Skip to content

Instantly share code, notes, and snippets.

@adilek

adilek/pandas-sql_14.ipynb

Created June 6, 2018 15:05
Show Gist options
  • Save adilek/4e82c0be08ab64c73a476c4d188f10e9 to your computer and use it in GitHub Desktop.
Save adilek/4e82c0be08ab64c73a476c4d188f10e9 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
pandas-sql_14.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"course_user = pd.merge(at_users, courses, left_on=\"course_id\", right_on=\"id\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>course_id</th>\n",
" <th>id_x</th>\n",
" <th>username</th>\n",
" <th>first_name</th>\n",
" <th>last_name</th>\n",
" <th>birth_year</th>\n",
" <th>points</th>\n",
" <th>email</th>\n",
" <th>id_y</th>\n",
" <th>course_name</th>\n",
" <th>instructor</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>john</td>\n",
" <td>John</td>\n",
" <td>Smith</td>\n",
" <td>1985</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>carter.jose</td>\n",
" <td>Carter</td>\n",
" <td>Jose</td>\n",
" <td>1992</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35</td>\n",
" <td>2</td>\n",
" <td>35</td>\n",
" <td>isaac.jaxson</td>\n",
" <td>Isaac</td>\n",
" <td>Jaxson</td>\n",
" <td>1997</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>dylan.theodore</td>\n",
" <td>Dylan</td>\n",
" <td>Theodore</td>\n",
" <td>1991</td>\n",
" <td>1</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>christopher.parker</td>\n",
" <td>Christopher</td>\n",
" <td>Parker</td>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>44</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>ryan.tyler</td>\n",
" <td>Ryan</td>\n",
" <td>Tyler</td>\n",
" <td>1996</td>\n",
" <td>1</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>45</td>\n",
" <td>2</td>\n",
" <td>45</td>\n",
" <td>jaxon.ayden</td>\n",
" <td>Jaxon</td>\n",
" <td>Ayden</td>\n",
" <td>1987</td>\n",
" <td>11</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>ann</td>\n",
" <td>Anna</td>\n",
" <td>Johnson</td>\n",
" <td>1964</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>noah.eli</td>\n",
" <td>Noah</td>\n",
" <td>Eli</td>\n",
" <td>1980</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>23</td>\n",
" <td>2</td>\n",
" <td>23</td>\n",
" <td>david.angel</td>\n",
" <td>David</td>\n",
" <td>Angel</td>\n",
" <td>1989</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>joseph.asher</td>\n",
" <td>Joseph</td>\n",
" <td>Asher</td>\n",
" <td>1989</td>\n",
" <td>19</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>32</td>\n",
" <td>2</td>\n",
" <td>32</td>\n",
" <td>john.cooper</td>\n",
" <td>John</td>\n",
" <td>Cooper</td>\n",
" <td>1996</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>33</td>\n",
" <td>2</td>\n",
" <td>33</td>\n",
" <td>luke.gavin</td>\n",
" <td>Luke</td>\n",
" <td>Gavin</td>\n",
" <td>1980</td>\n",
" <td>15</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>39</td>\n",
" <td>2</td>\n",
" <td>39</td>\n",
" <td>joshua.chase</td>\n",
" <td>Joshua</td>\n",
" <td>Chase</td>\n",
" <td>1990</td>\n",
" <td>17</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>christopher.parker</td>\n",
" <td>Christopher</td>\n",
" <td>Parker</td>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>46</td>\n",
" <td>2</td>\n",
" <td>46</td>\n",
" <td>levi.elias</td>\n",
" <td>Levi</td>\n",
" <td>Elias</td>\n",
" <td>1992</td>\n",
" <td>11</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>john</td>\n",
" <td>John</td>\n",
" <td>Smith</td>\n",
" <td>1985</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>jack</td>\n",
" <td>Jack</td>\n",
" <td>Wilson</td>\n",
" <td>1994</td>\n",
" <td>15</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>samuel.dominic</td>\n",
" <td>Samuel</td>\n",
" <td>Dominic</td>\n",
" <td>1983</td>\n",
" <td>3</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>27</td>\n",
" <td>2</td>\n",
" <td>27</td>\n",
" <td>owen.leo</td>\n",
" <td>Owen</td>\n",
" <td>Leo</td>\n",
" <td>1981</td>\n",
" <td>17</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>29</td>\n",
" <td>2</td>\n",
" <td>29</td>\n",
" <td>gabriel.jace</td>\n",
" <td>Gabriel</td>\n",
" <td>Jace</td>\n",
" <td>1981</td>\n",
" <td>8</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>31</td>\n",
" <td>2</td>\n",
" <td>31</td>\n",
" <td>jayden.ian</td>\n",
" <td>Jayden</td>\n",
" <td>Ian</td>\n",
" <td>1998</td>\n",
" <td>10</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>dylan.theodore</td>\n",
" <td>Dylan</td>\n",
" <td>Theodore</td>\n",
" <td>1991</td>\n",
" <td>1</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>christopher.parker</td>\n",
" <td>Christopher</td>\n",
" <td>Parker</td>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>elijah.adrian</td>\n",
" <td>Elijah</td>\n",
" <td>Adrian</td>\n",
" <td>1994</td>\n",
" <td>6</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>21</td>\n",
" <td>jackson.colton</td>\n",
" <td>Jackson</td>\n",
" <td>Colton</td>\n",
" <td>1993</td>\n",
" <td>13</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>joseph.asher</td>\n",
" <td>Joseph</td>\n",
" <td>Asher</td>\n",
" <td>1989</td>\n",
" <td>19</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
" <td>sebastian.adam</td>\n",
" <td>Sebastian</td>\n",
" <td>Adam</td>\n",
" <td>1980</td>\n",
" <td>10</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>30</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>carter.jose</td>\n",
" <td>Carter</td>\n",
" <td>Jose</td>\n",
" <td>1992</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>39</td>\n",
" <td>2</td>\n",
" <td>39</td>\n",
" <td>joshua.chase</td>\n",
" <td>Joshua</td>\n",
" <td>Chase</td>\n",
" <td>1990</td>\n",
" <td>17</td>\n",
" <td>[email protected]</td>\n",
" <td>2</td>\n",
" <td>Data Science applications</td>\n",
" <td>Michael Jackson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>52</td>\n",
" <td>6</td>\n",
" <td>52</td>\n",
" <td>thomas.brandon</td>\n",
" <td>Thomas</td>\n",
" <td>Brandon</td>\n",
" <td>1988</td>\n",
" <td>9</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>john</td>\n",
" <td>John</td>\n",
" <td>Smith</td>\n",
" <td>1985</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>william.connor</td>\n",
" <td>William</td>\n",
" <td>Connor</td>\n",
" <td>1996</td>\n",
" <td>8</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>james.jonathan</td>\n",
" <td>James</td>\n",
" <td>Jonathan</td>\n",
" <td>1993</td>\n",
" <td>9</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>lucas.nolan</td>\n",
" <td>Lucas</td>\n",
" <td>Nolan</td>\n",
" <td>1994</td>\n",
" <td>12</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>29</td>\n",
" <td>6</td>\n",
" <td>29</td>\n",
" <td>gabriel.jace</td>\n",
" <td>Gabriel</td>\n",
" <td>Jace</td>\n",
" <td>1981</td>\n",
" <td>8</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>45</td>\n",
" <td>6</td>\n",
" <td>45</td>\n",
" <td>jaxon.ayden</td>\n",
" <td>Jaxon</td>\n",
" <td>Ayden</td>\n",
" <td>1987</td>\n",
" <td>11</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>50</td>\n",
" <td>6</td>\n",
" <td>50</td>\n",
" <td>christian.sawyer</td>\n",
" <td>Christian</td>\n",
" <td>Sawyer</td>\n",
" <td>1993</td>\n",
" <td>12</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>51</td>\n",
" <td>6</td>\n",
" <td>51</td>\n",
" <td>isaiah.roman</td>\n",
" <td>Isaiah</td>\n",
" <td>Roman</td>\n",
" <td>1987</td>\n",
" <td>13</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>ann</td>\n",
" <td>Anna</td>\n",
" <td>Johnson</td>\n",
" <td>1964</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>ethan.hudson</td>\n",
" <td>Ethan</td>\n",
" <td>Hudson</td>\n",
" <td>1988</td>\n",
" <td>8</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>16</td>\n",
" <td>oliver.nicholas</td>\n",
" <td>Oliver</td>\n",
" <td>Nicholas</td>\n",
" <td>1994</td>\n",
" <td>4</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>30</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" <td>carter.jose</td>\n",
" <td>Carter</td>\n",
" <td>Jose</td>\n",
" <td>1992</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>38</td>\n",
" <td>6</td>\n",
" <td>38</td>\n",
" <td>andrew.ezra</td>\n",
" <td>Andrew</td>\n",
" <td>Ezra</td>\n",
" <td>1980</td>\n",
" <td>10</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>42</td>\n",
" <td>6</td>\n",
" <td>42</td>\n",
" <td>jack.kevin</td>\n",
" <td>Jack</td>\n",
" <td>Kevin</td>\n",
" <td>1996</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>48</td>\n",
" <td>6</td>\n",
" <td>48</td>\n",
" <td>caleb.leonardo</td>\n",
" <td>Caleb</td>\n",
" <td>Leonardo</td>\n",
" <td>1988</td>\n",
" <td>9</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>49</td>\n",
" <td>6</td>\n",
" <td>49</td>\n",
" <td>hunter.greyson</td>\n",
" <td>Hunter</td>\n",
" <td>Greyson</td>\n",
" <td>1995</td>\n",
" <td>20</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>clara</td>\n",
" <td>Clara</td>\n",
" <td>Setkin</td>\n",
" <td>1988</td>\n",
" <td>8</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>noah.eli</td>\n",
" <td>Noah</td>\n",
" <td>Eli</td>\n",
" <td>1980</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>benjamin.cameron</td>\n",
" <td>Benjamin</td>\n",
" <td>Cameron</td>\n",
" <td>1983</td>\n",
" <td>11</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>michael.mateo</td>\n",
" <td>Michael</td>\n",
" <td>Mateo</td>\n",
" <td>1984</td>\n",
" <td>2</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>20</td>\n",
" <td>aiden.jordan</td>\n",
" <td>Aiden</td>\n",
" <td>Jordan</td>\n",
" <td>1997</td>\n",
" <td>10</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>25</td>\n",
" <td>6</td>\n",
" <td>25</td>\n",
" <td>samuel.dominic</td>\n",
" <td>Samuel</td>\n",
" <td>Dominic</td>\n",
" <td>1983</td>\n",
" <td>3</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>35</td>\n",
" <td>6</td>\n",
" <td>35</td>\n",
" <td>isaac.jaxson</td>\n",
" <td>Isaac</td>\n",
" <td>Jaxson</td>\n",
" <td>1997</td>\n",
" <td>5</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>37</td>\n",
" <td>6</td>\n",
" <td>37</td>\n",
" <td>wyatt.jason</td>\n",
" <td>Wyatt</td>\n",
" <td>Jason</td>\n",
" <td>2000</td>\n",
" <td>3</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>38</td>\n",
" <td>6</td>\n",
" <td>38</td>\n",
" <td>andrew.ezra</td>\n",
" <td>Andrew</td>\n",
" <td>Ezra</td>\n",
" <td>1980</td>\n",
" <td>10</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>40</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>christopher.parker</td>\n",
" <td>Christopher</td>\n",
" <td>Parker</td>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>52</td>\n",
" <td>6</td>\n",
" <td>52</td>\n",
" <td>thomas.brandon</td>\n",
" <td>Thomas</td>\n",
" <td>Brandon</td>\n",
" <td>1988</td>\n",
" <td>9</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>54</td>\n",
" <td>6</td>\n",
" <td>54</td>\n",
" <td>lincoln.kayden</td>\n",
" <td>Lincoln</td>\n",
" <td>Kayden</td>\n",
" <td>1997</td>\n",
" <td>3</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>55</td>\n",
" <td>6</td>\n",
" <td>55</td>\n",
" <td>charles.ryder</td>\n",
" <td>Charles</td>\n",
" <td>Ryder</td>\n",
" <td>1984</td>\n",
" <td>15</td>\n",
" <td>[email protected]</td>\n",
" <td>6</td>\n",
" <td>Intro to Deep Learning</td>\n",
" <td>Emma Watson</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>206 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" user_id course_id id_x username first_name last_name \\\n",
"0 1 2 1 john John Smith \n",
"1 30 2 30 carter.jose Carter Jose \n",
"2 35 2 35 isaac.jaxson Isaac Jaxson \n",
"3 36 2 36 dylan.theodore Dylan Theodore \n",
"4 40 2 40 christopher.parker Christopher Parker \n",
"5 44 2 44 ryan.tyler Ryan Tyler \n",
"6 45 2 45 jaxon.ayden Jaxon Ayden \n",
"7 4 2 4 ann Anna Johnson \n",
"8 5 2 5 noah.eli Noah Eli \n",
"9 23 2 23 david.angel David Angel \n",
"10 24 2 24 joseph.asher Joseph Asher \n",
"11 32 2 32 john.cooper John Cooper \n",
"12 33 2 33 luke.gavin Luke Gavin \n",
"13 39 2 39 joshua.chase Joshua Chase \n",
"14 40 2 40 christopher.parker Christopher Parker \n",
"15 46 2 46 levi.elias Levi Elias \n",
"16 1 2 1 john John Smith \n",
"17 2 2 2 jack Jack Wilson \n",
"18 25 2 25 samuel.dominic Samuel Dominic \n",
"19 27 2 27 owen.leo Owen Leo \n",
"20 29 2 29 gabriel.jace Gabriel Jace \n",
"21 31 2 31 jayden.ian Jayden Ian \n",
"22 36 2 36 dylan.theodore Dylan Theodore \n",
"23 40 2 40 christopher.parker Christopher Parker \n",
"24 13 2 13 elijah.adrian Elijah Adrian \n",
"25 21 2 21 jackson.colton Jackson Colton \n",
"26 24 2 24 joseph.asher Joseph Asher \n",
"27 28 2 28 sebastian.adam Sebastian Adam \n",
"28 30 2 30 carter.jose Carter Jose \n",
"29 39 2 39 joshua.chase Joshua Chase \n",
".. ... ... ... ... ... ... \n",
"176 52 6 52 thomas.brandon Thomas Brandon \n",
"177 1 6 1 john John Smith \n",
"178 7 6 7 william.connor William Connor \n",
"179 9 6 9 james.jonathan James Jonathan \n",
"180 18 6 18 lucas.nolan Lucas Nolan \n",
"181 29 6 29 gabriel.jace Gabriel Jace \n",
"182 45 6 45 jaxon.ayden Jaxon Ayden \n",
"183 50 6 50 christian.sawyer Christian Sawyer \n",
"184 51 6 51 isaiah.roman Isaiah Roman \n",
"185 4 6 4 ann Anna Johnson \n",
"186 14 6 14 ethan.hudson Ethan Hudson \n",
"187 16 6 16 oliver.nicholas Oliver Nicholas \n",
"188 30 6 30 carter.jose Carter Jose \n",
"189 38 6 38 andrew.ezra Andrew Ezra \n",
"190 42 6 42 jack.kevin Jack Kevin \n",
"191 48 6 48 caleb.leonardo Caleb Leonardo \n",
"192 49 6 49 hunter.greyson Hunter Greyson \n",
"193 3 6 3 clara Clara Setkin \n",
"194 5 6 5 noah.eli Noah Eli \n",
"195 10 6 10 benjamin.cameron Benjamin Cameron \n",
"196 12 6 12 michael.mateo Michael Mateo \n",
"197 20 6 20 aiden.jordan Aiden Jordan \n",
"198 25 6 25 samuel.dominic Samuel Dominic \n",
"199 35 6 35 isaac.jaxson Isaac Jaxson \n",
"200 37 6 37 wyatt.jason Wyatt Jason \n",
"201 38 6 38 andrew.ezra Andrew Ezra \n",
"202 40 6 40 christopher.parker Christopher Parker \n",
"203 52 6 52 thomas.brandon Thomas Brandon \n",
"204 54 6 54 lincoln.kayden Lincoln Kayden \n",
"205 55 6 55 charles.ryder Charles Ryder \n",
"\n",
" birth_year points email id_y \\\n",
"0 1985 5 [email protected] 2 \n",
"1 1992 2 [email protected] 2 \n",
"2 1997 5 [email protected] 2 \n",
"3 1991 1 [email protected] 2 \n",
"4 1982 4 [email protected] 2 \n",
"5 1996 1 [email protected] 2 \n",
"6 1987 11 [email protected] 2 \n",
"7 1964 10 NaN 2 \n",
"8 1980 2 [email protected] 2 \n",
"9 1989 5 [email protected] 2 \n",
"10 1989 19 [email protected] 2 \n",
"11 1996 5 [email protected] 2 \n",
"12 1980 15 [email protected] 2 \n",
"13 1990 17 [email protected] 2 \n",
"14 1982 4 [email protected] 2 \n",
"15 1992 11 [email protected] 2 \n",
"16 1985 5 [email protected] 2 \n",
"17 1994 15 [email protected] 2 \n",
"18 1983 3 [email protected] 2 \n",
"19 1981 17 [email protected] 2 \n",
"20 1981 8 [email protected] 2 \n",
"21 1998 10 [email protected] 2 \n",
"22 1991 1 [email protected] 2 \n",
"23 1982 4 [email protected] 2 \n",
"24 1994 6 [email protected] 2 \n",
"25 1993 13 [email protected] 2 \n",
"26 1989 19 [email protected] 2 \n",
"27 1980 10 [email protected] 2 \n",
"28 1992 2 [email protected] 2 \n",
"29 1990 17 [email protected] 2 \n",
".. ... ... ... ... \n",
"176 1988 9 [email protected] 6 \n",
"177 1985 5 [email protected] 6 \n",
"178 1996 8 [email protected] 6 \n",
"179 1993 9 [email protected] 6 \n",
"180 1994 12 [email protected] 6 \n",
"181 1981 8 [email protected] 6 \n",
"182 1987 11 [email protected] 6 \n",
"183 1993 12 [email protected] 6 \n",
"184 1987 13 [email protected] 6 \n",
"185 1964 10 NaN 6 \n",
"186 1988 8 [email protected] 6 \n",
"187 1994 4 [email protected] 6 \n",
"188 1992 2 [email protected] 6 \n",
"189 1980 10 [email protected] 6 \n",
"190 1996 2 [email protected] 6 \n",
"191 1988 9 [email protected] 6 \n",
"192 1995 20 [email protected] 6 \n",
"193 1988 8 [email protected] 6 \n",
"194 1980 2 [email protected] 6 \n",
"195 1983 11 [email protected] 6 \n",
"196 1984 2 [email protected] 6 \n",
"197 1997 10 [email protected] 6 \n",
"198 1983 3 [email protected] 6 \n",
"199 1997 5 [email protected] 6 \n",
"200 2000 3 [email protected] 6 \n",
"201 1980 10 [email protected] 6 \n",
"202 1982 4 [email protected] 6 \n",
"203 1988 9 [email protected] 6 \n",
"204 1997 3 [email protected] 6 \n",
"205 1984 15 [email protected] 6 \n",
"\n",
" course_name instructor \n",
"0 Data Science applications Michael Jackson \n",
"1 Data Science applications Michael Jackson \n",
"2 Data Science applications Michael Jackson \n",
"3 Data Science applications Michael Jackson \n",
"4 Data Science applications Michael Jackson \n",
"5 Data Science applications Michael Jackson \n",
"6 Data Science applications Michael Jackson \n",
"7 Data Science applications Michael Jackson \n",
"8 Data Science applications Michael Jackson \n",
"9 Data Science applications Michael Jackson \n",
"10 Data Science applications Michael Jackson \n",
"11 Data Science applications Michael Jackson \n",
"12 Data Science applications Michael Jackson \n",
"13 Data Science applications Michael Jackson \n",
"14 Data Science applications Michael Jackson \n",
"15 Data Science applications Michael Jackson \n",
"16 Data Science applications Michael Jackson \n",
"17 Data Science applications Michael Jackson \n",
"18 Data Science applications Michael Jackson \n",
"19 Data Science applications Michael Jackson \n",
"20 Data Science applications Michael Jackson \n",
"21 Data Science applications Michael Jackson \n",
"22 Data Science applications Michael Jackson \n",
"23 Data Science applications Michael Jackson \n",
"24 Data Science applications Michael Jackson \n",
"25 Data Science applications Michael Jackson \n",
"26 Data Science applications Michael Jackson \n",
"27 Data Science applications Michael Jackson \n",
"28 Data Science applications Michael Jackson \n",
"29 Data Science applications Michael Jackson \n",
".. ... ... \n",
"176 Intro to Deep Learning Emma Watson \n",
"177 Intro to Deep Learning Emma Watson \n",
"178 Intro to Deep Learning Emma Watson \n",
"179 Intro to Deep Learning Emma Watson \n",
"180 Intro to Deep Learning Emma Watson \n",
"181 Intro to Deep Learning Emma Watson \n",
"182 Intro to Deep Learning Emma Watson \n",
"183 Intro to Deep Learning Emma Watson \n",
"184 Intro to Deep Learning Emma Watson \n",
"185 Intro to Deep Learning Emma Watson \n",
"186 Intro to Deep Learning Emma Watson \n",
"187 Intro to Deep Learning Emma Watson \n",
"188 Intro to Deep Learning Emma Watson \n",
"189 Intro to Deep Learning Emma Watson \n",
"190 Intro to Deep Learning Emma Watson \n",
"191 Intro to Deep Learning Emma Watson \n",
"192 Intro to Deep Learning Emma Watson \n",
"193 Intro to Deep Learning Emma Watson \n",
"194 Intro to Deep Learning Emma Watson \n",
"195 Intro to Deep Learning Emma Watson \n",
"196 Intro to Deep Learning Emma Watson \n",
"197 Intro to Deep Learning Emma Watson \n",
"198 Intro to Deep Learning Emma Watson \n",
"199 Intro to Deep Learning Emma Watson \n",
"200 Intro to Deep Learning Emma Watson \n",
"201 Intro to Deep Learning Emma Watson \n",
"202 Intro to Deep Learning Emma Watson \n",
"203 Intro to Deep Learning Emma Watson \n",
"204 Intro to Deep Learning Emma Watson \n",
"205 Intro to Deep Learning Emma Watson \n",
"\n",
"[206 rows x 12 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"course_user"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment