |        "  Vote Number        Vote Respecting  \\\n",
 98 |        "0    No. 1379  House Government Bill   \n",
 99 |        "1    No. 1378  House Government Bill   \n",
100 |        "2    No. 1374  House Government Bill   \n",
101 |        "3    No. 1373  House Government Bill   \n",
102 |        "4    No. 1372  House Government Bill   \n",
103 |        "\n",
104 |        "                                             Subject  \\\n",
105 |        "0  Motion respecting Senate amendments to Bill C-...   \n",
106 |        "1                                 Motion for closure   \n",
107 |        "2  Motion respecting Senate amendments to Bill C-...   \n",
108 |        "3                                 Motion for closure   \n",
109 |        "4  3rd reading and adoption of Bill C-102, An Act...   \n",
110 |        "\n",
111 |        "  Votes  (Yeas/ Nays / Paired) Vote Result           Date  \n",
112 |        "0                 161 / 58 / 2   Agreed To  June 19, 2019  \n",
113 |        "1                 149 / 67 / 2   Agreed To  June 19, 2019  \n",
114 |        "2                 190 / 86 / 2   Agreed To  June 18, 2019  \n",
115 |        "3                157 / 113 / 2   Agreed To  June 18, 2019  \n",
116 |        "4                167 / 123 / 2   Agreed To  June 18, 2019  "
117 |       ]
118 |      },
119 |      "execution_count": 2,
120 |      "metadata": {},
121 |      "output_type": "execute_result"
122 |     }
123 |    ],
124 |    "source": [
125 |     "# TypeId=3 in URL returns only government bills, TypeId=4 returns only private members bills\n",
126 |     "dfs_vote_list = pd.read_html('https://www.ourcommons.ca/Members/en/votes?parlSession=42-1&billDocumentTypeId=3',header=0)\n",
127 |     "vote_list = dfs_vote_list[0]\n",
128 |     "vote_list.head()"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 3,
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": [
137 |     "# [initialize global variables:]\n",
138 |     "total_votes = 0\n",
139 |     "partyLineVotesConservative = 0 \n",
140 |     "non_partyLineVotesConservative = 0\n",
141 |     "partyLineVotesLiberal = 0 \n",
142 |     "non_partyLineVotesLiberal = 0\n",
143 |     "partyLineVotesNDP = 0 \n",
144 |     "non_partyLineVotesNDP = 0\n",
145 |     "partyLineVotesBloc = 0 \n",
146 |     "non_partyLineVotesBloc = 0"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 4,
152 |    "metadata": {},
153 |    "outputs": [],
154 |    "source": [
155 |     "# Functions for categorizing and tabulating votes by party:\n",
156 |     "def liberal_votes():\n",
157 |     "    global partyLineVotesLiberal \n",
158 |     "    global non_partyLineVotesLiberal\n",
159 |     "    \n",
160 |     "    df_party = df[df['Party'].str.contains('Liberal')]\n",
161 |     "    vote_output_yea = df_party['Vote'].str.contains('Yea')\n",
162 |     "    total_votes_yea = vote_output_yea.sum()\n",
163 |     "    vote_output_nay = df_party['Vote'].str.contains('Nay')\n",
164 |     "    total_votes_nay = vote_output_nay.sum()\n",
165 |     "    if total_votes_yea>0 and total_votes_nay>0:\n",
166 |     "        non_partyLineVotesLiberal += 1\n",
167 |     "    else:\n",
168 |     "        partyLineVotesLiberal += 1\n",
169 |     "    \n",
170 |     "def conservative_votes():\n",
171 |     "    global partyLineVotesConservative \n",
172 |     "    global non_partyLineVotesConservative\n",
173 |     "    \n",
174 |     "    df_party = df[df['Party'].str.contains('Conservative')]\n",
175 |     "    vote_output_yea = df_party['Vote'].str.contains('Yea')\n",
176 |     "    total_votes_yea = vote_output_yea.sum()\n",
177 |     "    vote_output_nay = df_party['Vote'].str.contains('Nay')\n",
178 |     "    total_votes_nay = vote_output_nay.sum()\n",
179 |     "    if total_votes_yea>0 and total_votes_nay>0:\n",
180 |     "        non_partyLineVotesConservative += 1\n",
181 |     "    else:\n",
182 |     "        partyLineVotesConservative += 1\n",
183 |     "        \n",
184 |     "def ndp_votes():\n",
185 |     "    global partyLineVotesNDP \n",
186 |     "    global non_partyLineVotesNDP\n",
187 |     "\n",
188 |     "    df_party = df[df['Party'].str.contains('NDP')]\n",
189 |     "    vote_output_yea = df_party['Vote'].str.contains('Yea')\n",
190 |     "    total_votes_yea = vote_output_yea.sum()\n",
191 |     "    vote_output_nay = df_party['Vote'].str.contains('Nay')\n",
192 |     "    total_votes_nay = vote_output_nay.sum()\n",
193 |     "    if total_votes_yea>0 and total_votes_nay>0:\n",
194 |     "        non_partyLineVotesNDP += 1\n",
195 |     "    else:\n",
196 |     "        partyLineVotesNDP += 1\n",
197 |     "    \n",
198 |     "def bloc_votes():\n",
199 |     "    global partyLineVotesBloc \n",
200 |     "    global non_partyLineVotesBloc\n",
201 |     "\n",
202 |     "    df_party = df[df['Party'].str.contains('Bloc')]\n",
203 |     "    vote_output_yea = df_party['Vote'].str.contains('Yea')\n",
204 |     "    total_votes_yea = vote_output_yea.sum()\n",
205 |     "    vote_output_nay = df_party['Vote'].str.contains('Nay')\n",
206 |     "    total_votes_nay = vote_output_nay.sum()\n",
207 |     "    if total_votes_yea>0 and total_votes_nay>0:\n",
208 |     "        non_partyLineVotesBloc += 1\n",
209 |     "    else:\n",
210 |     "        partyLineVotesBloc += 1"
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": 5,
216 |    "metadata": {},
217 |    "outputs": [
218 |     {
219 |      "data": {
220 |       "text/html": [
221 |        "<div>\n",
222 |        "<style scoped>\n",
223 |        "    .dataframe tbody tr th:only-of-type {\n",
224 |        "        vertical-align: middle;\n",
225 |        "    }\n",
226 |        "\n",
227 |        "    .dataframe tbody tr th {\n",
228 |        "        vertical-align: top;\n",
229 |        "    }\n",
230 |        "\n",
231 |        "    .dataframe thead th {\n",
232 |        "        text-align: right;\n",
233 |        "    }\n",
234 |        "</style>\n",
235 |        "<table border=\"1\" class=\"dataframe\">\n",
236 |        "  <thead>\n",
237 |        "    <tr style=\"text-align: right;\">\n",
238 |        "      <th></th>\n",
239 |        "      <th>Vote</th>\n",
240 |        "    </tr>\n",
241 |        "  </thead>\n",
242 |        "  <tbody>\n",
243 |        "    <tr>\n",
244 |        "      <th>0</th>\n",
245 |        "      <td>https://www.ourcommons.ca/Members/en/votes/42/...</td>\n",
246 |        "    </tr>\n",
247 |        "    <tr>\n",
248 |        "      <th>1</th>\n",
249 |        "      <td>https://www.ourcommons.ca/Members/en/votes/42/...</td>\n",
250 |        "    </tr>\n",
251 |        "    <tr>\n",
252 |        "      <th>2</th>\n",
253 |        "      <td>https://www.ourcommons.ca/Members/en/votes/42/...</td>\n",
254 |        "    </tr>\n",
255 |        "    <tr>\n",
256 |        "      <th>3</th>\n",
257 |        "      <td>https://www.ourcommons.ca/Members/en/votes/42/...</td>\n",
258 |        "    </tr>\n",
259 |        "    <tr>\n",
260 |        "      <th>4</th>\n",
261 |        "      <td>https://www.ourcommons.ca/Members/en/votes/42/...</td>\n",
262 |        "    </tr>\n",
263 |        "  </tbody>\n",
264 |        "</table>\n",
265 |        "</div>"
266 |       ],
267 |       "text/plain": [
268 |        "                                                Vote\n",
269 |        "0  https://www.ourcommons.ca/Members/en/votes/42/...\n",
270 |        "1  https://www.ourcommons.ca/Members/en/votes/42/...\n",
271 |        "2  https://www.ourcommons.ca/Members/en/votes/42/...\n",
272 |        "3  https://www.ourcommons.ca/Members/en/votes/42/...\n",
273 |        "4  https://www.ourcommons.ca/Members/en/votes/42/..."
