{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intro to Pandas, Reading Data, and Plotting\n", "\n", "The cell below loads up a few libraries and does some initialization. In this notebook we'll do a few basic data manipulations and see the Pandas formatting for the first time and make some simple graphs.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Standard Magic and startup initializers.\n", "\n", "# Load Numpy\n", "import numpy as np\n", "# Load MatPlotLib\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "# Load Pandas\n", "import pandas as pd\n", "\n", "# This lets us show plots inline and also save PDF plots if we want them\n", "%matplotlib inline\n", "from matplotlib.backends.backend_pdf import PdfPages\n", "matplotlib.style.use('fivethirtyeight')\n", "# Seaborn is a plotting package for Pandas that we'll try out...\n", "import seaborn as sns\n", "\n", "# Make the fonts a little bigger..\n", "font = {'size' : 24}\n", "matplotlib.rc('font', **font)\n", "matplotlib.rcParams['mathtext.fontset'] = 'cm'\n", "matplotlib.rcParams['pdf.fonttype'] = 42\n", "\n", "# These two things are for Pandas, it widens the notebook and lets us display data easily.\n", "from IPython.core.display import display, HTML\n", "display(HTML(\"\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with some real data and Pandas!\n", "\n", "Opening and reading CSV files is very easy with Pandas [Read CSV Documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Open the NBA Salaries file.\n", "\n", "df_nba = pd.read_csv(\"./nba_salaries.csv\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PLAYERPOSITIONTEAM'15-'16 SALARY
0Paul MillsapPFAtlanta Hawks18.671659
1Al HorfordCAtlanta Hawks12.000000
2Tiago SplitterCAtlanta Hawks9.756250
3Jeff TeaguePGAtlanta Hawks8.000000
4Kyle KorverSGAtlanta Hawks5.746479
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" ], "text/plain": [ " PLAYER POSITION TEAM '15-'16 SALARY\n", "0 Paul Millsap PF Atlanta Hawks 18.671659\n", "1 Al Horford C Atlanta Hawks 12.000000\n", "2 Tiago Splitter C Atlanta Hawks 9.756250\n", "3 Jeff Teague PG Atlanta Hawks 8.000000\n", "4 Kyle Korver SG Atlanta Hawks 5.746479" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display gives us a basic table. Note that we can index and slice this in many different ways.\n", "display(df_nba[:5])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PLAYERPOSITIONTEAM'15-'16 SALARY
121Stephen CurryPGGolden State Warriors11.370786
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" ], "text/plain": [ " PLAYER POSITION TEAM '15-'16 SALARY\n", "121 Stephen Curry PG Golden State Warriors 11.370786" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at a specific person... a little clunky.\n", "df_nba.loc[df_nba['PLAYER'] == \"Stephen Curry\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PLAYERPOSITIONTEAM'15-'16 SALARY
3Jeff TeaguePGAtlanta Hawks8.000000
8Dennis SchroderPGAtlanta Hawks1.763400
14Avery BradleyPGBoston Celtics7.730337
15Isaiah ThomasPGBoston Celtics6.912869
18Marcus SmartPGBoston Celtics3.431040
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" ], "text/plain": [ " PLAYER POSITION TEAM '15-'16 SALARY\n", "3 Jeff Teague PG Atlanta Hawks 8.000000\n", "8 Dennis Schroder PG Atlanta Hawks 1.763400\n", "14 Avery Bradley PG Boston Celtics 7.730337\n", "15 Isaiah Thomas PG Boston Celtics 6.912869\n", "18 Marcus Smart PG Boston Celtics 3.431040" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Can filter for a whole set\n", "df_nba.loc[df_nba['POSITION'] == \"PG\"][:5]\n", "\n", "# Again note that we can slice this different ways.." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PLAYERPOSITIONTEAM'15-'16 SALARY
239Eric GordonSGNew Orleans Pelicans15.514031
240Tyreke EvansSGNew Orleans Pelicans10.734586
241Jrue HolidayPGNew Orleans Pelicans10.595507
242Omer AsikCNew Orleans Pelicans9.213483
243Ryan AndersonPFNew Orleans Pelicans8.500000
244Anthony DavisPFNew Orleans Pelicans7.070730
245Alexis AjincaCNew Orleans Pelicans4.389607
246Quincy PondexterSFNew Orleans Pelicans3.382023
247Norris ColePGNew Orleans Pelicans3.036927
248Dante CunninghamPFNew Orleans Pelicans2.850000
249Alonzo GeeSFNew Orleans Pelicans1.320000
250Toney DouglasPGNew Orleans Pelicans1.164858
251Luke BabbittSFNew Orleans Pelicans1.100602
252Kendrick PerkinsCNew Orleans Pelicans0.947276
253Bo McCalebbPGNew Orleans Pelicans0.525093
254Bryce Dejean-JonesSGNew Orleans Pelicans0.169883
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" ], "text/plain": [ " PLAYER POSITION TEAM '15-'16 SALARY\n", "239 Eric Gordon SG New Orleans Pelicans 15.514031\n", "240 Tyreke Evans SG New Orleans Pelicans 10.734586\n", "241 Jrue Holiday PG New Orleans Pelicans 10.595507\n", "242 Omer Asik C New Orleans Pelicans 9.213483\n", "243 Ryan Anderson PF New Orleans Pelicans 8.500000\n", "244 Anthony Davis PF New Orleans Pelicans 7.070730\n", "245 Alexis Ajinca C New Orleans Pelicans 4.389607\n", "246 Quincy Pondexter SF New Orleans Pelicans 3.382023\n", "247 Norris Cole PG New Orleans Pelicans 3.036927\n", "248 Dante Cunningham PF New Orleans Pelicans 2.850000\n", "249 Alonzo Gee SF New Orleans Pelicans 1.320000\n", "250 Toney Douglas PG New Orleans Pelicans 1.164858\n", "251 Luke Babbitt SF New Orleans Pelicans 1.100602\n", "252 Kendrick Perkins C New Orleans Pelicans 0.947276\n", "253 Bo McCalebb PG New Orleans Pelicans 0.525093\n", "254 Bryce Dejean-Jones SG New Orleans Pelicans 0.169883" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Can also see a team...\n", "df_nba.loc[df_nba['TEAM'] == \"New Orleans Pelicans\"]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PLAYERPOSITIONTEAM'15-'16 SALARY
156Chris PaulPGLos Angeles Clippers21.468695
60Derrick RosePGChicago Bulls20.093064
269Russell WestbrookPGOklahoma City Thunder16.744218
74Kyrie IrvingPGCleveland Cavaliers16.407501
400John WallPGWashington Wizards15.851950
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" ], "text/plain": [ " PLAYER POSITION TEAM '15-'16 SALARY\n", "156 Chris Paul PG Los Angeles Clippers 21.468695\n", "60 Derrick Rose PG Chicago Bulls 20.093064\n", "269 Russell Westbrook PG Oklahoma City Thunder 16.744218\n", "74 Kyrie Irving PG Cleveland Cavaliers 16.407501\n", "400 John Wall PG Washington Wizards 15.851950" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at all PGs sorted by salary...\n", "df_nba.loc[df_nba['POSITION'] == \"PG\"].sort_values(\"'15-'16 SALARY\", ascending=False)[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the above sorting does not happen *in place* unless we explicitly tell Pandas to do so -- [Documentation for sort_values](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PLAYERPOSITIONTEAM'15-'16 SALARY
3Jeff TeaguePGAtlanta Hawks8.000000
8Dennis SchroderPGAtlanta Hawks1.763400
14Avery BradleyPGBoston Celtics7.730337
15Isaiah ThomasPGBoston Celtics6.912869
18Marcus SmartPGBoston Celtics3.431040
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" ], "text/plain": [ " PLAYER POSITION TEAM '15-'16 SALARY\n", "3 Jeff Teague PG Atlanta Hawks 8.000000\n", "8 Dennis Schroder PG Atlanta Hawks 1.763400\n", "14 Avery Bradley PG Boston Celtics 7.730337\n", "15 Isaiah Thomas PG Boston Celtics 6.912869\n", "18 Marcus Smart PG Boston Celtics 3.431040" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Once we sort by values it does not stay the same unless we overwrite the table or do it in place...\n", "df_nba.loc[df_nba['POSITION'] == \"PG\"][:5]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Maybe see what the position distribution is...\n", "sns.countplot(df_nba['POSITION'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## We can also use Pandas to read a CSV that is online... \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# We can also read directly from a google sheet if we want. Note that at the end we have to add `/export?gid=1081980213&format=csv`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# The gid field tell us what sheet to load and the format gives us csv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf_class_survey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"https://docs.google.com/spreadsheets/d/1d4C9HEIOkL7x_W4rYCRsflt_Mw7I6DGLUbAAUwwIqUM/export?gid=1081980213&format=csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 695\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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 698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 699\u001b[0m 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\u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1358\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\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 1359\u001b[0m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0;32m-> 1360\u001b[0;31m context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[1;32m 1361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1362\u001b[0m \u001b[0mhttps_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/python/3.7.2_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1315\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1316\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1317\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1318\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/python/3.7.2_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1227\u001b[0m encode_chunked=False):\n\u001b[1;32m 1228\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\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 1230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1231\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\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 1276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1277\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/usr/local/Cellar/python/3.7.2_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1222\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[0;32mraise\u001b[0m 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"\u001b[0;32m/usr/local/Cellar/python/3.7.2_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 954\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 956\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\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 957\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 958\u001b[0m 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\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\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 717\u001b[0m \u001b[0;31m# Break explicitly a reference cycle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "#df_class_survey = pd.read_csv(\"./Data Science Day 1 Questions (Responses) - Form Responses 1.csv\")\n", "\n", "# We can also read directly from a google sheet if we want. Note that at the end we have to add `/export?gid=1081980213&format=csv`\n", "# The gid field tell us what sheet to load and the format gives us csv\n", "df_class_survey = pd.read_csv(\"https://docs.google.com/spreadsheets/d/1d4C9HEIOkL7x_W4rYCRsflt_Mw7I6DGLUbAAUwwIqUM/export?gid=1081980213&format=csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_class_survey[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Maybe see what the position distribution is...\n", "sns.countplot(df_class_survey['I use Jupyter Notebooks'])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Maybe see what the position distribution is...\n", "g = sns.countplot(df_class_survey['I use Jupyter Notebooks'])\n", "g.set_xticklabels(g.get_xticklabels(),rotation=-85)\n", "display(g)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# That was fun, let's try to read some books..." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from urllib.request import urlopen \n", "import re\n", "def read_url(url): \n", " return re.sub('\\\\s+', ' ', urlopen(url).read().decode())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "\n", "# Read two books, fast!\n", "\n", "huck_finn_url = 'https://www.inferentialthinking.com/data/huck_finn.txt'\n", "huck_finn_text = read_url(huck_finn_url)\n", "huck_finn_chapters = huck_finn_text.split('CHAPTER ')[44:]\n", "\n", "little_women_url = 'https://www.inferentialthinking.com/data/little_women.txt'\n", "little_women_text = read_url(little_women_url)\n", "little_women_chapters = little_women_text.split('CHAPTER ')[1:]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'III. WELL, I got a good going-over in the morning from old Miss Watson on account of my clothes; but the widow she didn\\'t scold, but only cleaned off the grease and clay, and looked so sorry that I thought I would behave awhile if I could. Then Miss Watson she took me in the closet and prayed, but nothing come of it. She told me to pray every day, and whatever I asked for I would get it. But it warn\\'t so. I tried it. Once I got a fish-line, but no hooks. It warn\\'t any good to me without hooks. I tried for the hooks three or four times, but somehow I couldn\\'t make it work. By and by, one day, I asked Miss Watson to try for me, but she said I was a fool. She never told me why, and I couldn\\'t make it out no way. I set down one time back in the woods, and had a long think about it. I says to myself, if a body can get anything they pray for, why don\\'t Deacon Winn get back the money he lost on pork? Why can\\'t the widow get back her silver snuffbox that was stole? Why can\\'t Miss Watson fat up? No, says I to my self, there ain\\'t nothing in it. I went and told the widow about it, and she said the thing a body could get by praying for it was \"spiritual gifts.\" This was too many for me, but she told me what she meant--I must help other people, and do everything I could for other people, and look out for them all the time, and never think about myself. This was including Miss Watson, as I took it. I went out in the woods and turned it over in my mind a long time, but I couldn\\'t see no advantage about it--except for the other people; so at last I reckoned I wouldn\\'t worry about it any more, but just let it go. Sometimes the widow would take me one side and talk about Providence in a way to make a body\\'s mouth water; but maybe next day Miss Watson would take hold and knock it all down again. I judged I could see that there was two Providences, and a poor chap would stand considerable show with the widow\\'s