a1-pandas-and-uci-adult-dataset.ipynb

{"cells":[{"metadata":{"_uuid":"570fb89bb4f7838b1d0fdff70b2935790e5dbdbe"},"cell_type":"markdown","source":"<center>\n<img src=\"https://habrastorage.org/files/fd4/502/43d/fd450243dd604b81b9713213a247aa20.jpg\">\n    \n## [mlcourse.ai](mlcourse.ai) – Open Machine Learning Course \n\n<center>Author: [Yury Kashnitskiy](http://yorko.github.io) <br>\nTranslated and edited by [Sergey Isaev](https://www.linkedin.com/in/isvforall/), [Artem Trunov](https://www.linkedin.com/in/datamove/), [Anastasia Manokhina](https://www.linkedin.com/in/anastasiamanokhina/), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/) <br>All content is distributed under the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license."},{"metadata":{"_uuid":"998fc5f76227ab1df9460a7b05c508304b14b3a6"},"cell_type":"markdown","source":"# <center> Assignment #1 (demo)\n## <center>  Exploratory data analysis with Pandas\n"},{"metadata":{"_uuid":"89ef93231815456f4e0bc5dcc00b094aee800993"},"cell_type":"markdown","source":"**In this task you should use Pandas to answer a few questions about the [Adult](https://archive.ics.uci.edu/ml/datasets/Adult) dataset. (You don't have to download the data – it's already here). Choose the answers in the [web-form](https://docs.google.com/forms/d/1uY7MpI2trKx6FLWZte0uVh3ULV4Cm_tDud0VDFGCOKg). This is a demo version of an assignment, so by submitting the form, you'll see a link to the solution .ipynb file.**"},{"metadata":{"_uuid":"c322301ac858c748f8f80e63cf6f734fc70dde30"},"cell_type":"markdown","source":"Unique values of all features (for more information, please see the links above):\n- `age`: continuous.\n- `workclass`: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n- `fnlwgt`: continuous.\n- `education`: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n- `education-num`: continuous.\n- `marital-status`: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n- `occupation`: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n- `relationship`: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n- `race`: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n- `sex`: Female, Male.\n- `capital-gain`: continuous.\n- `capital-loss`: continuous.\n- `hours-per-week`: continuous.\n- `native-country`: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.   \n- `salary`: >50K,<=50K"},{"metadata":{"trusted":true,"_uuid":"d3eb2411e23b7db11e4cbbb498a42752442a6f4c"},"cell_type":"code","source":"import pandas as pd","execution_count":2,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c5119d1b8151029b4ff57735c4279b795ae492a2"},"cell_type":"code","source":"data = pd.read_csv('../input/adult.data.csv')\ndata.head()","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"   age         workclass  fnlwgt  ...   hours-per-week  native-country salary\n0   39         State-gov   77516  ...               40   United-States  <=50K\n1   50  Self-emp-not-inc   83311  ...               13   United-States  <=50K\n2   38           Private  215646  ...               40   United-States  <=50K\n3   53           Private  234721  ...               40   United-States  <=50K\n4   28           Private  338409  ...               40            Cuba  <=50K\n\n[5 rows x 15 columns]","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>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>education</th>\n      <th>education-num</th>\n      <th>marital-status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capital-gain</th>\n      <th>capital-loss</th>\n      <th>hours-per-week</th>\n      <th>native-country</th>\n      <th>salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>39</td>\n      <td>State-gov</td>\n      <td>77516</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>2174</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>Self-emp-not-inc</td>\n      <td>83311</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>215646</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Handlers-cleaners</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>53</td>\n      <td>Private</td>\n      <td>234721</td>\n      <td>11th</td>\n      <td>7</td>\n      <td>Married-civ-spouse</td>\n      <td>Handlers-cleaners</td>\n      <td>Husband</td>\n      <td>Black</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>28</td>\n      <td>Private</td>\n      <td>338409</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Wife</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Cuba</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"6b19853f936853d40971b56f4d298ab55ee78cac"},"cell_type":"markdown","source":"**1. How many men and women (*sex* feature) are represented in this dataset?** "},{"metadata":{"trusted":true,"_uuid":"d32cafcbe966dbdfa40c3017d16d6f8c96bfb3e2"},"cell_type":"code","source":"# You code here\ndata['sex'].value_counts()","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"Male      21790\nFemale    10771\nName: sex, dtype: int64"},"metadata":{}}]},{"metadata":{"_uuid":"4748793234a5d325ea60d13331ecf93e29774523"},"cell_type":"markdown","source":"**2. What is the average age (*age* feature) of women?**"},{"metadata":{"trusted":true,"_uuid":"64f60d928855107d8980875656dcdf3587d91646"},"cell_type":"code","source":"# You code here\ndata[data['sex']=='Female'].mean()","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"age                   36.858230\nfnlwgt            185746.311206\neducation-num         10.035744\ncapital-gain         568.410547\ncapital-loss          61.187633\nhours-per-week        36.410361\ndtype: float64"},"metadata":{}}]},{"metadata":{"_uuid":"80e87c421c348067e88769c64e554206c51727d1"},"cell_type":"markdown","source":"**3. What is the percentage of German citizens (*native-country* feature)?**"},{"metadata":{"trusted":true,"_uuid":"ca9807509692638bd945671ed7d8b4a5ad1f3626"},"cell_type":"code","source":"# You code here\ndata['native-country'].value_counts(normalize=True)","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"United-States                 0.895857\nMexico                        0.019748\n?                             0.017905\nPhilippines                   0.006081\nGermany                       0.004207\nCanada                        0.003716\nPuerto-Rico                   0.003501\nEl-Salvador                   0.003255\nIndia                         0.003071\nCuba                          0.002918\nEngland                       0.002764\nJamaica                       0.002488\nSouth                         0.002457\nChina                         0.002303\nItaly                         0.002242\nDominican-Republic            0.002150\nVietnam                       0.002058\nGuatemala                     0.001966\nJapan                         0.001904\nPoland                        0.001843\nColumbia                      0.001812\nTaiwan                        0.001566\nHaiti                         0.001351\nIran                          0.001321\nPortugal                      0.001136\nNicaragua                     0.001044\nPeru                          0.000952\nFrance                        0.000891\nGreece                        0.000891\nEcuador                       0.000860\nIreland                       0.000737\nHong                          0.000614\nTrinadad&Tobago               0.000584\nCambodia                      0.000584\nThailand                      0.000553\nLaos                          0.000553\nYugoslavia                    0.000491\nOutlying-US(Guam-USVI-etc)    0.000430\nHungary                       0.000399\nHonduras                      0.000399\nScotland                      0.000369\nHoland-Netherlands            0.000031\nName: native-country, dtype: float64"},"metadata":{}}]},{"metadata":{"_uuid":"be9911cf525df4fca2c1d14b8720bc8a82f462e7"},"cell_type":"markdown","source":"**4-5. What are the mean and standard deviation of age for those who earn more than 50K per year (*salary* feature) and those who earn less than 50K per year? **"},{"metadata":{"trusted":true,"_uuid":"97948f0115f949913d211627151b5605bcd204fd"},"cell_type":"code","source":"# You code here\n#data[data['salary']=='>50K']['age'].describe()\n#data[data['salary']=='<=50K']['age'].describe()\n\ncolumn = ['age']\ndata.groupby(['salary'])[column].describe()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"            age                                                    \n          count       mean        std   min   25%   50%   75%   max\nsalary                                                             \n<=50K   24720.0  36.783738  14.020088  17.0  25.0  34.0  46.0  90.0\n>50K     7841.0  44.249841  10.519028  19.0  36.0  44.0  51.0  90.0","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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">age</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>&lt;=50K</th>\n      <td>24720.0</td>\n      <td>36.783738</td>\n      <td>14.020088</td>\n      <td>17.0</td>\n      <td>25.0</td>\n      <td>34.0</td>\n      <td>46.0</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>&gt;50K</th>\n      <td>7841.0</td>\n      <td>44.249841</td>\n      <td>10.519028</td>\n      <td>19.0</td>\n      <td>36.0</td>\n      <td>44.0</td>\n      <td>51.0</td>\n      <td>90.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"180c3fe6f8b250b877c1d86866067b80acabdb64"},"cell_type":"markdown","source":"**6. Is it true that people who earn more than 50K have at least high school education? (*education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* or *Doctorate* feature)**"},{"metadata":{"trusted":true,"_uuid":"58f38ebfa2b0e1418a6a1a680d0743697f9876a2"},"cell_type":"code","source":"# You code here\npd.crosstab(data['salary'], data['education'], margins = True)","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"education  10th  11th  12th  ...    Prof-school  Some-college    All\nsalary                       ...                                    \n<=50K       871  1115   400  ...            153          5904  24720\n>50K         62    60    33  ...            423          1387   7841\nAll         933  1175   433  ...            576          7291  32561\n\n[3 rows x 17 columns]","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>education</th>\n      <th>10th</th>\n      <th>11th</th>\n      <th>12th</th>\n      <th>1st-4th</th>\n      <th>5th-6th</th>\n      <th>7th-8th</th>\n      <th>9th</th>\n      <th>Assoc-acdm</th>\n      <th>Assoc-voc</th>\n      <th>Bachelors</th>\n      <th>Doctorate</th>\n      <th>HS-grad</th>\n      <th>Masters</th>\n      <th>Preschool</th>\n      <th>Prof-school</th>\n      <th>Some-college</th>\n      <th>All</th>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>&lt;=50K</th>\n      <td>871</td>\n      <td>1115</td>\n      <td>400</td>\n      <td>162</td>\n      <td>317</td>\n      <td>606</td>\n      <td>487</td>\n      <td>802</td>\n      <td>1021</td>\n      <td>3134</td>\n      <td>107</td>\n      <td>8826</td>\n      <td>764</td>\n      <td>51</td>\n      <td>153</td>\n      <td>5904</td>\n      <td>24720</td>\n    </tr>\n    <tr>\n      <th>&gt;50K</th>\n      <td>62</td>\n      <td>60</td>\n      <td>33</td>\n      <td>6</td>\n      <td>16</td>\n      <td>40</td>\n      <td>27</td>\n      <td>265</td>\n      <td>361</td>\n      <td>2221</td>\n      <td>306</td>\n      <td>1675</td>\n      <td>959</td>\n      <td>0</td>\n      <td>423</td>\n      <td>1387</td>\n      <td>7841</td>\n    </tr>\n    <tr>\n      <th>All</th>\n      <td>933</td>\n      <td>1175</td>\n      <td>433</td>\n      <td>168</td>\n      <td>333</td>\n      <td>646</td>\n      <td>514</td>\n      <td>1067</td>\n      <td>1382</td>\n      <td>5355</td>\n      <td>413</td>\n      <td>10501</td>\n      <td>1723</td>\n      <td>51</td>\n      <td>576</td>\n      <td>7291</td>\n      <td>32561</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"87d6bc9ec1c230e66416ce94533ea250368009cc"},"cell_type":"markdown","source":"**7. Display age statistics for each race (*race* feature) and each gender (*sex* feature). Use *groupby()* and *describe()*. Find the maximum age of men of *Amer-Indian-Eskimo* race.**"},{"metadata":{"trusted":true,"_uuid":"a4567b72200b583f7f2ee1583405d636c12169db"},"cell_type":"code","source":"# You code here\ncolumns = ['race', 'sex']\ndata.groupby(columns)['age'].describe()","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"                             count       mean  ...     75%   max\nrace               sex                         ...              \nAmer-Indian-Eskimo Female    119.0  37.117647  ...   46.00  80.0\n                   Male      192.0  37.208333  ...   45.00  82.0\nAsian-Pac-Islander Female    346.0  35.089595  ...   43.75  75.0\n                   Male      693.0  39.073593  ...   46.00  90.0\nBlack              Female   1555.0  37.854019  ...   46.00  90.0\n                   Male     1569.0  37.682600  ...   46.00  90.0\nOther              Female    109.0  31.678899  ...   39.00  74.0\n                   Male      162.0  34.654321  ...   42.00  77.0\nWhite              Female   8642.0  36.811618  ...   46.00  90.0\n                   Male    19174.0  39.652498  ...   49.00  90.0\n\n[10 rows x 8 columns]","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></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>race</th>\n      <th>sex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Amer-Indian-Eskimo</th>\n      <th>Female</th>\n      <td>119.0</td>\n      <td>37.117647</td>\n      <td>13.114991</td>\n      <td>17.0</td>\n      <td>27.0</td>\n      <td>36.0</td>\n      <td>46.00</td>\n      <td>80.0</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>192.0</td>\n      <td>37.208333</td>\n      <td>12.049563</td>\n      <td>17.0</td>\n      <td>28.0</td>\n      <td>35.0</td>\n      <td>45.00</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Asian-Pac-Islander</th>\n      <th>Female</th>\n      <td>346.0</td>\n      <td>35.089595</td>\n      <td>12.300845</td>\n      <td>17.0</td>\n      <td>25.0</td>\n      <td>33.0</td>\n      <td>43.75</td>\n      <td>75.0</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>693.0</td>\n      <td>39.073593</td>\n      <td>12.883944</td>\n      <td>18.0</td>\n      <td>29.0</td>\n      <td>37.0</td>\n      <td>46.00</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Black</th>\n      <th>Female</th>\n      <td>1555.0</td>\n      <td>37.854019</td>\n      <td>12.637197</td>\n      <td>17.0</td>\n      <td>28.0</td>\n      <td>37.0</td>\n      <td>46.00</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>1569.0</td>\n      <td>37.682600</td>\n      <td>12.882612</td>\n      <td>17.0</td>\n      <td>27.0</td>\n      <td>36.0</td>\n      <td>46.00</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Other</th>\n      <th>Female</th>\n      <td>109.0</td>\n      <td>31.678899</td>\n      <td>11.631599</td>\n      <td>17.0</td>\n      <td>23.0</td>\n      <td>29.0</td>\n      <td>39.00</td>\n      <td>74.0</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>162.0</td>\n      <td>34.654321</td>\n      <td>11.355531</td>\n      <td>17.0</td>\n      <td>26.0</td>\n      <td>32.0</td>\n      <td>42.00</td>\n      <td>77.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">White</th>\n      <th>Female</th>\n      <td>8642.0</td>\n      <td>36.811618</td>\n      <td>14.329093</td>\n      <td>17.0</td>\n      <td>25.0</td>\n      <td>35.0</td>\n      <td>46.00</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>Male</th>\n      <td>19174.0</td>\n      <td>39.652498</td>\n      <td>13.436029</td>\n      <td>17.0</td>\n      <td>29.0</td>\n      <td>38.0</td>\n      <td>49.00</td>\n      <td>90.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"19411d604b0d4337a4a048a2f0e898541f39ab76"},"cell_type":"markdown","source":"**8. Among whom is the proportion of those who earn a lot (>50K) greater: married or single men (*marital-status* feature)? Consider as married those who have a *marital-status* starting with *Married* (Married-civ-spouse, Married-spouse-absent or Married-AF-spouse), the rest are considered bachelors.**"},{"metadata":{"trusted":true,"_uuid":"5bb3a8524fcbd794707075ccf6aaf8dd0f30a3dd"},"cell_type":"code","source":"# You code here\npd.crosstab(data['salary'],data['marital-status'], normalize = True)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"marital-status  Divorced  Married-AF-spouse    ...     Separated   Widowed\nsalary                                         ...                        \n<=50K           0.122232           0.000399    ...      0.029452  0.027886\n>50K            0.014219           0.000307    ...      0.002027  0.002610\n\n[2 rows x 7 columns]","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>marital-status</th>\n      <th>Divorced</th>\n      <th>Married-AF-spouse</th>\n      <th>Married-civ-spouse</th>\n      <th>Married-spouse-absent</th>\n      <th>Never-married</th>\n      <th>Separated</th>\n      <th>Widowed</th>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>&lt;=50K</th>\n      <td>0.122232</td>\n      <td>0.000399</td>\n      <td>0.254415</td>\n      <td>0.011793</td>\n      <td>0.313012</td>\n      <td>0.029452</td>\n      <td>0.027886</td>\n    </tr>\n    <tr>\n      <th>&gt;50K</th>\n      <td>0.014219</td>\n      <td>0.000307</td>\n      <td>0.205522</td>\n      <td>0.001044</td>\n      <td>0.015079</td>\n      <td>0.002027</td>\n      <td>0.002610</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"34f2076e7d331b95a23b990372809b709d2c8183"},"cell_type":"markdown","source":"**9. What is the maximum number of hours a person works per week (*hours-per-week* feature)? How many people work such a number of hours, and what is the percentage of those who earn a lot (>50K) among them?**"},{"metadata":{"trusted":true,"_uuid":"3571468172fc4bcf34e10b3d6ab358d4e41f7ef4"},"cell_type":"code","source":"# You code here\n#data['hours-per-week'].max()\n\npd.crosstab(data['salary'],data['hours-per-week'])","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"hours-per-week  1   2   3   4   5   6   7  ...  92  94  95  96  97  98  99\nsalary                                     ...                            \n<=50K           18  24  38  51  53  56  22 ...   1   1   1   4   1   8  60\n>50K             2   8   1   3   7   8   4 ...   0   0   1   1   1   3  25\n\n[2 rows x 94 columns]","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>hours-per-week</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>40</th>\n      <th>...</th>\n      <th>55</th>\n      <th>56</th>\n      <th>57</th>\n      <th>58</th>\n      <th>59</th>\n      <th>60</th>\n      <th>61</th>\n      <th>62</th>\n      <th>63</th>\n      <th>64</th>\n      <th>65</th>\n      <th>66</th>\n      <th>67</th>\n      <th>68</th>\n      <th>70</th>\n      <th>72</th>\n      <th>73</th>\n      <th>74</th>\n      <th>75</th>\n      <th>76</th>\n      <th>77</th>\n      <th>78</th>\n      <th>80</th>\n      <th>81</th>\n      <th>82</th>\n      <th>84</th>\n      <th>85</th>\n      <th>86</th>\n      <th>87</th>\n      <th>88</th>\n      <th>89</th>\n      <th>90</th>\n      <th>91</th>\n      <th>92</th>\n      <th>94</th>\n      <th>95</th>\n      <th>96</th>\n      <th>97</th>\n      <th>98</th>\n      <th>99</th>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>&lt;=50K</th>\n      <td>18</td>\n      <td>24</td>\n      <td>38</td>\n      <td>51</td>\n      <td>53</td>\n      <td>56</td>\n      <td>22</td>\n      <td>134</td>\n      <td>17</td>\n      <td>258</td>\n      <td>11</td>\n      <td>161</td>\n      <td>21</td>\n      <td>32</td>\n      <td>389</td>\n      <td>192</td>\n      <td>28</td>\n      <td>68</td>\n      <td>14</td>\n      <td>1146</td>\n      <td>22</td>\n      <td>40</td>\n      <td>21</td>\n      <td>229</td>\n      <td>639</td>\n      <td>26</td>\n      <td>29</td>\n      <td>81</td>\n      <td>6</td>\n      <td>1066</td>\n      <td>5</td>\n      <td>238</td>\n      <td>35</td>\n      <td>26</td>\n      <td>1102</td>\n      <td>177</td>\n      <td>129</td>\n      <td>390</td>\n      <td>31</td>\n      <td>11970</td>\n      <td>...</td>\n      <td>371</td>\n      <td>57</td>\n      <td>10</td>\n      <td>19</td>\n      <td>3</td>\n      <td>827</td>\n      <td>0</td>\n      <td>14</td>\n      <td>7</td>\n      <td>9</td>\n      <td>140</td>\n      <td>15</td>\n      <td>1</td>\n      <td>7</td>\n      <td>185</td>\n      <td>46</td>\n      <td>1</td>\n      <td>1</td>\n      <td>46</td>\n      <td>1</td>\n      <td>6</td>\n      <td>6</td>\n      <td>76</td>\n      <td>3</td>\n      <td>1</td>\n      <td>28</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>19</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>8</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>&gt;50K</th>\n      <td>2</td>\n      <td>8</td>\n      <td>1</td>\n      <td>3</td>\n      <td>7</td>\n      <td>8</td>\n      <td>4</td>\n      <td>11</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>2</td>\n      <td>15</td>\n      <td>13</td>\n      <td>1</td>\n      <td>7</td>\n      <td>0</td>\n      <td>78</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0</td>\n      <td>23</td>\n      <td>35</td>\n      <td>4</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>83</td>\n      <td>0</td>\n      <td>28</td>\n      <td>4</td>\n      <td>2</td>\n      <td>195</td>\n      <td>43</td>\n      <td>20</td>\n      <td>86</td>\n      <td>7</td>\n      <td>3247</td>\n      <td>...</td>\n      <td>323</td>\n      <td>40</td>\n      <td>7</td>\n      <td>9</td>\n      <td>2</td>\n      <td>648</td>\n      <td>2</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>104</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>106</td>\n      <td>25</td>\n      <td>1</td>\n      <td>0</td>\n      <td>20</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>17</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_uuid":"d539d4d137fac1c5ea25bc516188eb657d67e9b4"},"cell_type":"markdown","source":"**10. Count the average time of work (*hours-per-week*) for those who earn a little and a lot (*salary*) for each country (*native-country*). What will these be for Japan?**"},{"metadata":{"trusted":true,"_uuid":"75a93411f1e3b519afcc27c026aaa0c67eb526e9"},"cell_type":"code","source":"# You code here\ndata[(data['salary'] == '<=50K') & (data['native-country'] == 'Japan')]['hours-per-week'].mean()\n#data[(data['salary'] == '>50K') & (data['native-country'] == 'Japan')]['hours-per-week'].mean()","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"41.0"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"anaconda-cloud":{},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}