274 |       ]
275 |      },
276 |      "execution_count": 5,
277 |      "metadata": {},
278 |      "output_type": "execute_result"
279 |     }
280 |    ],
281 |    "source": [
282 |     "# Build URL list from master Votes page:\n",
283 |     "dfs_vote_list = pd.read_html('https://www.ourcommons.ca/Members/en/votes?parlSession=42-1&billDocumentTypeId=3',header=0)\n",
284 |     "vote_list = dfs_vote_list[0]\n",
285 |     "vote_list.columns = ['Number','Type','Subject','Votes','Result','Date']\n",
286 |     "vote_list['Number'] = vote_list['Number'].str.extract('(\\d+)', expand=False)\n",
287 |     "base_url = \"https://www.ourcommons.ca/Members/en/votes/42/1/\"\n",
288 |     "url_data = pd.DataFrame(columns=[\"Vote\"])\n",
289 |     "Vote = []\n",
290 |     "for name in vote_list['Number']:\n",
291 |     "    newUrl = base_url + name\n",
292 |     "    Vote.append(newUrl)\n",
293 |     "url_data[\"Vote\"] = Vote\n",
294 |     "url_data.head()"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": null,
300 |    "metadata": {},
301 |    "outputs": [],
302 |    "source": [
303 |     "# Run this only once!\n",
304 |     "url_data.to_csv(r'url-text-42-1-government', \n",
305 |     "                header=None, index=None, sep=' ', mode='a')"
306 |    ]
307 |   },
308 |   {
309 |    "cell_type": "code",
310 |    "execution_count": null,
311 |    "metadata": {},
312 |    "outputs": [],
313 |    "source": [
314 |     "# Read URLs\n",
315 |     "URLS = open(\"url-text-42-1-government\",\"r\")\n",
316 |     "for url in URLS:\n",
317 |     "    # Read next HTML page in set:\n",
318 |     "    dfs = pd.read_html(url,header=0)\n",
319 |     "    df = dfs[0]\n",
320 |     "    df.rename(columns={'Member Voted':'Vote'}, inplace=True)\n",
321 |     "    df.rename(columns={'Political Affiliation':'Party'}, inplace=True)\n",
322 |     "    # Ignore unanimous votes:\n",
323 |     "    vote_output_nay = df[df['Vote'].str.contains('Nay', na=False)]\n",
324 |     "    total_votes_nay = vote_output_nay['Vote'].str.contains('Nay', na=False)\n",
325 |     "    filtered_votes = total_votes_nay.sum()\n",
326 |     "    if filtered_votes==0:\n",
327 |     "        continue\n",
328 |     "    # Call functions to tabulate votes:\n",
329 |     "    else:\n",
330 |     "        liberal_votes()\n",
331 |     "        conservative_votes()\n",
332 |     "        ndp_votes()\n",
333 |     "        bloc_votes()\n",
334 |     "\n",
335 |     "        total_votes += 1\n",
336 |     "\n",
337 |     "\n",
338 |     "print(\"We counted\", total_votes, \"votes in total.\")\n",
339 |     "\n",
340 |     "print(\"Conservative members voted the party line\", partyLineVotesConservative, \n",
341 |     "      \"times, and split their vote\", non_partyLineVotesConservative, \"times.\")\n",
342 |     "\n",
343 |     "print(\"Liberal members voted the party line\", partyLineVotesLiberal, \n",
344 |     "      \"times, and split their vote\", non_partyLineVotesLiberal, \"times.\")\n",
345 |     "\n",
346 |     "print(\"NDP members voted the party line\", partyLineVotesNDP, \n",
347 |     "      \"times, and split their vote\", non_partyLineVotesNDP, \"times.\")\n",
348 |     "    \n",
349 |     "print(\"Bloc members voted the party line\", partyLineVotesBloc, \n",
350 |     "      \"times, and split their vote\", non_partyLineVotesBloc, \"times.\")"
351 |    ]
352 |   },
353 |   {
354 |    "cell_type": "code",
355 |    "execution_count": null,
356 |    "metadata": {},
357 |    "outputs": [],
358 |    "source": [
359 |     "data = {'Party': ['Conservative', 'Liberal', 'NDP', 'Bloc'],\n",
360 |     "'Party-line votes': [408, 402, 417, 423],\n",
361 |     "'Non-party-line votes': [16, 22, 7, 1]}\n",
362 |     "dfdata = pd.DataFrame (data, columns = ['Party', 'Party-line votes', 'Non-party-line votes'])\n",
363 |     "\n",
364 |     "print(df_output_42_1_government)"
365 |    ]
366 |   },
367 |   {
368 |    "cell_type": "code",
369 |    "execution_count": null,
370 |    "metadata": {},
371 |    "outputs": [],
372 |    "source": [
373 |     "dfdata['percentage']= dfdata['Non-party-line votes']/dfdata['Party-line votes'].sum()\n",
374 |     "dfdata\n",
375 |     "#dfdata = df_output_42_1_government['Party-line votes',+,'non-party-line votes',/,'Party-line votes']\n",
376 |     "\n",
377 |     "#df_output_42_1_government.plot(kind='bar')"
378 |    ]
379 |   }
380 |  ],
381 |  "metadata": {
382 |   "kernelspec": {
383 |    "display_name": "Python 3",
384 |    "language": "python",
385 |    "name": "python3"
386 |   },
387 |   "language_info": {
388 |    "codemirror_mode": {
389 |     "name": "ipython",
390 |     "version": 3
391 |    },
392 |    "file_extension": ".py",
393 |    "mimetype": "text/x-python",
394 |    "name": "python",
395 |    "nbconvert_exporter": "python",
396 |    "pygments_lexer": "ipython3",
397 |    "version": "3.7.6"
398 |   }
399 |  },
400 |  "nbformat": 4,
401 |  "nbformat_minor": 4
402 | }
403 | 


--------------------------------------------------------------------------------
/Plot-birthdays.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 141,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import requests\n",
 11 |     "import json\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "import numpy as np"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 157,
 19 |    "metadata": {
 20 |     "scrolled": false
 21 |    },
 22 |    "outputs": [
 23 |     {
 24 |      "data": {
 25 |       "text/plain": [
 26 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ccf19a550>"
 27 |       ]
 28 |      },
 29 |      "execution_count": 157,
 30 |      "metadata": {},
 31 |      "output_type": "execute_result"
 32 |     },
 33 |     {
 34 |      "data": {
 35 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAARgklEQVR4nO3df4wcd3nH8feDTYrxgZ0QsjJ21DPCMtC4MXhFQyOhu5hULkHYfwAKSqNL6/b+gTRtXRXTSm3/6A8jGn5IRa0sQn1V0xyRSWQrETSW8TWqRCJ8AeGASU1TE+wYmx+O4ULUYPr0jxuXy/nOu7e3P/LdvF+StTPfndl5nr27j+fmZnYiM5EkledlvS5AktQaA1ySCmWAS1KhDHBJKpQBLkmFWtrNjV155ZU5ODjYzU227Nlnn2X58uW9LqMj7K1c/dyfvc1vcnLyB5n52tnjDQM8ItYDn5sx9Hrgz4F/rsYHgePA+zPz7KVea3BwkMOHDzdfdQ9NTEwwNDTU6zI6wt7K1c/92dv8IuI7c403PISSmU9k5sbM3AhsAn4K3A/sBA5m5jrgYDUvSeqShR4D3wz8V2Z+B9gKjFXjY8C2dhYmSbq0hQb4zcA91XQtM08BVI9XtbMwSdKlRbOX0kfEZcDTwK9k5umIeCYzV854/mxmXj7HeqPAKECtVts0Pj7enso7bGpqioGBgV6X0RH2Vq5+7s/e5jc8PDyZmfWLnsjMpv4xfcjkoRnzTwCrqulVwBONXmPTpk1ZikOHDvW6hI6xt3L1c3/2Nj/gcM6RqQs5hPIBfnH4BGA/MFJNjwD7FvifiiRpEZoK8Ih4JXAjcN+M4V3AjRFxrHpuV/vLkyTNp6kLeTLzp8BrZo39kOmzUiRJPeCl9JJUqK5eSr8Ygzsf7Or2dmw4z207H+T4rpu6ul1JapZ74JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKlRTAR4RKyNib0R8KyKORsTbI+KKiDgQEceqx8s7Xawk6Rea3QP/FPDFzHwjcC1wFNgJHMzMdcDBal6S1CUNAzwiXg28A7gLIDOfz8xngK3AWLXYGLCtU0VKki4WmXnpBSI2AruBbzK99z0J3AGczMyVM5Y7m5kXHUaJiFFgFKBWq20aHx9vqdAjJ8+1tF6rasvg9HOwYfWKrm63G6amphgYGOh1GR3Rz71Bf/dnb/MbHh6ezMz67PFmArwOPAJcn5mPRsSngB8DtzcT4DPV6/U8fPhwSw0M7nywpfVatWPDee48spTju27q6na7YWJigqGhoV6X0RH93Bv0d3/2Nr+ImDPAmzkGfgI4kZmPVvN7gbcCpyNiVfXiq4AzLVcnSVqwhgGemd8DvhsR66uhzUwfTtkPjFRjI8C+jlQoSZrT0iaXux24OyIuA54Efpvp8L83IrYDTwHv60yJkqS5NBXgmfk14KLjL0zvjUuSesArMSWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIK1dRd6SPiOPAT4OfA+cysR8QVwOeAQeA48P7MPNuZMiVJsy1kD3w4MzdmZr2a3wkczMx1wMFqXpLUJYs5hLIVGKumx4Btiy9HktSsZgM8gYciYjIiRquxWmaeAqger+pEgZKkuUVmNl4o4nWZ+XREXAUcAG4H9mfmyhnLnM3My+dYdxQYBajVapvGx8dbKvTIyXMtrdeq2jI4/RxsWL2iq9vthqmpKQYGBnpdRkf0c2/Q3/3Z2/yGh4cnZxy+/n9NBfgLVoj4S2AK+D1gKDNPRcQqYCIz119q3Xq9nocPH17Q9i4Y3PlgS+u1aseG89x5ZCnHd93U1e12w8TEBENDQ70uoyP6uTfo7/7sbX4RMWeANzyEEhHLI+JVF6aB3wAeB/YDI9ViI8C+lquTJC1YM6cR1oD7I+LC8v+amV+MiK8A90bEduAp4H2dK1OSFq/bv8lfsGfL8o68bsMAz8wngWvnGP8hsLkTRUmSGvNKTEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoZq5I49eYnp11xKgL+9BKnWKe+CSVCgDXJIKZYBLUqEMcEkqlAEuSYVqOsAjYklEfDUiHqjm10bEoxFxLCI+FxGXda5MSdJsC9kDvwM4OmP+o8AnMnMdcBbY3s7CJEmX1lSAR8Qa4CbgM9V8ADcAe6tFxoBtnShQkjS3yMzGC0XsBf4WeBXwx8BtwCOZ+Ybq+auBL2TmNXOsOwqMAtRqtU3j4+MtFXrk5LmW1mtVbRmcfg42rF7R1e12w9TUFAMDA/M+3+33eqbFvt+NeitdP/fXjd569b29dsWSRfU2PDw8mZn12eMNr8SMiHcDZzJzMiKGLgzPseic/xNk5m5gN0C9Xs+hoaG5Fmvoti5fHbhjw3nuPLKU47cMdXW73TAxMcGlvg7dfq9nWuz73ai30vVzf93orVff23u2LO9Ib81cSn898J6IeBfwCuDVwCeBlRGxNDPPA2uAp9tenSRpXg2PgWfmRzJzTWYOAjcDX8rMW4BDwHurxUaAfR2rUpJ0kcWcB/5h4I8i4tvAa4C72lOSJKkZC/o0wsycACaq6SeBt7W/JElSM7wSU5IKZYBLUqEMcEkqlHfkacC700h6sXIPXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgrV8I48EfEK4GHgl6rl92bmX0TEWmAcuAJ4DLg1M5/vZLEvNZ26G9CODee5rYd3GpLUHs3sgf8PcENmXgtsBLZExHXAR4FPZOY64CywvXNlSpJmaxjgOW2qmn159S+BG4C91fgYsK0jFUqS5hSZ2XihiCXAJPAG4NPAx4BHMvMN1fNXA1/IzGvmWHcUGAWo1WqbxsfHWyr0yMlzLa3XqtoyOP1cVzfZNS/m3jasXrGo9aemphgYGGhTNS8+/dxfN3rrdo5csHbFkkX1Njw8PJmZ9dnjTd2VPjN/DmyMiJXA/cCb5lpsnnV3A7sB6vV6Dg0NNVvzC3T7mO2ODee580hTb09xXsy9Hb9laFHrT0xM0Or3WAn6ub9u9Narv/3s2bK8I70t6CyUzHwGmACuA1ZGxIUUWAM83d7SJEmX0sxZKK8FfpaZz0TEMuCdTP8B8xDwXqbPRBkB9nWyUKmTOnXGTzOO77qpZ9vulbneb8+OWrhmfo9eBYxVx8FfBtybmQ9ExDeB8Yj4K+CrwF0drFOSNEvDAM/MrwNvmWP8SeBtnShKktSYV2JKUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQV6sV5WxbpJWQhn0Xezs/Mfil+Dnm/cQ9ckgplgEtSoQxwSSqUAS5JhTLAJalQnoWiF5XF3h3eO5vrpcQ9cEkqlAEuSYVqGOARcXVEHIqIoxHxjYi4oxq/IiIORMSx6vHyzpcrSbqgmT3w88COzHwTcB3wwYh4M7ATOJiZ64CD1bwkqUsaBnhmnsrMx6rpnwBHgdXAVmCsWmwM2NapIiVJF4vMbH7hiEHgYeAa4KnMXDnjubOZedFhlIgYBUYBarXapvHx8ZYKPXLyXEvrtaq2DE4/19VNdo29laud/W1YvaI9L9SCuX6e+/lrt3bFEgYGBlpef3h4eDIz67PHmw7wiBgA/h3468y8LyKeaSbAZ6rX63n48OEFlj5tsaeXLdSODee580h/nmVpb+VqZ3+9/DCruX6e+/lrt2fLcoaGhlpePyLmDPCmzkKJiJcDnwfuzsz7quHTEbGqen4VcKbl6iRJC9bMWSgB3AUczcyPz3hqPzBSTY8A+9pfniRpPs38vnI9cCtwJCK+Vo39KbALuDcitgNPAe/rTImSpLk0DPDM/A8g5nl6c3vLkSQ1yysxJalQBrgkFcoAl6RC9edJl5Ia6va1FWo/98AlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUA0DPCI+GxFnIuLxGWNXRMSBiDhWPV7e2TIlSbM1swe+B9gya2wncDAz1wEHq3lJUhc1DPDMfBj40azhrcBYNT0GbGtzXZKkBiIzGy8UMQg8kJnXVPPPZObKGc+fzcw5D6NExCgwClCr1TaNj4+3VOiRk+daWq9VtWVw+rmubrJr7K1c/dxfP/e2dsUSBgYGWl5/eHh4MjPrs8c7flf6zNwN7Aao1+s5NDTU0uvc1uU7aO/YcJ47j3T87ekJeytXP/fXz73t2bKcVrPvUlo9C+V0RKwCqB7PtK8kSVIzWg3w/cBINT0C7GtPOZKkZjVzGuE9wJeB9RFxIiK2A7uAGyPiGHBjNS9J6qKGB5wy8wPzPLW5zbVIkhbAKzElqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhFhXgEbElIp6IiG9HxM52FSVJaqzlAI+IJcCngd8E3gx8ICLe3K7CJEmXtpg98LcB387MJzPzeWAc2NqesiRJjURmtrZixHuBLZn5u9X8rcCvZeaHZi03CoxWs+uBJ1ovt6uuBH7Q6yI6xN7K1c/92dv8fjkzXzt7cOkiXjDmGLvof4PM3A3sXsR2eiIiDmdmvdd1dIK9lauf+7O3hVvMIZQTwNUz5tcATy+uHElSsxYT4F8B1kXE2oi4DLgZ2N+esiRJjbR8CCUzz0fEh4B/A5YAn83Mb7Stst4r7rDPAthbufq5P3tboJb/iClJ6i2vxJSkQhngklQoA3yGiLg6Ig5FxNGI+EZE3NHrmtotIpZExFcj4oFe19JuEbEyIvZGxLeqr+Hbe11Tu0TEH1bfk49HxD0R8Ype17QYEfHZiDgTEY/PGLsiIg5ExLHq8fJe1tiqeXr7WPV9+fWIuD8iVrZjWwb4C50HdmTmm4DrgA/24ccD3AEc7XURHfIp4IuZ+UbgWvqkz4hYDfw+UM/Ma5g+aeDm3la1aHuALbPGdgIHM3MdcLCaL9EeLu7tAHBNZv4q8J/AR9qxIQN8hsw8lZmPVdM/YToAVve2qvaJiDXATcBnel1Lu0XEq4F3AHcBZObzmflMb6tqq6XAsohYCrySwq+5yMyHgR/NGt4KjFXTY8C2rhbVJnP1lpkPZeb5avYRpq+bWTQDfB4RMQi8BXi0t5W01SeBPwH+t9eFdMDrge8D/1QdIvpMRCzvdVHtkJkngb8DngJOAecy86HeVtURtcw8BdM7U8BVPa6nU34H+EI7XsgAn0NEDACfB/4gM3/c63raISLeDZzJzMle19IhS4G3Av+QmW8BnqXcX8FfoDoWvBVYC7wOWB4Rv9XbqtSKiPgzpg/V3t2O1zPAZ4mIlzMd3ndn5n29rqeNrgfeExHHmf7kyBsi4l96W1JbnQBOZOaF35j2Mh3o/eCdwH9n5vcz82fAfcCv97imTjgdEasAqsczPa6nrSJiBHg3cEu26QIcA3yGiAimj6EezcyP97qedsrMj2TmmswcZPoPYF/KzL7Zi8vM7wHfjYj11dBm4Js9LKmdngKui4hXVt+jm+mTP9DOsh8YqaZHgH09rKWtImIL8GHgPZn503a9rgH+QtcDtzK9d/q16t+7el2UmnY7cHdEfB3YCPxNj+tpi+q3ir3AY8ARpn9ui77sPCLuAb4MrI+IExGxHdgF3BgRx4Abq/nizNPb3wOvAg5UufKPbdmWl9JLUpncA5ekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVD/B6eJhGWF/319AAAAAElFTkSuQmCC\n",
 36 |       "text/plain": [
 37 |        "<Figure size 432x288 with 1 Axes>"
 38 |       ]
 39 |      },
 40 |      "metadata": {
 41 |       "needs_background": "light"
 42 |      },
 43 |      "output_type": "display_data"
 44 |     }
 45 |    ],
 46 |    "source": [
 47 |     "df3 = pd.DataFrame(columns=['months'])\n",
 48 |     "# num_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30]\n",
 49 |     "for team_id in range(1, 11, 1):\n",
 50 |     "    url = 'https://statsapi.web.nhl.com/api/v1/teams/{}/roster'.format(team_id)\n",
 51 |     "    r = requests.get(url)\n",
 52 |     "    roster_data = r.json()\n",
 53 |     "    df = pd.json_normalize(roster_data['roster'])\n",
 54 |     "    for index, row in df.iterrows():\n",
 55 |     "        newrow = row['person.id']\n",
 56 |     "        url = 'https://statsapi.web.nhl.com/api/v1/people/{}'.format(newrow)\n",
 57 |     "        #print(url)\n",
 58 |     "        newerdata = requests.get(url)\n",
 59 |     "        player_stats = newerdata.json()\n",
 60 |     "        birthday = (player_stats['people'][0]['birthDate'])\n",
 61 |     "        newmonth = int(birthday.split('-')[1])\n",
 62 |     "        df3 = df3.append({'months': newmonth}, ignore_index=True)\n",
 63 |     "        #plt.plot(newmonth)\n",
 64 |     "#df1.plot(kind=\"bar\")\n",
 65 |     "df3.months.hist()"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 179,
 71 |    "metadata": {},
 72 |    "outputs": [
 73 |     {
 74 |      "data": {
 75 |       "text/plain": [
 76 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ccefb1e10>"
 77 |       ]
 78 |      },
 79 |      "execution_count": 179,
 80 |      "metadata": {},
 81 |      "output_type": "execute_result"
 82 |     },
 83 |     {
 84 |      "data": {
 85 |       "image/png": 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\n",
 86 |       "text/plain": [
 87 |        "<Figure size 432x288 with 1 Axes>"
 88 |       ]
 89 |      },
 90 |      "metadata": {
 91 |       "needs_background": "light"
 92 |      },
 93 |      "output_type": "display_data"
 94 |     }
 95 |    ],
 96 |    "source": [
 97 |     "for team_id in range(13, 20, 1):\n",
 98 |     "    url = 'https://statsapi.web.nhl.com/api/v1/teams/{}/roster'.format(team_id)\n",
 99 |     "    r = requests.get(url)\n",
100 |     "    roster_data = r.json()\n",
101 |     "    df = pd.json_normalize(roster_data['roster'])\n",
102 |     "    for index, row in df.iterrows():\n",
103 |     "        newrow = row['person.id']\n",
104 |     "        url = 'https://statsapi.web.nhl.com/api/v1/people/{}'.format(newrow)\n",
105 |     "        #print(url)\n",
106 |     "        newerdata = requests.get(url)\n",
107 |     "        player_stats = newerdata.json()\n",
108 |     "        birthday = (player_stats['people'][0]['birthDate'])\n",
109 |     "        newmonth = int(birthday.split('-')[1])\n",
110 |     "        df3 = df3.append({'months': newmonth}, ignore_index=True)\n",
111 |     "        #plt.plot(newmonth)\n",
112 |     "#df1.plot(kind=\"bar\")\n",
113 |     "df3.months.hist()"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 180,
119 |    "metadata": {},
120 |    "outputs": [
121 |     {
122 |      "data": {
123 |       "text/plain": [
124 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ccee98350>"
125 |       ]
126 |      },
127 |      "execution_count": 180,
128 |      "metadata": {},
129 |      "output_type": "execute_result"
130 |     },
131 |     {
132 |      "data": {
133 |       "image/png": 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fMvOOzFyZmSNMPRn3D5lZ4ugvM/8T+I+IuKgZuhL4Vosl9dsB4IqIeHvzM3olhZ4wbmwH1jfL64GHW6yl7yJiLXA78KHMfK1f+zXcO/d+4Camjmqfar6ubrsodeTjwH0R8TRwKfDHLdfTN81fJA8CTwJ7mPo/vWAv14+I+4F/BC6KiIMRcQuwGfhARDwPfKBZX5DO0N9ngXcAO5pc+eu+zOXbD0hSPR65S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JB/wvohfWX3nQimQAAAABJRU5ErkJggg==\n",
134 |       "text/plain": [
135 |        "<Figure size 432x288 with 1 Axes>"
136 |       ]
137 |      },
138 |      "metadata": {
139 |       "needs_background": "light"
140 |      },
141 |      "output_type": "display_data"
142 |     }
143 |    ],
144 |    "source": [
145 |     "for team_id in range(22, 25, 1):\n",
146 |     "    url = 'https://statsapi.web.nhl.com/api/v1/teams/{}/roster'.format(team_id)\n",
147 |     "    r = requests.get(url)\n",
148 |     "    roster_data = r.json()\n",
149 |     "    df = pd.json_normalize(roster_data['roster'])\n",
150 |     "    for index, row in df.iterrows():\n",
151 |     "        newrow = row['person.id']\n",
152 |     "        url = 'https://statsapi.web.nhl.com/api/v1/people/{}'.format(newrow)\n",
153 |     "        #print(url)\n",
154 |     "        newerdata = requests.get(url)\n",
155 |     "        player_stats = newerdata.json()\n",
156 |     "        birthday = (player_stats['people'][0]['birthDate'])\n",
157 |     "        newmonth = int(birthday.split('-')[1])\n",
158 |     "        df3 = df3.append({'months': newmonth}, ignore_index=True)\n",
159 |     "        #plt.plot(newmonth)\n",
160 |     "#df1.plot(kind=\"bar\")\n",
161 |     "df3.months.hist()"
162 |    ]
163 |   },
164 |   {
165 |    "cell_type": "code",
166 |    "execution_count": 181,
167 |    "metadata": {},
168 |    "outputs": [
169 |     {
170 |      "data": {
171 |       "text/plain": [
172 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ccee11610>"
173 |       ]
174 |      },
175 |      "execution_count": 181,
176 |      "metadata": {},
177 |      "output_type": "execute_result"
178 |     },
179 |     {
180 |      "data": {
181 |       "image/png": 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ZKzJzhKlfxv1TZpY4+8vM/wb+KyIubIauBL7dx5K6bT9wRUS8s/kavZJCvzBubAPWNtNrgYf6WEvXRcRq4HbgI5n5Wrf2a7i37oPATUyd1T7VfFzd76LUkk8C90TE08AlwJ/0uZ6uaX4iuR94EtjN1P/pgb1dPyLuBf4ZuDAiDkTELcBG4EMR8TzwoWZ+IJ2lvy8A7wK2N7nyN105lo8fkKR6PHOXpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kqyHCXpIL+H3ig/H3ifXw+AAAAAElFTkSuQmCC\n",
182 |       "text/plain": [
183 |        "<Figure size 432x288 with 1 Axes>"
184 |       ]
185 |      },
186 |      "metadata": {
187 |       "needs_background": "light"
188 |      },
189 |      "output_type": "display_data"
190 |     }
191 |    ],
192 |    "source": [
193 |     "for team_id in range(28, 30, 1):\n",
194 |     "    url = 'https://statsapi.web.nhl.com/api/v1/teams/{}/roster'.format(team_id)\n",
195 |     "    r = requests.get(url)\n",
196 |     "    roster_data = r.json()\n",
197 |     "    df = pd.json_normalize(roster_data['roster'])\n",
198 |     "    for index, row in df.iterrows():\n",
199 |     "        newrow = row['person.id']\n",
200 |     "        url = 'https://statsapi.web.nhl.com/api/v1/people/{}'.format(newrow)\n",
201 |     "        #print(url)\n",
202 |     "        newerdata = requests.get(url)\n",
203 |     "        player_stats = newerdata.json()\n",
204 |     "        birthday = (player_stats['people'][0]['birthDate'])\n",
205 |     "        newmonth = int(birthday.split('-')[1])\n",
206 |     "        df3 = df3.append({'months': newmonth}, ignore_index=True)\n",
207 |     "        #plt.plot(newmonth)\n",
208 |     "#df1.plot(kind=\"bar\")\n",
209 |     "df3.months.hist()"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "code",
214 |    "execution_count": 184,
215 |    "metadata": {},
216 |    "outputs": [],
217 |    "source": [
218 |     "df3.to_csv('player_data.csv') "
219 |    ]
220 |   },
221 |   {
222 |    "cell_type": "code",
223 |    "execution_count": 177,
224 |    "metadata": {},
225 |    "outputs": [
226 |     {
227 |      "ename": "KeyError",
228 |      "evalue": "'roster'",
229 |      "output_type": "error",
230 |      "traceback": [
231 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
232 |       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
233 |       "\u001b[0;32m<ipython-input-177-bf18e300bbf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mroster_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson_normalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroster_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'roster'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mnewrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'person.id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
234 |       "\u001b[0;31mKeyError\u001b[0m: 'roster'"
235 |      ]
236 |     }
237 |    ],
238 |    "source": [
239 |     "df4 = pd.DataFrame(columns=['months'])\n",
240 |     "\n",
241 |     "read_for = open('data/teams.txt')\n",
242 |     "for txt_line in read_for:\n",
243 |     "    url = txt_line\n",
244 |     "    r = requests.get(url)\n",
245 |     "    roster_data = r.json()\n",
246 |     "    df = pd.json_normalize(roster_data['roster'])\n",
247 |     "    for index, row in df.iterrows():\n",
248 |     "        newrow = row['person.id']\n",
249 |     "        url = 'https://statsapi.web.nhl.com/api/v1/people/{}'.format(newrow)\n",
250 |     "        #print(url)\n",
251 |     "        newerdata = requests.get(url)\n",
252 |     "        player_stats = newerdata.json()\n",
253 |     "        birthday = (player_stats['people'][0]['birthDate'])\n",
254 |     "        newmonth = int(birthday.split('-')[1])\n",
255 |     "        df3 = df3.append({'months': newmonth}, ignore_index=True)\n",
256 |     "        #plt.plot(newmonth)\n",
257 |     "#df1.plot(kind=\"bar\")\n",
258 |     "df4.months.hist()"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "code",
263 |    "execution_count": 120,
264 |    "metadata": {},
265 |    "outputs": [
266 |     {
267 |      "name": "stdout",
268 |      "output_type": "stream",
269 |      "text": [
270 |       "5\n",
271 |       "11\n",
272 |       "9\n",
273 |       "3\n",
274 |       "5\n",
275 |       "3\n",
276 |       "3\n",
277 |       "1\n",
278 |       "8\n",
279 |       "11\n",
280 |       "2\n",
281 |       "4\n",
282 |       "7\n",
283 |       "2\n",
284 |       "4\n",
285 |       "12\n",
286 |       "1\n",
287 |       "10\n",
288 |       "7\n",
289 |       "7\n",
290 |       "8\n",
291 |       "1\n",
292 |       "9\n",
293 |       "3\n",
294 |       "6\n",
295 |       "11\n",
296 |       "9\n",
297 |       "3\n",
298 |       "8\n",
299 |       "8\n",
300 |       "1\n",
301 |       "7\n",
302 |       "10\n",
303 |       "3\n",
304 |       "3\n",
305 |       "11\n"
306 |      ]
307 |     }
308 |    ],
309 |    "source": [
310 |     "\n",
311 |     "url = 'https://statsapi.web.nhl.com/api/v1/teams/28/roster'.format(team_id)\n",
312 |     "r = requests.get(url)\n",
313 |     "roster_data = r.json()\n",
314 |     "df = pd.json_normalize(roster_data['roster'])\n",
315 |     "for index, row in df.iterrows():\n",
316 |     "    newrow = row['person.id']\n",
317 |     "    url = 'https://statsapi.web.nhl.com/api/v1/people/{}'.format(newrow)\n",
318 |     "    newerdata = requests.get(url)\n",
319 |     "    player_stats = newerdata.json()\n",
320 |     "    birthday = (player_stats['people'][0]['birthDate'])\n",
321 |     "    newmonth = int(birthday.split('-')[1])\n",
322 |     "    print(newmonth)"
323 |    ]
324 |   },
325 |   {
326 |    "cell_type": "markdown",
327 |    "metadata": {},
328 |    "source": [
329 |     "r = requests.get(url)\n",
330 |     "player_data = r.json()\n",
331 |     "df = pd.json_normalize(player_data['roster'])"
332 |    ]
333 |   }
334 |  ],
335 |  "metadata": {
336 |   "kernelspec": {
337 |    "display_name": "Python 3",
338 |    "language": "python",
339 |    "name": "python3"
340 |   },
341 |   "language_info": {
342 |    "codemirror_mode": {
343 |     "name": "ipython",
344 |     "version": 3
345 |    },
346 |    "file_extension": ".py",
347 |    "mimetype": "text/x-python",
348 |    "name": "python",
349 |    "nbconvert_exporter": "python",
350 |    "pygments_lexer": "ipython3",
351 |    "version": "3.7.6"
352 |   }
353 |  },
354 |  "nbformat": 4,
355 |  "nbformat_minor": 4
356 | }
357 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Teach Yourself Data Analytics in 30 Days
 2 | 
 3 | The contents of this repo are meant to support you as you work through the chapters that make up [the free online curriculum in my Data Project](https://stories.thedataproject.net/). 
 4 | 
 5 | The Jupyter Notebooks might not always deliver exactly the same results shown on the website - or in the [book version](https://www.amazon.com/dp/1777721008). That might be because your local Python version isn't completely compatible with the one I used. 
 6 | 
 7 | But whatever the cause, it's a good thing, because it'll force you to understand exactly what the code is doing and how it interacts with its host environment. 
 8 | 
 9 | Good luck!
10 | 


--------------------------------------------------------------------------------
/tropical_storm_data.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import matplotlib as plt\n",
 11 |     "import numpy as np"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 3,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "df = pd.read_csv('all-us-tropical-storms-noaa.csv')"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": null,
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "df.head()"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 4,
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "df = df[(df.Date.str.contains(\"None\")) == False]"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 5,
 44 |    "metadata": {},
 45 |    "outputs": [],
 46 |    "source": [
 47 |     "df['Date'] = df.Date.str.replace('\\$', '')\n",
 48 |     "df['Date'] = df.Date.str.replace('\\*', '')\n",
 49 |     "df['Date'] = df.Date.str.replace('\\#', '')\n",
 50 |     "df['Date'] = df.Date.str.replace('\\%', '')\n",
 51 |     "df['Date'] = df.Date.str.replace('\\&', '')"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 7,
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "df.columns =['Storm#', 'Date', 'Time', 'Lat', 'Lon', \n",
 61 |     "             'MaxWinds', 'LandfallState', 'StormName'] "
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 8,
 67 |    "metadata": {},
 68 |    "outputs": [
 69 |     {
 70 |      "data": {
 71 |       "text/plain": [
 72 |        "Storm#            object\n",
 73 |        "Date              object\n",
 74 |        "Time              object\n",
 75 |        "Lat               object\n",
 76 |        "Lon               object\n",
 77 |        "MaxWinds         float64\n",
 78 |        "LandfallState     object\n",
 79 |        "StormName         object\n",
 80 |        "dtype: object"
 81 |       ]
 82 |      },
 83 |      "execution_count": 8,
 84 |      "metadata": {},
 85 |      "output_type": "execute_result"
 86 |     }
 87 |    ],
 88 |    "source": [
 89 |     "df.dtypes"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 9,
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "text/html": [
100 |        "<div>\n",
101 |        "<style scoped>\n",
102 |        "    .dataframe tbody tr th:only-of-type {\n",
103 |        "        vertical-align: middle;\n",
104 |        "    }\n",
105 |        "\n",
106 |        "    .dataframe tbody tr th {\n",
107 |        "        vertical-align: top;\n",
108 |        "    }\n",
109 |        "\n",
110 |        "    .dataframe thead th {\n",
111 |        "        text-align: right;\n",
112 |        "    }\n",
113 |        "</style>\n",
114 |        "<table border=\"1\" class=\"dataframe\">\n",
115 |        "  <thead>\n",
116 |        "    <tr style=\"text-align: right;\">\n",
117 |        "      <th></th>\n",
118 |        "      <th>Storm#</th>\n",
119 |        "      <th>Date</th>\n",
120 |        "      <th>Time</th>\n",
121 |        "      <th>Lat</th>\n",
122 |        "      <th>Lon</th>\n",
123 |        "      <th>MaxWinds</th>\n",
124 |        "      <th>LandfallState</th>\n",
125 |        "      <th>StormName</th>\n",
126 |        "    </tr>\n",
127 |        "  </thead>\n",
128 |        "  <tbody>\n",
129 |        "    <tr>\n",
130 |        "      <th>1</th>\n",
131 |        "      <td>6</td>\n",
132 |        "      <td>10/19/1851</td>\n",
133 |        "      <td>1500Z</td>\n",
134 |        "      <td>41.1N</td>\n",
135 |        "      <td>71.7W</td>\n",
136 |        "      <td>50.0</td>\n",
137 |        "      <td>NY</td>\n",
138 |        "      <td>NaN</td>\n",
139 |        "    </tr>\n",
140 |        "    <tr>\n",
141 |        "      <th>6</th>\n",
142 |        "      <td>3</td>\n",
143 |        "      <td>8/19/1856</td>\n",
144 |        "      <td>1100Z</td>\n",
145 |        "      <td>34.8</td>\n",
146 |        "      <td>76.4</td>\n",
147 |        "      <td>50.0</td>\n",
148 |        "      <td>NC</td>\n",
149 |        "      <td>NaN</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>7</th>\n",
153 |        "      <td>4</td>\n",
154 |        "      <td>9/30/1857</td>\n",
155 |        "      <td>1000Z</td>\n",
156 |        "      <td>25.8</td>\n",
157 |        "      <td>97</td>\n",
158 |        "      <td>50.0</td>\n",
159 |        "      <td>TX</td>\n",
160 |        "      <td>NaN</td>\n",
161 |        "    </tr>\n",
162 |        "    <tr>\n",
163 |        "      <th>8</th>\n",
164 |        "      <td>3</td>\n",
165 |        "      <td>9/14/1858</td>\n",
166 |        "      <td>1500Z</td>\n",
167 |        "      <td>27.6</td>\n",
168 |        "      <td>82.7</td>\n",
169 |        "      <td>60.0</td>\n",
170 |        "      <td>FL</td>\n",
171 |        "      <td>NaN</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>9</th>\n",
175 |        "      <td>3</td>\n",
176 |        "      <td>9/16/1858</td>\n",
177 |        "      <td>0300Z</td>\n",
178 |        "      <td>35.2</td>\n",
179 |        "      <td>75.2</td>\n",
180 |        "      <td>50.0</td>\n",
181 |        "      <td>NC</td>\n",
182 |        "      <td>NaN</td>\n",
183 |        "    </tr>\n",
184 |        "  </tbody>\n",
185 |        "</table>\n",
186 |        "</div>"
187 |       ],
188 |       "text/plain": [
189 |        "  Storm#        Date   Time    Lat    Lon  MaxWinds LandfallState StormName\n",
190 |        "1      6  10/19/1851  1500Z  41.1N  71.7W      50.0            NY       NaN\n",
191 |        "6      3   8/19/1856  1100Z   34.8   76.4      50.0            NC       NaN\n",
192 |        "7      4   9/30/1857  1000Z   25.8     97      50.0            TX       NaN\n",
193 |        "8      3   9/14/1858  1500Z   27.6   82.7      60.0            FL       NaN\n",
194 |        "9      3   9/16/1858  0300Z   35.2   75.2      50.0            NC       NaN"
195 |       ]
196 |      },
197 |      "execution_count": 9,
198 |      "metadata": {},
199 |      "output_type": "execute_result"
200 |     }
201 |    ],
202 |    "source": [
203 |     "df.head()"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 10,
209 |    "metadata": {},
210 |    "outputs": [],
211 |    "source": [
212 |     "df.Date = pd.to_datetime(df.Date)"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 11,
218 |    "metadata": {},
219 |    "outputs": [],
220 |    "source": [
221 |     "df1 = df[['Date','MaxWinds']]"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": 12,
227 |    "metadata": {},
228 |    "outputs": [
229 |     {
230 |      "data": {
231 |       "text/plain": [
232 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f4db8d9efd0>"
233 |       ]
234 |      },
235 |      "execution_count": 12,
236 |      "metadata": {},
237 |      "output_type": "execute_result"
238 |     },
239 |     {
240 |      "data": {
241 |       "image/png": 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\n",
242 |       "text/plain": [
243 |        "<Figure size 432x288 with 1 Axes>"
244 |       ]
245 |      },
246 |      "metadata": {
247 |       "needs_background": "light"
248 |      },
249 |      "output_type": "display_data"
250 |     }
251 |    ],
252 |    "source": [
253 |     "df1['Date'].hist()"
254 |    ]
255 |   },
256 |   {
257 |    "cell_type": "code",
258 |    "execution_count": 13,
259 |    "metadata": {},
260 |    "outputs": [
261 |     {
262 |      "name": "stdout",
263 |      "output_type": "stream",
264 |      "text": [
265 |       "(362, 2)\n"
266 |      ]
267 |     }
268 |    ],
269 |    "source": [
270 |     "print(df1.shape)"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": 15,
276 |    "metadata": {},
277 |    "outputs": [
278 |     {
279 |      "data": {
280 |       "text/html": [
281 |        "<div>\n",
282 |        "<style scoped>\n",
283 |        "    .dataframe tbody tr th:only-of-type {\n",
284 |        "        vertical-align: middle;\n",
285 |        "    }\n",
286 |        "\n",
287 |        "    .dataframe tbody tr th {\n",
288 |        "        vertical-align: top;\n",
289 |        "    }\n",
290 |        "\n",
291 |        "    .dataframe thead th {\n",
292 |        "        text-align: right;\n",
293 |        "    }\n",
294 |        "</style>\n",
295 |        "<table border=\"1\" class=\"dataframe\">\n",
296 |        "  <thead>\n",
297 |        "    <tr style=\"text-align: right;\">\n",
298 |        "      <th></th>\n",
299 |        "      <th>Date</th>\n",
300 |        "      <th>MaxWinds</th>\n",
301 |        "    </tr>\n",
302 |        "  </thead>\n",
303 |        "  <tbody>\n",
304 |        "    <tr>\n",
305 |        "      <th>1</th>\n",
306 |        "      <td>1851-10-19</td>\n",
307 |        "      <td>50.0</td>\n",
308 |        "    </tr>\n",
309 |        "    <tr>\n",
310 |        "      <th>6</th>\n",
311 |        "      <td>1856-08-19</td>\n",
312 |        "      <td>50.0</td>\n",
313 |        "    </tr>\n",
314 |        "    <tr>\n",
315 |        "      <th>7</th>\n",
316 |        "      <td>1857-09-30</td>\n",
317 |        "      <td>50.0</td>\n",
318 |        "    </tr>\n",
319 |        "    <tr>\n",
320 |        "      <th>8</th>\n",
321 |        "      <td>1858-09-14</td>\n",
322 |        "      <td>60.0</td>\n",
323 |        "    </tr>\n",
324 |        "    <tr>\n",
325 |        "      <th>9</th>\n",
326 |        "      <td>1858-09-16</td>\n",
327 |        "      <td>50.0</td>\n",
328 |        "    </tr>\n",
329 |        "    <tr>\n",
330 |        "      <th>...</th>\n",
331 |        "      <td>...</td>\n",
332 |        "      <td>...</td>\n",
333 |        "    </tr>\n",
334 |        "    <tr>\n",
335 |        "      <th>391</th>\n",
336 |        "      <td>2017-09-27</td>\n",
337 |        "      <td>45.0</td>\n",
338 |        "    </tr>\n",
339 |        "    <tr>\n",
340 |        "      <th>392</th>\n",
341 |        "      <td>2018-05-28</td>\n",
342 |        "      <td>40.0</td>\n",
343 |        "    </tr>\n",
344 |        "    <tr>\n",
345 |        "      <th>393</th>\n",
346 |        "      <td>2018-09-03</td>\n",
347 |        "      <td>45.0</td>\n",
348 |        "    </tr>\n",
349 |        "    <tr>\n",
350 |        "      <th>394</th>\n",
351 |        "      <td>2018-09-03</td>\n",
352 |        "      <td>45.0</td>\n",
353 |        "    </tr>\n",
354 |        "    <tr>\n",
355 |        "      <th>395</th>\n",
356 |        "      <td>2019-09-17</td>\n",
357 |        "      <td>40.0</td>\n",
358 |        "    </tr>\n",
359 |        "  </tbody>\n",
360 |        "</table>\n",
361 |        "<p>362 rows × 2 columns</p>\n",
362 |        "</div>"
363 |       ],
364 |       "text/plain": [
365 |        "          Date  MaxWinds\n",
366 |        "1   1851-10-19      50.0\n",
367 |        "6   1856-08-19      50.0\n",
368 |        "7   1857-09-30      50.0\n",
369 |        "8   1858-09-14      60.0\n",
370 |        "9   1858-09-16      50.0\n",
371 |        "..         ...       ...\n",
372 |        "391 2017-09-27      45.0\n",
373 |        "392 2018-05-28      40.0\n",
374 |        "393 2018-09-03      45.0\n",
375 |        "394 2018-09-03      45.0\n",
376 |        "395 2019-09-17      40.0\n",
377 |        "\n",
378 |        "[362 rows x 2 columns]"
379 |       ]
380 |      },
381 |      "execution_count": 15,
382 |      "metadata": {},
383 |      "output_type": "execute_result"
384 |     }
385 |    ],
386 |    "source": [
387 |     "df1"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 16,
393 |    "metadata": {},
394 |    "outputs": [],
395 |    "source": [
396 |     "df_30 = df1[df1['MaxWinds'].between(30, 39)]\n",
397 |     "df_40 = df1[df1['MaxWinds'].between(40, 49)]\n",
398 |     "df_50 = df1[df1['MaxWinds'].between(50, 59)]\n",
399 |     "df_60 = df1[df1['MaxWinds'].between(60, 79)]"
400 |    ]
401 |   },
402 |   {
403 |    "cell_type": "code",
404 |    "execution_count": 24,
405 |    "metadata": {},
406 |    "outputs": [
407 |     {
408 |      "name": "stdout",
409 |      "output_type": "stream",
410 |      "text": [
411 |       "The number of storms between 30 and 39:  51\n",
412 |       "The number of storms between 40 and 49:  113\n",
413 |       "The number of storms between 50 and 59:  142\n",
414 |       "The number of storms between 60 and 79:  56\n"
415 |      ]
416 |     }
417 |    ],
418 |    "source": [
419 |     "st1 = len(df_30.index)\n",
420 |     "print('The number of storms between 30 and 39: ', st1)\n",
421 |     "st2 = len(df_40.index)\n",
422 |     "print('The number of storms between 40 and 49: ', st2)\n",
423 |     "st3 = len(df_50.index)\n",
424 |     "print('The number of storms between 50 and 59: ', st3)\n",
425 |     "st4 = len(df_60.index)\n",
426 |     "print('The number of storms between 60 and 79: ', st4)"
427 |    ]
428 |   },
429 |   {
430 |    "cell_type": "code",
431 |    "execution_count": 26,
432 |    "metadata": {},
433 |    "outputs": [
434 |     {
435 |      "data": {
436 |       "text/plain": [
437 |        "40.0    71\n",
438 |        "45.0    42\n",
439 |        "Name: MaxWinds, dtype: int64"
440 |       ]
441 |      },
442 |      "execution_count": 26,
443 |      "metadata": {},
444 |      "output_type": "execute_result"
445 |     }
446 |    ],
447 |    "source": [
448 |     "df_40['MaxWinds'].value_counts()"
449 |    ]
450 |   },
451 |   {
452 |    "cell_type": "code",
453 |    "execution_count": 23,
454 |    "metadata": {},
455 |    "outputs": [
456 |     {
457 |      "data": {
458 |       "text/plain": [
459 |        "<matplotlib.legend.Legend at 0x7f4db80b4110>"
460 |       ]
461 |      },
462 |      "execution_count": 23,
463 |      "metadata": {},
464 |      "output_type": "execute_result"
465 |     },
466 |     {
467 |      "data": {
468 |       "image/png": 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\n",
469 |       "text/plain": [
470 |        "<Figure size 432x288 with 1 Axes>"
471 |       ]
472 |      },
473 |      "metadata": {
474 |       "needs_background": "light"
475 |      },
476 |      "output_type": "display_data"
477 |     }
478 |    ],
479 |    "source": [
480 |     "import matplotlib.pyplot as plt\n",
481 |     "fig = plt.figure()\n",
482 |     "ax = plt.subplot(111)\n",
483 |     "df_30['MaxWinds'].plot(ax=ax, label='df_30')\n",
484 |     "df_40['MaxWinds'].plot(ax=ax, label='df_40')\n",
485 |     "df_50['MaxWinds'].plot(ax=ax, label='df_50')\n",
486 |     "df_60['MaxWinds'].plot(ax=ax, label='df_60')\n",
487 |     "ax.set_ylabel('Wind Speed In Knots')\n",
488 |     "ax.set_xlabel('Time Between 1851 and 2019')\n",
489 |     "plt.title('Tropical Storms by Maximum Wind Speeds (knots)')\n",
490 |     "ax.legend()"
491 |    ]
492 |   },
493 |   {
494 |    "cell_type": "code",
495 |    "execution_count": 35,
496 |    "metadata": {},
497 |    "outputs": [
498 |     {
499 |      "data": {
500 |       "text/plain": [
501 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f4db7de7590>"
502 |       ]
503 |      },
504 |      "execution_count": 35,
505 |      "metadata": {},
506 |      "output_type": "execute_result"
507 |     },
508 |     {
509 |      "data": {
510 |       "image/png": 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\n",
511 |       "text/plain": [
512 |        "<Figure size 432x288 with 1 Axes>"
513 |       ]
514 |      },
515 |      "metadata": {
516 |       "needs_background": "light"
517 |      },
518 |      "output_type": "display_data"
519 |     }
520 |    ],
521 |    "source": [
522 |     "df_30['Date'].hist(bins=20)"
523 |    ]
524 |   },
525 |   {
526 |    "cell_type": "code",
527 |    "execution_count": 37,
528 |    "metadata": {},
529 |    "outputs": [
530 |     {
531 |      "data": {
532 |       "text/plain": [
533 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f4db7aa5d90>"
534 |       ]
535 |      },
536 |      "execution_count": 37,
537 |      "metadata": {},
538 |      "output_type": "execute_result"
539 |     },
540 |     {
541 |      "data": {
542 |       "image/png": 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\n",
543 |       "text/plain": [
544 |        "<Figure size 432x288 with 1 Axes>"
545 |       ]
546 |      },
547 |      "metadata": {
548 |       "needs_background": "light"
549 |      },
550 |      "output_type": "display_data"
551 |     }
552 |    ],
553 |    "source": [
554 |     "df_40['Date'].hist(bins=30)"
555 |    ]
556 |   },
557 |   {
558 |    "cell_type": "code",
559 |    "execution_count": 39,
560 |    "metadata": {},
561 |    "outputs": [
562 |     {
563 |      "data": {
564 |       "text/plain": [
565 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f4db78b2d90>"
566 |       ]
567 |      },
568 |      "execution_count": 39,
569 |      "metadata": {},
570 |      "output_type": "execute_result"
571 |     },
572 |     {
573 |      "data": {
574 |       "image/png": 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\n",
575 |       "text/plain": [
576 |        "<Figure size 432x288 with 1 Axes>"
577 |       ]
578 |      },
579 |      "metadata": {
580 |       "needs_background": "light"
581 |      },
582 |      "output_type": "display_data"
583 |     }
584 |    ],
585 |    "source": [
586 |     "df_50['Date'].hist(bins=20)"
587 |    ]
588 |   },
589 |   {
590 |    "cell_type": "code",
591 |    "execution_count": 41,
592 |    "metadata": {},
593 |    "outputs": [
594 |     {
595 |      "data": {
596 |       "text/plain": [
597 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f4db774f890>"
598 |       ]
599 |      },
600 |      "execution_count": 41,
601 |      "metadata": {},
602 |      "output_type": "execute_result"
603 |     },
604 |     {
605 |      "data": {
606 |       "image/png": 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\n",
607 |       "text/plain": [
608 |        "<Figure size 432x288 with 1 Axes>"
609 |       ]
610 |      },
611 |      "metadata": {
612 |       "needs_background": "light"
613 |      },
614 |      "output_type": "display_data"
615 |     }
616 |    ],
617 |    "source": [
618 |     "df_60['Date'].hist(bins=20)"
619 |    ]
620 |   }
621 |  ],
622 |  "metadata": {
623 |   "kernelspec": {
624 |    "display_name": "Python 3",
625 |    "language": "python",
626 |    "name": "python3"
627 |   },
628 |   "language_info": {
629 |    "codemirror_mode": {
630 |     "name": "ipython",
631 |     "version": 3
632 |    },
633 |    "file_extension": ".py",
634 |    "mimetype": "text/x-python",
635 |    "name": "python",
636 |    "nbconvert_exporter": "python",
637 |    "pygments_lexer": "ipython3",
638 |    "version": "3.7.6"
639 |   }
640 |  },
641 |  "nbformat": 4,
642 |  "nbformat_minor": 4
643 | }
644 | 


--------------------------------------------------------------------------------