Providence, but if Miss Watson\\'s got him there warn\\'t no help for him any more. I thought it all out, and reckoned I would belong to the widow\\'s if he wanted me, though I couldn\\'t make out how he was a-going to be any better off then than what he was before, seeing I was so ignorant, and so kind of low-down and ornery. Pap he hadn\\'t been seen for more than a year, and that was comfortable for me; I didn\\'t want to see him no more. He used to always whale me when he was sober and could get his hands on me; though I used to take to the woods most of the time when he was around. Well, about this time he was found in the river drownded, about twelve mile above town, so people said. They judged it was him, anyway; said this drownded man was just his size, and was ragged, and had uncommon long hair, which was all like pap; but they couldn\\'t make nothing out of the face, because it had been in the water so long it warn\\'t much like a face at all. They said he was floating on his back in the water. They took him and buried him on the bank. But I warn\\'t comfortable long, because I happened to think of something. I knowed mighty well that a drownded man don\\'t float on his back, but on his face. So I knowed, then, that this warn\\'t pap, but a woman dressed up in a man\\'s clothes. So I was uncomfortable again. I judged the old man would turn up again by and by, though I wished he wouldn\\'t. We played robber now and then about a month, and then I resigned. All the boys did. We hadn\\'t robbed nobody, hadn\\'t killed any people, but only just pretended. We used to hop out of the woods and go charging down on hog-drivers and women in carts taking garden stuff to market, but we never hived any of them. Tom Sawyer called the hogs \"ingots,\" and he called the turnips and stuff \"julery,\" and we would go to the cave and powwow over what we had done, and how many people we had killed and marked. But I couldn\\'t see no profit in it. One time Tom sent a boy to run about town with a blazing stick, which he called a slogan (which was the sign for the Gang to get together), and then he said he had got secret news by his spies that next day a whole parcel of Spanish merchants and rich A-rabs was going to camp in Cave Hollow with two hundred elephants, and six hundred camels, and over a thousand \"sumter\" mules, all loaded down with di\\'monds, and they didn\\'t have only a guard of four hundred soldiers, and so we would lay in ambuscade, as he called it, and kill the lot and scoop the things. He said we must slick up our swords and guns, and get ready. He never could go after even a turnip-cart but he must have the swords and guns all scoured up for it, though they was only lath and broomsticks, and you might scour at them till you rotted, and then they warn\\'t worth a mouthful of ashes more than what they was before. I didn\\'t believe we could lick such a crowd of Spaniards and A-rabs, but I wanted to see the camels and elephants, so I was on hand next day, Saturday, in the ambuscade; and when we got the word we rushed out of the woods and down the hill. But there warn\\'t no Spaniards and A-rabs, and there warn\\'t no camels nor no elephants. It warn\\'t anything but a Sunday-school picnic, and only a primer-class at that. We busted it up, and chased the children up the hollow; but we never got anything but some doughnuts and jam, though Ben Rogers got a rag doll, and Jo Harper got a hymn-book and a tract; and then the teacher charged in, and made us drop everything and cut. I didn\\'t see no di\\'monds, and I told Tom Sawyer so. He said there was loads of them there, anyway; and he said there was A-rabs there, too, and elephants and things. I said, why couldn\\'t we see them, then? He said if I warn\\'t so ignorant, but had read a book called Don Quixote, I would know without asking. He said it was all done by enchantment. He said there was hundreds of soldiers there, and elephants and treasure, and so on, but we had enemies which he called magicians; and they had turned the whole thing into an infant Sunday-school, just out of spite. I said, all right; then the thing for us to do was to go for the magicians. Tom Sawyer said I was a numskull. \"Why,\" said he, \"a magician could call up a lot of genies, and they would hash you up like nothing before you could say Jack Robinson. They are as tall as a tree and as big around as a church.\" \"Well,\" I says, \"s\\'pose we got some genies to help _us_--can\\'t we lick the other crowd then?\" \"How you going to get them?\" \"I don\\'t know. How do _they_ get them?\" \"Why, they rub an old tin lamp or an iron ring, and then the genies come tearing in, with the thunder and lightning a-ripping around and the smoke a-rolling, and everything they\\'re told to do they up and do it. They don\\'t think nothing of pulling a shot-tower up by the roots, and belting a Sunday-school superintendent over the head with it--or any other man.\" \"Who makes them tear around so?\" \"Why, whoever rubs the lamp or the ring. They belong to whoever rubs the lamp or the ring, and they\\'ve got to do whatever he says. If he tells them to build a palace forty miles long out of di\\'monds, and fill it full of chewing-gum, or whatever you want, and fetch an emperor\\'s daughter from China for you to marry, they\\'ve got to do it--and they\\'ve got to do it before sun-up next morning, too. And more: they\\'ve got to waltz that palace around over the country wherever you want it, you understand.\" \"Well,\" says I, \"I think they are a pack of flat-heads for not keeping the palace themselves \\'stead of fooling them away like that. And what\\'s more--if I was one of them I would see a man in Jericho before I would drop my business and come to him for the rubbing of an old tin lamp.\" \"How you talk, Huck Finn. Why, you\\'d _have_ to come when he rubbed it, whether you wanted to or not.\" \"What! and I as high as a tree and as big as a church? All right, then; I _would_ come; but I lay I\\'d make that man climb the highest tree there was in the country.\" \"Shucks, it ain\\'t no use to talk to you, Huck Finn. You don\\'t seem to know anything, somehow--perfect saphead.\" I thought all this over for two or three days, and then I reckoned I would see if there was anything in it. I got an old tin lamp and an iron ring, and went out in the woods and rubbed and rubbed till I sweat like an Injun, calculating to build a palace and sell it; but it warn\\'t no use, none of the genies come. So then I judged that all that stuff was only just one of Tom Sawyer\\'s lies. I reckoned he believed in the A-rabs and the elephants, but as for me I think different. It had all the marks of a Sunday-school. 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = counts.cumsum().plot(figsize=(10,8))\n", "ax.set_xlabel(\"Chapter\")\n", "ax.set_ylabel(\"Number of Times\")\n", "#ax.set_ylim((-5,310))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are lots of options for the figures ... Note that here we are using [Pandas Plot](https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.DataFrame.plot.html) which is a wrapper around [MatPlot's Plot](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.plot.html)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = counts.cumsum().plot(figsize=(10,8), fontsize=(5),\n", " lw=2, \n", " markersize=12,\n", " style=['X-','o-.','v--','s:','d:','*-.'])\n", "ax.set_xlabel(\"Chapter\")\n", "ax.set_ylabel(\"Number of Times\")\n", "ax.set_ylim((-5,310))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Now for Little women...\n", "\n", "people = ['Amy', 'Beth', 'Jo', 'Laurie', 'Meg']\n", "people_counts = {pp: np.char.count(little_women_chapters, pp) for pp in people}\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['Amy', 'Beth', 'Jo', 'Laurie', 'Meg'])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "people_counts.keys()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([26, 12, 2, 18, 14, 28, 5, 9, 5, 5, 20, 20, 13, 5, 12, 9, 29,\n", " 30, 7, 7, 1, 15, 6, 9, 6, 5, 4, 0, 1, 5, 3, 33, 6, 2,\n", " 3, 37, 1, 0, 1, 21, 7, 9, 8, 0, 2, 1, 3])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "people_counts['Beth']" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AmyBethJoLaurieMeg
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" ], "text/plain": [ " Amy Beth Jo Laurie Meg\n", "0 23 26 44 0 26\n", "1 13 12 21 0 20\n", "2 2 2 62 16 36\n", "3 14 18 34 0 17\n", "4 6 14 55 35 13" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Make a pandas table...\n", "counts = pd.DataFrame(people_counts)\n", "counts[:5]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = counts.cumsum().plot(figsize=(10,8), fontsize=(15),\n", " lw=2, \n", " markersize=12,\n", " style=['X-','o-.','v--','s:','d:','*-.'])\n", "ax.set_xlabel(\"Chapter\")\n", "ax.set_ylabel(\"Number of Times\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Something more fun...\n", "\n", "Inspired by the [Inferential Thinking Book](https://www.inferentialthinking.com/chapters/01/3/2/Another_Kind_Of_Character) let's do some more analysis on the text that we have loaded up.\n", "\n", "First let's count the number of periods and the total number of characters in each of the books." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I. YOU don't know about me without you have read a\n", "ONE PLAYING PILGRIMS \"Christmas won't be Christmas\n" ] } ], "source": [ "# Recall that each element in the array corresponds to a chapter.\n", "print(huck_finn_chapters[0][:50])\n", "print(little_women_chapters[0][:50])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "chars_periods_huck_finn = pd.DataFrame({\n", " 'Huck Finn Chapter Length':[len(s) for s in huck_finn_chapters],\n", " 'Number of Periods':np.char.count(huck_finn_chapters, '.')\n", " })\n", "chars_periods_little_women = pd.DataFrame({\n", " 'Little Women Chapter Length': [len(s) for s in little_women_chapters],\n", " 'Number of Periods': np.char.count(little_women_chapters, '.')\n", " })" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Huck Finn Chapter Length Number of Periods\n", "0 7026 66\n", "1 11982 117\n", "2 8529 72\n", "3 6799 84\n", "4 8166 91" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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Little Women Chapter LengthNumber of Periods
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" ], "text/plain": [ " Little Women Chapter Length Number of Periods\n", "0 21759 189\n", "1 22148 188\n", "2 20558 231\n", "3 25526 195\n", "4 23395 255" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(chars_periods_huck_finn[:5])\n", "display(chars_periods_little_women[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do we notice about the above? It seems like *Little Women* is significantly longer per chapter than *Huck Finn*. Let's try plotting this relationship on the same graph.\n", "\n", "To do this we are going to use the `scatter` function from [MatPlotLib](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.scatter.html)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'Number of Periods'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2601\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2602\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2603\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'Number of Periods'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\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----> 2\u001b[0;31m plt.scatter(chars_periods_huck_finn[\"Number of Periods\"], \n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mchars_periods_huck_finn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Huck Finn Chapter Length\"\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 4\u001b[0m color='darkblue')\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2915\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2916\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2917\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2918\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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 2919\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2602\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2603\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2604\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2605\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2606\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'Number of Periods'" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6, 6))\n", "plt.scatter(chars_periods_huck_finn[\"Number of Periods\"], \n", " chars_periods_huck_finn[\"Huck Finn Chapter Length\"], \n", " color='darkblue')\n", "\n", "plt.scatter(chars_periods_little_women[\"Number of Periods\"], \n", " chars_periods_little_women[\"Little Women Chapter Length\"], \n", " color='gold')\n", "\n", "plt.xlabel('Number of periods in chapter')\n", "plt.ylabel('Number of characters in chapter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above plot shows us a few things:\n", "1. Little Women is much longer on average than Huck\n", "2. There seems to be a linear relationship between the number of characters and the number of periods\n", "\n", "If we look at all the chapters that have 100 periods we see they have 10,000 - 15,000 characters.. or roughly 100-150 characters per sentence. Seems like a Tweet." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
charactersperiods
0702666
111982117
2852972
3679984
4816691
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" ], "text/plain": [ " characters periods\n", "0 7026 66\n", "1 11982 117\n", "2 8529 72\n", "3 6799 84\n", "4 8166 91" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "43" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's formally find the relationship...\n", "from scipy import stats\n", "\n", "# First let's make the tables the same..\n", "chars_periods_huck_finn.columns = ['characters', 'periods']\n", "chars_periods_little_women.columns = ['characters', 'periods']\n", "display(chars_periods_huck_finn[:5])\n", "len(chars_periods_huck_finn)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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charactersperiods
0702666
111982117
2852972
3679984
4816691
514550125
613218127
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.........
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\n", "

90 rows × 2 columns

\n", "
" ], "text/plain": [ " characters periods\n", "0 7026 66\n", "1 11982 117\n", "2 8529 72\n", "3 6799 84\n", "4 8166 91\n", "5 14550 125\n", "6 13218 127\n", "7 22208 249\n", "8 8081 71\n", "9 7036 70\n", "10 14437 168\n", "11 14665 129\n", "12 10234 83\n", "13 7620 89\n", "14 12046 98\n", "15 17027 183\n", "16 17182 145\n", "17 24031 202\n", "18 17259 105\n", "19 18338 122\n", "20 19172 275\n", "21 10431 70\n", "22 11442 102\n", "23 12041 103\n", "24 14971 107\n", "25 14717 145\n", "26 13608 99\n", "27 18347 160\n", "28 19038 127\n", "29 6119 39\n", ".. ... ...\n", "17 17432 144\n", "18 17338 121\n", "19 14252 145\n", "20 23545 269\n", "21 13708 120\n", "22 23015 247\n", "23 24239 182\n", "24 13526 91\n", "25 21739 150\n", "26 15754 109\n", "27 30992 271\n", "28 26254 233\n", "29 24137 218\n", "30 20723 178\n", "31 23158 224\n", "32 25777 232\n", "33 32496 257\n", "34 21310 201\n", "35 11441 100\n", "36 23524 157\n", "37 26091 206\n", "38 27473 263\n", "39 11368 61\n", "40 26464 187\n", "41 16753 118\n", "42 33202 305\n", "43 10289 95\n", "44 12558 96\n", "45 27094 234\n", "46 40935 392\n", "\n", "[90 rows x 2 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now we are going to concatinate the data together -- this is our first join operation!\n", "\n", "merged = pd.concat([chars_periods_huck_finn, chars_periods_little_women])\n", "merged" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "90" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(merged)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "slope, intercept, r_value, p_value, std_err = stats.linregress(merged['periods'],merged['characters'])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "89.0611539393866" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "slope" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3664.067173280624" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intercept" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9233365944610994" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r_value" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.413147744447107e-38" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p_value" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.9482862802859104" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "std_err" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "line = slope * merged['periods'] + intercept" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can add the line above to our plot using the [plot function](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.plot.html)." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Number of characters in chapter')" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 10))\n", "plt.scatter(chars_periods_huck_finn[\"periods\"], \n", " chars_periods_huck_finn[\"characters\"], \n", " color='darkblue')\n", "\n", "plt.scatter(chars_periods_little_women[\"periods\"], \n", " chars_periods_little_women[\"characters\"], \n", " color='gold')\n", "\n", "plt.plot(merged['periods'], line, lw=1, ls=':')\n", "\n", "plt.xlabel('Number of periods in chapter')\n", "plt.ylabel('Number of characters in chapter')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }