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asoc1al d02f6c55a1 Reapply "lab1" 
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Основные возможности работы с библиотекой pandas"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка и сохранение данных"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"./datasets/2022/heart_2022_no_nans.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
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" <td>No</td>\n",
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" <td>Yes, received Tdap</td>\n",
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" <td>No</td>\n",
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" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>77.11</td>\n",
" <td>28.29</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
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" <td>Virgin Islands</td>\n",
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" <td>Good</td>\n",
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" <td>0.0</td>\n",
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" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
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" <td>No</td>\n",
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" <td>0.0</td>\n",
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" <td>32.28</td>\n",
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" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
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" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>5.07</td>\n",
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" <td>Virgin Islands</td>\n",
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" <td>Yes</td>\n",
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" <td>1 to 5</td>\n",
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" <td>...</td>\n",
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" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
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" <td>Yes</td>\n",
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" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
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" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>5.30</td>\n",
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" <th>246021</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
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" <td>32.55</td>\n",
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" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
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" <td>Yes</td>\n",
" <td>3.17</td>\n",
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" State Sex GeneralHealth PhysicalHealthDays \\\n",
"246012 Virgin Islands Male Fair 7.0 \n",
"246013 Virgin Islands Male Excellent 0.0 \n",
"246014 Virgin Islands Female Good 0.0 \n",
"246015 Virgin Islands Female Very good 0.0 \n",
"246016 Virgin Islands Male Good 0.0 \n",
"246017 Virgin Islands Male Very good 0.0 \n",
"246018 Virgin Islands Female Fair 0.0 \n",
"246019 Virgin Islands Male Good 0.0 \n",
"246020 Virgin Islands Female Excellent 2.0 \n",
"246021 Virgin Islands Male Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"246012 30.0 Within past year (anytime less than 12 months ... \n",
"246013 7.0 Within past year (anytime less than 12 months ... \n",
"246014 0.0 Within past year (anytime less than 12 months ... \n",
"246015 0.0 Within past year (anytime less than 12 months ... \n",
"246016 0.0 Within past year (anytime less than 12 months ... \n",
"246017 0.0 Within past 2 years (1 year but less than 2 ye... \n",
"246018 7.0 Within past year (anytime less than 12 months ... \n",
"246019 15.0 Within past year (anytime less than 12 months ... \n",
"246020 2.0 Within past year (anytime less than 12 months ... \n",
"246021 0.0 Within past year (anytime less than 12 months ... \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack ... \\\n",
"246012 No 4.0 None of them Yes ... \n",
"246013 No 4.0 None of them No ... \n",
"246014 Yes 12.0 1 to 5 No ... \n",
"246015 Yes 7.0 1 to 5 No ... \n",
"246016 No 6.0 1 to 5 Yes ... \n",
"246017 Yes 6.0 None of them No ... \n",
"246018 Yes 7.0 None of them No ... \n",
"246019 Yes 7.0 1 to 5 No ... \n",
"246020 Yes 7.0 None of them No ... \n",
"246021 No 5.0 None of them Yes ... \n",
"\n",
" WeightInKilograms BMI AlcoholDrinkers HIVTesting FluVaxLast12 \\\n",
"246012 117.93 33.38 Yes Yes No \n",
"246013 49.90 18.30 Yes No No \n",
"246014 52.16 19.14 No No No \n",
"246015 77.11 28.29 Yes Yes No \n",
"246016 118.84 36.54 Yes Yes Yes \n",
"246017 102.06 32.28 Yes No No \n",
"246018 90.72 24.34 No No No \n",
"246019 83.91 29.86 Yes Yes Yes \n",
"246020 83.01 28.66 No Yes Yes \n",
"246021 108.86 32.55 No Yes Yes \n",
"\n",
" PneumoVaxEver TetanusLast10Tdap \\\n",
"246012 No No, did not receive any tetanus shot in the pa... \n",
"246013 No No, did not receive any tetanus shot in the pa... \n",
"246014 Yes Yes, received Tdap \n",
"246015 No No, did not receive any tetanus shot in the pa... \n",
"246016 No Yes, received tetanus shot but not sure what type \n",
"246017 No Yes, received tetanus shot but not sure what type \n",
"246018 No No, did not receive any tetanus shot in the pa... \n",
"246019 Yes Yes, received tetanus shot but not sure what type \n",
"246020 No Yes, received tetanus shot but not sure what type \n",
"246021 Yes No, did not receive any tetanus shot in the pa... \n",
"\n",
" HighRiskLastYear CovidPos SleepHours-HeightInMeters \n",
"246012 No Yes 2.12 \n",
"246013 No No 2.35 \n",
"246014 No No 10.35 \n",
"246015 No No 5.35 \n",
"246016 No No 4.20 \n",
"246017 No No 4.22 \n",
"246018 No Yes 5.07 \n",
"246019 No Yes 5.32 \n",
"246020 No No 5.30 \n",
"246021 No Yes 3.17 \n",
"\n",
"[10 rows x 41 columns]"
]
},
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"source": [
"df.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
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" <td>9.0</td>\n",
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" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
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" <td>Yes</td>\n",
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" <td>33.30</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>6.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Fair</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>8.0</td>\n",
" <td>1 to 5</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>74.84</td>\n",
" <td>24.37</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>6.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>5 or more years ago</td>\n",
" <td>No</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>78.02</td>\n",
" <td>26.94</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>4.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>63.50</td>\n",
" <td>22.60</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>5.32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays MentalHealthDays \\\n",
"0 Alabama Female Very good 4.0 0.0 \n",
"1 Alabama Male Very good 0.0 0.0 \n",
"2 Alabama Male Very good 0.0 0.0 \n",
"3 Alabama Female Fair 5.0 0.0 \n",
"4 Alabama Female Good 3.0 15.0 \n",
"5 Alabama Male Good 0.0 0.0 \n",
"6 Alabama Female Good 3.0 0.0 \n",
"7 Alabama Male Fair 5.0 0.0 \n",
"8 Alabama Male Good 2.0 0.0 \n",
"9 Alabama Female Very good 0.0 0.0 \n",
"\n",
" LastCheckupTime PhysicalActivities \\\n",
"0 Within past year (anytime less than 12 months ... Yes \n",
"1 Within past year (anytime less than 12 months ... Yes \n",
"2 Within past year (anytime less than 12 months ... No \n",
"3 Within past year (anytime less than 12 months ... Yes \n",
"4 Within past year (anytime less than 12 months ... Yes \n",
"5 Within past year (anytime less than 12 months ... Yes \n",
"6 Within past year (anytime less than 12 months ... Yes \n",
"7 Within past year (anytime less than 12 months ... Yes \n",
"8 5 or more years ago No \n",
"9 Within past year (anytime less than 12 months ... Yes \n",
"\n",
" SleepHours RemovedTeeth HadHeartAttack ... WeightInKilograms \\\n",
"0 9.0 None of them No ... 71.67 \n",
"1 6.0 None of them No ... 95.25 \n",
"2 8.0 6 or more, but not all No ... 108.86 \n",
"3 9.0 None of them No ... 90.72 \n",
"4 5.0 1 to 5 No ... 79.38 \n",
"5 7.0 None of them No ... 120.20 \n",
"6 8.0 6 or more, but not all No ... 88.00 \n",
"7 8.0 1 to 5 Yes ... 74.84 \n",
"8 6.0 None of them No ... 78.02 \n",
"9 7.0 None of them No ... 63.50 \n",
"\n",
" BMI AlcoholDrinkers HIVTesting FluVaxLast12 PneumoVaxEver \\\n",
"0 27.99 No No Yes Yes \n",
"1 30.13 No No Yes Yes \n",
"2 31.66 Yes No No Yes \n",
"3 31.32 No No Yes Yes \n",
"4 33.07 No No Yes Yes \n",
"5 34.96 Yes Yes Yes No \n",
"6 33.30 No No Yes Yes \n",
"7 24.37 No Yes Yes Yes \n",
"8 26.94 No No No No \n",
"9 22.60 No No Yes Yes \n",
"\n",
" TetanusLast10Tdap HighRiskLastYear \\\n",
"0 Yes, received Tdap No \n",
"1 Yes, received tetanus shot but not sure what type No \n",
"2 No, did not receive any tetanus shot in the pa... No \n",
"3 No, did not receive any tetanus shot in the pa... No \n",
"4 No, did not receive any tetanus shot in the pa... No \n",
"5 Yes, received tetanus shot but not sure what type No \n",
"6 No, did not receive any tetanus shot in the pa... No \n",
"7 No, did not receive any tetanus shot in the pa... No \n",
"8 No, did not receive any tetanus shot in the pa... No \n",
"9 No, did not receive any tetanus shot in the pa... No \n",
"\n",
" CovidPos SleepHours-HeightInMeters \n",
"0 No 7.40 \n",
"1 No 4.22 \n",
"2 Yes 6.15 \n",
"3 Yes 7.30 \n",
"4 No 3.45 \n",
"5 No 5.15 \n",
"6 No 6.37 \n",
"7 Yes 6.25 \n",
"8 Yes 4.30 \n",
"9 No 5.32 \n",
"\n",
"[10 rows x 41 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"new.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о датафрейме с данными¶"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PhysicalHealthDays</th>\n",
" <th>MentalHealthDays</th>\n",
" <th>SleepHours</th>\n",
" <th>HeightInMeters</th>\n",
" <th>WeightInKilograms</th>\n",
" <th>BMI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>246022.000000</td>\n",
" <td>246022.000000</td>\n",
" <td>246022.000000</td>\n",
" <td>246022.000000</td>\n",
" <td>246022.000000</td>\n",
" <td>246022.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.119026</td>\n",
" <td>4.167140</td>\n",
" <td>7.021331</td>\n",
" <td>1.705150</td>\n",
" <td>83.615179</td>\n",
" <td>28.668136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.405844</td>\n",
" <td>8.102687</td>\n",
" <td>1.440681</td>\n",
" <td>0.106654</td>\n",
" <td>21.323156</td>\n",
" <td>6.513973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.910000</td>\n",
" <td>28.120000</td>\n",
" <td>12.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>1.630000</td>\n",
" <td>68.040000</td>\n",
" <td>24.270000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.000000</td>\n",
" <td>1.700000</td>\n",
" <td>81.650000</td>\n",
" <td>27.460000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>8.000000</td>\n",
" <td>1.780000</td>\n",
" <td>95.250000</td>\n",
" <td>31.890000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>30.000000</td>\n",
" <td>30.000000</td>\n",
" <td>24.000000</td>\n",
" <td>2.410000</td>\n",
" <td>292.570000</td>\n",
" <td>97.650000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PhysicalHealthDays MentalHealthDays SleepHours HeightInMeters \\\n",
"count 246022.000000 246022.000000 246022.000000 246022.000000 \n",
"mean 4.119026 4.167140 7.021331 1.705150 \n",
"std 8.405844 8.102687 1.440681 0.106654 \n",
"min 0.000000 0.000000 1.000000 0.910000 \n",
"25% 0.000000 0.000000 6.000000 1.630000 \n",
"50% 0.000000 0.000000 7.000000 1.700000 \n",
"75% 3.000000 4.000000 8.000000 1.780000 \n",
"max 30.000000 30.000000 24.000000 2.410000 \n",
"\n",
" WeightInKilograms BMI \n",
"count 246022.000000 246022.000000 \n",
"mean 83.615179 28.668136 \n",
"std 21.323156 6.513973 \n",
"min 28.120000 12.020000 \n",
"25% 68.040000 24.270000 \n",
"50% 81.650000 27.460000 \n",
"75% 95.250000 31.890000 \n",
"max 292.570000 97.650000 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 246022 entries, 0 to 246021\n",
"Data columns (total 40 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 State 246022 non-null object \n",
" 1 Sex 246022 non-null object \n",
" 2 GeneralHealth 246022 non-null object \n",
" 3 PhysicalHealthDays 246022 non-null float64\n",
" 4 MentalHealthDays 246022 non-null float64\n",
" 5 LastCheckupTime 246022 non-null object \n",
" 6 PhysicalActivities 246022 non-null object \n",
" 7 SleepHours 246022 non-null float64\n",
" 8 RemovedTeeth 246022 non-null object \n",
" 9 HadHeartAttack 246022 non-null object \n",
" 10 HadAngina 246022 non-null object \n",
" 11 HadStroke 246022 non-null object \n",
" 12 HadAsthma 246022 non-null object \n",
" 13 HadSkinCancer 246022 non-null object \n",
" 14 HadCOPD 246022 non-null object \n",
" 15 HadDepressiveDisorder 246022 non-null object \n",
" 16 HadKidneyDisease 246022 non-null object \n",
" 17 HadArthritis 246022 non-null object \n",
" 18 HadDiabetes 246022 non-null object \n",
" 19 DeafOrHardOfHearing 246022 non-null object \n",
" 20 BlindOrVisionDifficulty 246022 non-null object \n",
" 21 DifficultyConcentrating 246022 non-null object \n",
" 22 DifficultyWalking 246022 non-null object \n",
" 23 DifficultyDressingBathing 246022 non-null object \n",
" 24 DifficultyErrands 246022 non-null object \n",
" 25 SmokerStatus 246022 non-null object \n",
" 26 ECigaretteUsage 246022 non-null object \n",
" 27 ChestScan 246022 non-null object \n",
" 28 RaceEthnicityCategory 246022 non-null object \n",
" 29 AgeCategory 246022 non-null object \n",
" 30 HeightInMeters 246022 non-null float64\n",
" 31 WeightInKilograms 246022 non-null float64\n",
" 32 BMI 246022 non-null float64\n",
" 33 AlcoholDrinkers 246022 non-null object \n",
" 34 HIVTesting 246022 non-null object \n",
" 35 FluVaxLast12 246022 non-null object \n",
" 36 PneumoVaxEver 246022 non-null object \n",
" 37 TetanusLast10Tdap 246022 non-null object \n",
" 38 HighRiskLastYear 246022 non-null object \n",
" 39 CovidPos 246022 non-null object \n",
"dtypes: float64(6), object(34)\n",
"memory usage: 75.1+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о колонках датафрейма¶"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays',\n",
" 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities',\n",
" 'SleepHours', 'RemovedTeeth', 'HadHeartAttack', 'HadAngina',\n",
" 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',\n",
" 'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',\n",
" 'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',\n",
" 'DifficultyConcentrating', 'DifficultyWalking',\n",
" 'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',\n",
" 'ECigaretteUsage', 'ChestScan', 'RaceEthnicityCategory', 'AgeCategory',\n",
" 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers',\n",
" 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap',\n",
" 'HighRiskLastYear', 'CovidPos'],\n",
" dtype='object')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вывод отельных строки и столбцов из датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" </tr>\n",
" <tr>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>Male</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>246018</th>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>90.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246019</th>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>83.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246020</th>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>83.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246021</th>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>108.86</td>\n",
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"</table>\n",
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"</div>"
],
"text/plain": [
" Sex HadHeartAttack WeightInKilograms\n",
"0 Female No 71.67\n",
"1 Male No 95.25\n",
"2 Male No 108.86\n",
"3 Female No 90.72\n",
"4 Female No 79.38\n",
"... ... ... ...\n",
"246017 Male No 102.06\n",
"246018 Female No 90.72\n",
"246019 Male No 83.91\n",
"246020 Female No 83.01\n",
"246021 Male Yes 108.86\n",
"\n",
"[246022 rows x 3 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"Sex\", \"HadHeartAttack\", \"WeightInKilograms\"]]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
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" <td>0.0</td>\n",
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" <td>...</td>\n",
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" <td>31.32</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
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" <td>...</td>\n",
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" <td>79.38</td>\n",
" <td>33.07</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.85</td>\n",
" <td>120.20</td>\n",
" <td>34.96</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays MentalHealthDays \\\n",
"3 Alabama Female Fair 5.0 0.0 \n",
"4 Alabama Female Good 3.0 15.0 \n",
"5 Alabama Male Good 0.0 0.0 \n",
"\n",
" LastCheckupTime PhysicalActivities \\\n",
"3 Within past year (anytime less than 12 months ... Yes \n",
"4 Within past year (anytime less than 12 months ... Yes \n",
"5 Within past year (anytime less than 12 months ... Yes \n",
"\n",
" SleepHours RemovedTeeth HadHeartAttack ... HeightInMeters \\\n",
"3 9.0 None of them No ... 1.70 \n",
"4 5.0 1 to 5 No ... 1.55 \n",
"5 7.0 None of them No ... 1.85 \n",
"\n",
" WeightInKilograms BMI AlcoholDrinkers HIVTesting FluVaxLast12 \\\n",
"3 90.72 31.32 No No Yes \n",
"4 79.38 33.07 No No Yes \n",
"5 120.20 34.96 Yes Yes Yes \n",
"\n",
" PneumoVaxEver TetanusLast10Tdap \\\n",
"3 Yes No, did not receive any tetanus shot in the pa... \n",
"4 Yes No, did not receive any tetanus shot in the pa... \n",
"5 No Yes, received tetanus shot but not sure what type \n",
"\n",
" HighRiskLastYear CovidPos \n",
"3 No Yes \n",
"4 No No \n",
"5 No No \n",
"\n",
"[3 rows x 40 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[3:6]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>PhysicalHealthDays</th>\n",
" <th>MentalHealthDays</th>\n",
" <th>LastCheckupTime</th>\n",
" <th>PhysicalActivities</th>\n",
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" <th>RemovedTeeth</th>\n",
" <th>HadHeartAttack</th>\n",
" <th>...</th>\n",
" <th>HeightInMeters</th>\n",
" <th>WeightInKilograms</th>\n",
" <th>BMI</th>\n",
" <th>AlcoholDrinkers</th>\n",
" <th>HIVTesting</th>\n",
" <th>FluVaxLast12</th>\n",
" <th>PneumoVaxEver</th>\n",
" <th>TetanusLast10Tdap</th>\n",
" <th>HighRiskLastYear</th>\n",
" <th>CovidPos</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
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" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.85</td>\n",
" <td>108.86</td>\n",
" <td>31.66</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
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" <td>0.0</td>\n",
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" <td>Yes</td>\n",
" <td>7.0</td>\n",
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" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.85</td>\n",
" <td>120.20</td>\n",
" <td>34.96</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>8.0</td>\n",
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" <td>122.47</td>\n",
" <td>36.62</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received Tdap</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Good</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.52</td>\n",
" <td>108.86</td>\n",
" <td>46.87</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot, but not Tdap</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>5.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>1.88</td>\n",
" <td>115.67</td>\n",
" <td>32.74</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>246002</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
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" <td>1.88</td>\n",
" <td>106.59</td>\n",
" <td>30.17</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>Tested positive using home test without a heal...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246012</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Fair</td>\n",
" <td>7.0</td>\n",
" <td>30.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>4.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
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" <td>1.88</td>\n",
" <td>117.93</td>\n",
" <td>33.38</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246016</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>6.0</td>\n",
" <td>1 to 5</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>1.80</td>\n",
" <td>118.84</td>\n",
" <td>36.54</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246017</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past 2 years (1 year but less than 2 ye...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.78</td>\n",
" <td>102.06</td>\n",
" <td>32.28</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246021</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>1.83</td>\n",
" <td>108.86</td>\n",
" <td>32.55</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>44646 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays \\\n",
"2 Alabama Male Very good 0.0 \n",
"5 Alabama Male Good 0.0 \n",
"10 Alabama Male Very good 0.0 \n",
"11 Alabama Female Good 3.0 \n",
"12 Alabama Male Good 5.0 \n",
"... ... ... ... ... \n",
"246002 Virgin Islands Male Good 0.0 \n",
"246012 Virgin Islands Male Fair 7.0 \n",
"246016 Virgin Islands Male Good 0.0 \n",
"246017 Virgin Islands Male Very good 0.0 \n",
"246021 Virgin Islands Male Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"2 0.0 Within past year (anytime less than 12 months ... \n",
"5 0.0 Within past year (anytime less than 12 months ... \n",
"10 0.0 Within past year (anytime less than 12 months ... \n",
"11 4.0 Within past year (anytime less than 12 months ... \n",
"12 0.0 Within past year (anytime less than 12 months ... \n",
"... ... ... \n",
"246002 0.0 Within past year (anytime less than 12 months ... \n",
"246012 30.0 Within past year (anytime less than 12 months ... \n",
"246016 0.0 Within past year (anytime less than 12 months ... \n",
"246017 0.0 Within past 2 years (1 year but less than 2 ye... \n",
"246021 0.0 Within past year (anytime less than 12 months ... \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack \\\n",
"2 No 8.0 6 or more, but not all No \n",
"5 Yes 7.0 None of them No \n",
"10 Yes 8.0 1 to 5 No \n",
"11 Yes 5.0 None of them No \n",
"12 Yes 5.0 6 or more, but not all Yes \n",
"... ... ... ... ... \n",
"246002 Yes 6.0 1 to 5 No \n",
"246012 No 4.0 None of them Yes \n",
"246016 No 6.0 1 to 5 Yes \n",
"246017 Yes 6.0 None of them No \n",
"246021 No 5.0 None of them Yes \n",
"\n",
" ... HeightInMeters WeightInKilograms BMI AlcoholDrinkers \\\n",
"2 ... 1.85 108.86 31.66 Yes \n",
"5 ... 1.85 120.20 34.96 Yes \n",
"10 ... 1.83 122.47 36.62 Yes \n",
"11 ... 1.52 108.86 46.87 No \n",
"12 ... 1.88 115.67 32.74 No \n",
"... ... ... ... ... ... \n",
"246002 ... 1.88 106.59 30.17 Yes \n",
"246012 ... 1.88 117.93 33.38 Yes \n",
"246016 ... 1.80 118.84 36.54 Yes \n",
"246017 ... 1.78 102.06 32.28 Yes \n",
"246021 ... 1.83 108.86 32.55 No \n",
"\n",
" HIVTesting FluVaxLast12 PneumoVaxEver \\\n",
"2 No No Yes \n",
"5 Yes Yes No \n",
"10 No Yes Yes \n",
"11 No No No \n",
"12 No Yes Yes \n",
"... ... ... ... \n",
"246002 No No No \n",
"246012 Yes No No \n",
"246016 Yes Yes No \n",
"246017 No No No \n",
"246021 Yes Yes Yes \n",
"\n",
" TetanusLast10Tdap HighRiskLastYear \\\n",
"2 No, did not receive any tetanus shot in the pa... No \n",
"5 Yes, received tetanus shot but not sure what type No \n",
"10 Yes, received Tdap No \n",
"11 Yes, received tetanus shot, but not Tdap No \n",
"12 Yes, received tetanus shot but not sure what type No \n",
"... ... ... \n",
"246002 Yes, received tetanus shot but not sure what type No \n",
"246012 No, did not receive any tetanus shot in the pa... No \n",
"246016 Yes, received tetanus shot but not sure what type No \n",
"246017 Yes, received tetanus shot but not sure what type No \n",
"246021 No, did not receive any tetanus shot in the pa... No \n",
"\n",
" CovidPos \n",
"2 Yes \n",
"5 No \n",
"10 No \n",
"11 Yes \n",
"12 No \n",
"... ... \n",
"246002 Tested positive using home test without a heal... \n",
"246012 Yes \n",
"246016 No \n",
"246017 No \n",
"246021 Yes \n",
"\n",
"[44646 rows x 40 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['WeightInKilograms'] > 100]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Группировка и агрегация данных в датафрейме¶"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WeightInKilograms</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alabama</th>\n",
" <td>85.225899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alaska</th>\n",
" <td>83.937201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arizona</th>\n",
" <td>82.626862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas</th>\n",
" <td>85.361796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>California</th>\n",
" <td>81.334135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Colorado</th>\n",
" <td>80.805505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Connecticut</th>\n",
" <td>82.192881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Delaware</th>\n",
" <td>84.224436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>District of Columbia</th>\n",
" <td>78.593038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Florida</th>\n",
" <td>83.155785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>84.332240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guam</th>\n",
" <td>77.294261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hawaii</th>\n",
" <td>76.419335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Idaho</th>\n",
" <td>84.648567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Illinois</th>\n",
" <td>83.459467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indiana</th>\n",
" <td>85.703237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iowa</th>\n",
" <td>86.970651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kansas</th>\n",
" <td>85.864583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kentucky</th>\n",
" <td>86.781960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Louisiana</th>\n",
" <td>85.162787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maine</th>\n",
" <td>82.949232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maryland</th>\n",
" <td>83.543344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Massachusetts</th>\n",
" <td>80.591010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Michigan</th>\n",
" <td>83.629868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minnesota</th>\n",
" <td>84.954303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mississippi</th>\n",
" <td>88.322797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Missouri</th>\n",
" <td>85.836119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Montana</th>\n",
" <td>84.231140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nebraska</th>\n",
" <td>85.961696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nevada</th>\n",
" <td>82.784771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Hampshire</th>\n",
" <td>80.702764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Jersey</th>\n",
" <td>81.270844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Mexico</th>\n",
" <td>80.529087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New York</th>\n",
" <td>80.960180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North Carolina</th>\n",
" <td>83.730953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North Dakota</th>\n",
" <td>85.924972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ohio</th>\n",
" <td>86.938279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oklahoma</th>\n",
" <td>85.517429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oregon</th>\n",
" <td>83.802043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pennsylvania</th>\n",
" <td>83.831872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Puerto Rico</th>\n",
" <td>79.152187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rhode Island</th>\n",
" <td>80.675832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Carolina</th>\n",
" <td>84.046443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Dakota</th>\n",
" <td>86.868195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tennessee</th>\n",
" <td>86.237325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Texas</th>\n",
" <td>84.894035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utah</th>\n",
" <td>83.888474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vermont</th>\n",
" <td>80.557657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Virgin Islands</th>\n",
" <td>82.131440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Virginia</th>\n",
" <td>83.822634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Washington</th>\n",
" <td>83.077369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>86.697505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wisconsin</th>\n",
" <td>86.167571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wyoming</th>\n",
" <td>83.844357</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WeightInKilograms\n",
"State \n",
"Alabama 85.225899\n",
"Alaska 83.937201\n",
"Arizona 82.626862\n",
"Arkansas 85.361796\n",
"California 81.334135\n",
"Colorado 80.805505\n",
"Connecticut 82.192881\n",
"Delaware 84.224436\n",
"District of Columbia 78.593038\n",
"Florida 83.155785\n",
"Georgia 84.332240\n",
"Guam 77.294261\n",
"Hawaii 76.419335\n",
"Idaho 84.648567\n",
"Illinois 83.459467\n",
"Indiana 85.703237\n",
"Iowa 86.970651\n",
"Kansas 85.864583\n",
"Kentucky 86.781960\n",
"Louisiana 85.162787\n",
"Maine 82.949232\n",
"Maryland 83.543344\n",
"Massachusetts 80.591010\n",
"Michigan 83.629868\n",
"Minnesota 84.954303\n",
"Mississippi 88.322797\n",
"Missouri 85.836119\n",
"Montana 84.231140\n",
"Nebraska 85.961696\n",
"Nevada 82.784771\n",
"New Hampshire 80.702764\n",
"New Jersey 81.270844\n",
"New Mexico 80.529087\n",
"New York 80.960180\n",
"North Carolina 83.730953\n",
"North Dakota 85.924972\n",
"Ohio 86.938279\n",
"Oklahoma 85.517429\n",
"Oregon 83.802043\n",
"Pennsylvania 83.831872\n",
"Puerto Rico 79.152187\n",
"Rhode Island 80.675832\n",
"South Carolina 84.046443\n",
"South Dakota 86.868195\n",
"Tennessee 86.237325\n",
"Texas 84.894035\n",
"Utah 83.888474\n",
"Vermont 80.557657\n",
"Virgin Islands 82.131440\n",
"Virginia 83.822634\n",
"Washington 83.077369\n",
"West Virginia 86.697505\n",
"Wisconsin 86.167571\n",
"Wyoming 83.844357"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group = df.groupby(['State'])['WeightInKilograms'].mean()\n",
"group.to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сортировка данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\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>State</th>\n",
" <th>Sex</th>\n",
" <th>GeneralHealth</th>\n",
" <th>PhysicalHealthDays</th>\n",
" <th>MentalHealthDays</th>\n",
" <th>LastCheckupTime</th>\n",
" <th>PhysicalActivities</th>\n",
" <th>SleepHours</th>\n",
" <th>RemovedTeeth</th>\n",
" <th>HadHeartAttack</th>\n",
" <th>...</th>\n",
" <th>HeightInMeters</th>\n",
" <th>WeightInKilograms</th>\n",
" <th>BMI</th>\n",
" <th>AlcoholDrinkers</th>\n",
" <th>HIVTesting</th>\n",
" <th>FluVaxLast12</th>\n",
" <th>PneumoVaxEver</th>\n",
" <th>TetanusLast10Tdap</th>\n",
" <th>HighRiskLastYear</th>\n",
" <th>CovidPos</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9060</th>\n",
" <td>Arizona</td>\n",
" <td>Male</td>\n",
" <td>Fair</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.85</td>\n",
" <td>292.57</td>\n",
" <td>85.10</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received Tdap</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48969</th>\n",
" <td>Hawaii</td>\n",
" <td>Male</td>\n",
" <td>Poor</td>\n",
" <td>30.0</td>\n",
" <td>30.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>4.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.93</td>\n",
" <td>276.24</td>\n",
" <td>74.13</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75697</th>\n",
" <td>Kentucky</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5 or more years ago</td>\n",
" <td>No</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.91</td>\n",
" <td>273.52</td>\n",
" <td>75.37</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143147</th>\n",
" <td>New York</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>8.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.88</td>\n",
" <td>273.06</td>\n",
" <td>77.29</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76244</th>\n",
" <td>Kentucky</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.83</td>\n",
" <td>272.16</td>\n",
" <td>81.37</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>Yes</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",
" <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>203695</th>\n",
" <td>Vermont</td>\n",
" <td>Female</td>\n",
" <td>Poor</td>\n",
" <td>30.0</td>\n",
" <td>3.0</td>\n",
" <td>Within past 2 years (1 year but less than 2 ye...</td>\n",
" <td>No</td>\n",
" <td>18.0</td>\n",
" <td>All</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.60</td>\n",
" <td>30.84</td>\n",
" <td>12.05</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242632</th>\n",
" <td>Puerto Rico</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>30.0</td>\n",
" <td>7.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>7.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.35</td>\n",
" <td>30.39</td>\n",
" <td>16.77</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11614</th>\n",
" <td>Arkansas</td>\n",
" <td>Female</td>\n",
" <td>Poor</td>\n",
" <td>30.0</td>\n",
" <td>30.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>All</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.52</td>\n",
" <td>29.48</td>\n",
" <td>12.69</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127404</th>\n",
" <td>Nebraska</td>\n",
" <td>Female</td>\n",
" <td>Poor</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.52</td>\n",
" <td>29.48</td>\n",
" <td>12.69</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179326</th>\n",
" <td>South Carolina</td>\n",
" <td>Female</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5 or more years ago</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.52</td>\n",
" <td>28.12</td>\n",
" <td>12.11</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>246022 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays \\\n",
"9060 Arizona Male Fair 15.0 \n",
"48969 Hawaii Male Poor 30.0 \n",
"75697 Kentucky Male Very good 0.0 \n",
"143147 New York Male Very good 3.0 \n",
"76244 Kentucky Male Very good 0.0 \n",
"... ... ... ... ... \n",
"203695 Vermont Female Poor 30.0 \n",
"242632 Puerto Rico Female Fair 30.0 \n",
"11614 Arkansas Female Poor 30.0 \n",
"127404 Nebraska Female Poor 30.0 \n",
"179326 South Carolina Female Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"9060 15.0 Within past year (anytime less than 12 months ... \n",
"48969 30.0 Within past year (anytime less than 12 months ... \n",
"75697 0.0 5 or more years ago \n",
"143147 1.0 Within past year (anytime less than 12 months ... \n",
"76244 0.0 Within past year (anytime less than 12 months ... \n",
"... ... ... \n",
"203695 3.0 Within past 2 years (1 year but less than 2 ye... \n",
"242632 7.0 Within past year (anytime less than 12 months ... \n",
"11614 30.0 Within past year (anytime less than 12 months ... \n",
"127404 0.0 Within past year (anytime less than 12 months ... \n",
"179326 0.0 5 or more years ago \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack \\\n",
"9060 No 8.0 None of them No \n",
"48969 Yes 4.0 None of them No \n",
"75697 No 7.0 None of them No \n",
"143147 Yes 8.0 None of them No \n",
"76244 Yes 7.0 None of them No \n",
"... ... ... ... ... \n",
"203695 No 18.0 All No \n",
"242632 No 7.0 6 or more, but not all No \n",
"11614 No 8.0 All No \n",
"127404 Yes 6.0 None of them No \n",
"179326 No 8.0 None of them No \n",
"\n",
" ... HeightInMeters WeightInKilograms BMI AlcoholDrinkers \\\n",
"9060 ... 1.85 292.57 85.10 No \n",
"48969 ... 1.93 276.24 74.13 No \n",
"75697 ... 1.91 273.52 75.37 No \n",
"143147 ... 1.88 273.06 77.29 Yes \n",
"76244 ... 1.83 272.16 81.37 No \n",
"... ... ... ... ... ... \n",
"203695 ... 1.60 30.84 12.05 No \n",
"242632 ... 1.35 30.39 16.77 No \n",
"11614 ... 1.52 29.48 12.69 No \n",
"127404 ... 1.52 29.48 12.69 No \n",
"179326 ... 1.52 28.12 12.11 No \n",
"\n",
" HIVTesting FluVaxLast12 PneumoVaxEver \\\n",
"9060 No No No \n",
"48969 No No No \n",
"75697 No No No \n",
"143147 No No No \n",
"76244 Yes No No \n",
"... ... ... ... \n",
"203695 No No No \n",
"242632 No Yes Yes \n",
"11614 No Yes Yes \n",
"127404 No No Yes \n",
"179326 No No No \n",
"\n",
" TetanusLast10Tdap HighRiskLastYear \\\n",
"9060 Yes, received Tdap No \n",
"48969 No, did not receive any tetanus shot in the pa... No \n",
"75697 Yes, received tetanus shot but not sure what type No \n",
"143147 Yes, received tetanus shot but not sure what type No \n",
"76244 Yes, received tetanus shot but not sure what type No \n",
"... ... ... \n",
"203695 No, did not receive any tetanus shot in the pa... No \n",
"242632 No, did not receive any tetanus shot in the pa... No \n",
"11614 No, did not receive any tetanus shot in the pa... No \n",
"127404 Yes, received tetanus shot but not sure what type No \n",
"179326 No, did not receive any tetanus shot in the pa... No \n",
"\n",
" CovidPos \n",
"9060 No \n",
"48969 Yes \n",
"75697 No \n",
"143147 No \n",
"76244 Yes \n",
"... ... \n",
"203695 No \n",
"242632 No \n",
"11614 Yes \n",
"127404 No \n",
"179326 No \n",
"\n",
"[246022 rows x 40 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_df = df.sort_values(by='WeightInKilograms', ascending = False)\n",
"sorted_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление строк/столбцов"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"df_dropped_columns = df.drop(columns=['AlcoholDrinkers', 'BMI']) # Удаление столбцов 'AlcoholDrinkers' и 'BMI'"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
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" <th>GeneralHealth</th>\n",
" <th>PhysicalHealthDays</th>\n",
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" <th>LastCheckupTime</th>\n",
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" <th>HIVTesting</th>\n",
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" <th>CovidPos</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
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" <td>Very good</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>White only, Non-Hispanic</td>\n",
" <td>Age 65 to 69</td>\n",
" <td>1.60</td>\n",
" <td>71.67</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received Tdap</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>White only, Non-Hispanic</td>\n",
" <td>Age 70 to 74</td>\n",
" <td>1.78</td>\n",
" <td>95.25</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>White only, Non-Hispanic</td>\n",
" <td>Age 75 to 79</td>\n",
" <td>1.85</td>\n",
" <td>108.86</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>White only, Non-Hispanic</td>\n",
" <td>Age 80 or older</td>\n",
" <td>1.70</td>\n",
" <td>90.72</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Good</td>\n",
" <td>3.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>5.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>White only, Non-Hispanic</td>\n",
" <td>Age 80 or older</td>\n",
" <td>1.55</td>\n",
" <td>79.38</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</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",
" <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>246017</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past 2 years (1 year but less than 2 ye...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>White only, Non-Hispanic</td>\n",
" <td>Age 60 to 64</td>\n",
" <td>1.78</td>\n",
" <td>102.06</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246018</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>Black only, Non-Hispanic</td>\n",
" <td>Age 25 to 29</td>\n",
" <td>1.93</td>\n",
" <td>90.72</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246019</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>Multiracial, Non-Hispanic</td>\n",
" <td>Age 65 to 69</td>\n",
" <td>1.68</td>\n",
" <td>83.91</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246020</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Excellent</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>Black only, Non-Hispanic</td>\n",
" <td>Age 50 to 54</td>\n",
" <td>1.70</td>\n",
" <td>83.01</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246021</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>Black only, Non-Hispanic</td>\n",
" <td>Age 70 to 74</td>\n",
" <td>1.83</td>\n",
" <td>108.86</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>246022 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays \\\n",
"0 Alabama Female Very good 4.0 \n",
"1 Alabama Male Very good 0.0 \n",
"2 Alabama Male Very good 0.0 \n",
"3 Alabama Female Fair 5.0 \n",
"4 Alabama Female Good 3.0 \n",
"... ... ... ... ... \n",
"246017 Virgin Islands Male Very good 0.0 \n",
"246018 Virgin Islands Female Fair 0.0 \n",
"246019 Virgin Islands Male Good 0.0 \n",
"246020 Virgin Islands Female Excellent 2.0 \n",
"246021 Virgin Islands Male Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"0 0.0 Within past year (anytime less than 12 months ... \n",
"1 0.0 Within past year (anytime less than 12 months ... \n",
"2 0.0 Within past year (anytime less than 12 months ... \n",
"3 0.0 Within past year (anytime less than 12 months ... \n",
"4 15.0 Within past year (anytime less than 12 months ... \n",
"... ... ... \n",
"246017 0.0 Within past 2 years (1 year but less than 2 ye... \n",
"246018 7.0 Within past year (anytime less than 12 months ... \n",
"246019 15.0 Within past year (anytime less than 12 months ... \n",
"246020 2.0 Within past year (anytime less than 12 months ... \n",
"246021 0.0 Within past year (anytime less than 12 months ... \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack \\\n",
"0 Yes 9.0 None of them No \n",
"1 Yes 6.0 None of them No \n",
"2 No 8.0 6 or more, but not all No \n",
"3 Yes 9.0 None of them No \n",
"4 Yes 5.0 1 to 5 No \n",
"... ... ... ... ... \n",
"246017 Yes 6.0 None of them No \n",
"246018 Yes 7.0 None of them No \n",
"246019 Yes 7.0 1 to 5 No \n",
"246020 Yes 7.0 None of them No \n",
"246021 No 5.0 None of them Yes \n",
"\n",
" ... RaceEthnicityCategory AgeCategory HeightInMeters \\\n",
"0 ... White only, Non-Hispanic Age 65 to 69 1.60 \n",
"1 ... White only, Non-Hispanic Age 70 to 74 1.78 \n",
"2 ... White only, Non-Hispanic Age 75 to 79 1.85 \n",
"3 ... White only, Non-Hispanic Age 80 or older 1.70 \n",
"4 ... White only, Non-Hispanic Age 80 or older 1.55 \n",
"... ... ... ... ... \n",
"246017 ... White only, Non-Hispanic Age 60 to 64 1.78 \n",
"246018 ... Black only, Non-Hispanic Age 25 to 29 1.93 \n",
"246019 ... Multiracial, Non-Hispanic Age 65 to 69 1.68 \n",
"246020 ... Black only, Non-Hispanic Age 50 to 54 1.70 \n",
"246021 ... Black only, Non-Hispanic Age 70 to 74 1.83 \n",
"\n",
" WeightInKilograms HIVTesting FluVaxLast12 PneumoVaxEver \\\n",
"0 71.67 No Yes Yes \n",
"1 95.25 No Yes Yes \n",
"2 108.86 No No Yes \n",
"3 90.72 No Yes Yes \n",
"4 79.38 No Yes Yes \n",
"... ... ... ... ... \n",
"246017 102.06 No No No \n",
"246018 90.72 No No No \n",
"246019 83.91 Yes Yes Yes \n",
"246020 83.01 Yes Yes No \n",
"246021 108.86 Yes Yes Yes \n",
"\n",
" TetanusLast10Tdap HighRiskLastYear \\\n",
"0 Yes, received Tdap No \n",
"1 Yes, received tetanus shot but not sure what type No \n",
"2 No, did not receive any tetanus shot in the pa... No \n",
"3 No, did not receive any tetanus shot in the pa... No \n",
"4 No, did not receive any tetanus shot in the pa... No \n",
"... ... ... \n",
"246017 Yes, received tetanus shot but not sure what type No \n",
"246018 No, did not receive any tetanus shot in the pa... No \n",
"246019 Yes, received tetanus shot but not sure what type No \n",
"246020 Yes, received tetanus shot but not sure what type No \n",
"246021 No, did not receive any tetanus shot in the pa... No \n",
"\n",
" CovidPos \n",
"0 No \n",
"1 No \n",
"2 Yes \n",
"3 Yes \n",
"4 No \n",
"... ... \n",
"246017 No \n",
"246018 Yes \n",
"246019 Yes \n",
"246020 No \n",
"246021 Yes \n",
"\n",
"[246022 rows x 38 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped_columns"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>State</th>\n",
" <th>Sex</th>\n",
" <th>GeneralHealth</th>\n",
" <th>PhysicalHealthDays</th>\n",
" <th>MentalHealthDays</th>\n",
" <th>LastCheckupTime</th>\n",
" <th>PhysicalActivities</th>\n",
" <th>SleepHours</th>\n",
" <th>RemovedTeeth</th>\n",
" <th>HadHeartAttack</th>\n",
" <th>...</th>\n",
" <th>HeightInMeters</th>\n",
" <th>WeightInKilograms</th>\n",
" <th>BMI</th>\n",
" <th>AlcoholDrinkers</th>\n",
" <th>HIVTesting</th>\n",
" <th>FluVaxLast12</th>\n",
" <th>PneumoVaxEver</th>\n",
" <th>TetanusLast10Tdap</th>\n",
" <th>HighRiskLastYear</th>\n",
" <th>CovidPos</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.85</td>\n",
" <td>108.86</td>\n",
" <td>31.66</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.70</td>\n",
" <td>90.72</td>\n",
" <td>31.32</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Good</td>\n",
" <td>3.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>5.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.55</td>\n",
" <td>79.38</td>\n",
" <td>33.07</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.85</td>\n",
" <td>120.20</td>\n",
" <td>34.96</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Good</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>8.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.63</td>\n",
" <td>88.00</td>\n",
" <td>33.30</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</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",
" <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>246017</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past 2 years (1 year but less than 2 ye...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.78</td>\n",
" <td>102.06</td>\n",
" <td>32.28</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246018</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.93</td>\n",
" <td>90.72</td>\n",
" <td>24.34</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246019</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.68</td>\n",
" <td>83.91</td>\n",
" <td>29.86</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246020</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Excellent</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>1.70</td>\n",
" <td>83.01</td>\n",
" <td>28.66</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246021</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>1.83</td>\n",
" <td>108.86</td>\n",
" <td>32.55</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>246020 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays \\\n",
"2 Alabama Male Very good 0.0 \n",
"3 Alabama Female Fair 5.0 \n",
"4 Alabama Female Good 3.0 \n",
"5 Alabama Male Good 0.0 \n",
"6 Alabama Female Good 3.0 \n",
"... ... ... ... ... \n",
"246017 Virgin Islands Male Very good 0.0 \n",
"246018 Virgin Islands Female Fair 0.0 \n",
"246019 Virgin Islands Male Good 0.0 \n",
"246020 Virgin Islands Female Excellent 2.0 \n",
"246021 Virgin Islands Male Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"2 0.0 Within past year (anytime less than 12 months ... \n",
"3 0.0 Within past year (anytime less than 12 months ... \n",
"4 15.0 Within past year (anytime less than 12 months ... \n",
"5 0.0 Within past year (anytime less than 12 months ... \n",
"6 0.0 Within past year (anytime less than 12 months ... \n",
"... ... ... \n",
"246017 0.0 Within past 2 years (1 year but less than 2 ye... \n",
"246018 7.0 Within past year (anytime less than 12 months ... \n",
"246019 15.0 Within past year (anytime less than 12 months ... \n",
"246020 2.0 Within past year (anytime less than 12 months ... \n",
"246021 0.0 Within past year (anytime less than 12 months ... \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack \\\n",
"2 No 8.0 6 or more, but not all No \n",
"3 Yes 9.0 None of them No \n",
"4 Yes 5.0 1 to 5 No \n",
"5 Yes 7.0 None of them No \n",
"6 Yes 8.0 6 or more, but not all No \n",
"... ... ... ... ... \n",
"246017 Yes 6.0 None of them No \n",
"246018 Yes 7.0 None of them No \n",
"246019 Yes 7.0 1 to 5 No \n",
"246020 Yes 7.0 None of them No \n",
"246021 No 5.0 None of them Yes \n",
"\n",
" ... HeightInMeters WeightInKilograms BMI AlcoholDrinkers \\\n",
"2 ... 1.85 108.86 31.66 Yes \n",
"3 ... 1.70 90.72 31.32 No \n",
"4 ... 1.55 79.38 33.07 No \n",
"5 ... 1.85 120.20 34.96 Yes \n",
"6 ... 1.63 88.00 33.30 No \n",
"... ... ... ... ... ... \n",
"246017 ... 1.78 102.06 32.28 Yes \n",
"246018 ... 1.93 90.72 24.34 No \n",
"246019 ... 1.68 83.91 29.86 Yes \n",
"246020 ... 1.70 83.01 28.66 No \n",
"246021 ... 1.83 108.86 32.55 No \n",
"\n",
" HIVTesting FluVaxLast12 PneumoVaxEver \\\n",
"2 No No Yes \n",
"3 No Yes Yes \n",
"4 No Yes Yes \n",
"5 Yes Yes No \n",
"6 No Yes Yes \n",
"... ... ... ... \n",
"246017 No No No \n",
"246018 No No No \n",
"246019 Yes Yes Yes \n",
"246020 Yes Yes No \n",
"246021 Yes Yes Yes \n",
"\n",
" TetanusLast10Tdap HighRiskLastYear \\\n",
"2 No, did not receive any tetanus shot in the pa... No \n",
"3 No, did not receive any tetanus shot in the pa... No \n",
"4 No, did not receive any tetanus shot in the pa... No \n",
"5 Yes, received tetanus shot but not sure what type No \n",
"6 No, did not receive any tetanus shot in the pa... No \n",
"... ... ... \n",
"246017 Yes, received tetanus shot but not sure what type No \n",
"246018 No, did not receive any tetanus shot in the pa... No \n",
"246019 Yes, received tetanus shot but not sure what type No \n",
"246020 Yes, received tetanus shot but not sure what type No \n",
"246021 No, did not receive any tetanus shot in the pa... No \n",
"\n",
" CovidPos \n",
"2 Yes \n",
"3 Yes \n",
"4 No \n",
"5 No \n",
"6 No \n",
"... ... \n",
"246017 No \n",
"246018 Yes \n",
"246019 Yes \n",
"246020 No \n",
"246021 Yes \n",
"\n",
"[246020 rows x 40 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped_rows = df.drop([0, 1]) # Удаление строк с индексами 0 и 1\n",
"df_dropped_rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создание новых столбцов на основе данных из существующих столбцов датафрейма¶"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"df['SleepHours-HeightInMeters'] = df['SleepHours'] - df['HeightInMeters']"
]
},
{
"cell_type": "code",
"execution_count": 43,
"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>State</th>\n",
" <th>Sex</th>\n",
" <th>GeneralHealth</th>\n",
" <th>PhysicalHealthDays</th>\n",
" <th>MentalHealthDays</th>\n",
" <th>LastCheckupTime</th>\n",
" <th>PhysicalActivities</th>\n",
" <th>SleepHours</th>\n",
" <th>RemovedTeeth</th>\n",
" <th>HadHeartAttack</th>\n",
" <th>...</th>\n",
" <th>WeightInKilograms</th>\n",
" <th>BMI</th>\n",
" <th>AlcoholDrinkers</th>\n",
" <th>HIVTesting</th>\n",
" <th>FluVaxLast12</th>\n",
" <th>PneumoVaxEver</th>\n",
" <th>TetanusLast10Tdap</th>\n",
" <th>HighRiskLastYear</th>\n",
" <th>CovidPos</th>\n",
" <th>SleepHours-HeightInMeters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Very good</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>71.67</td>\n",
" <td>27.99</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received Tdap</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>7.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>95.25</td>\n",
" <td>30.13</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>4.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>108.86</td>\n",
" <td>31.66</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>6.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>90.72</td>\n",
" <td>31.32</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Good</td>\n",
" <td>3.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>5.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>79.38</td>\n",
" <td>33.07</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>3.45</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",
" <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>246017</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past 2 years (1 year but less than 2 ye...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>102.06</td>\n",
" <td>32.28</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>4.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246018</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>90.72</td>\n",
" <td>24.34</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>5.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246019</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>83.91</td>\n",
" <td>29.86</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>5.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246020</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Excellent</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>83.01</td>\n",
" <td>28.66</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>5.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246021</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>108.86</td>\n",
" <td>32.55</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>3.17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>246022 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays \\\n",
"0 Alabama Female Very good 4.0 \n",
"1 Alabama Male Very good 0.0 \n",
"2 Alabama Male Very good 0.0 \n",
"3 Alabama Female Fair 5.0 \n",
"4 Alabama Female Good 3.0 \n",
"... ... ... ... ... \n",
"246017 Virgin Islands Male Very good 0.0 \n",
"246018 Virgin Islands Female Fair 0.0 \n",
"246019 Virgin Islands Male Good 0.0 \n",
"246020 Virgin Islands Female Excellent 2.0 \n",
"246021 Virgin Islands Male Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"0 0.0 Within past year (anytime less than 12 months ... \n",
"1 0.0 Within past year (anytime less than 12 months ... \n",
"2 0.0 Within past year (anytime less than 12 months ... \n",
"3 0.0 Within past year (anytime less than 12 months ... \n",
"4 15.0 Within past year (anytime less than 12 months ... \n",
"... ... ... \n",
"246017 0.0 Within past 2 years (1 year but less than 2 ye... \n",
"246018 7.0 Within past year (anytime less than 12 months ... \n",
"246019 15.0 Within past year (anytime less than 12 months ... \n",
"246020 2.0 Within past year (anytime less than 12 months ... \n",
"246021 0.0 Within past year (anytime less than 12 months ... \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack \\\n",
"0 Yes 9.0 None of them No \n",
"1 Yes 6.0 None of them No \n",
"2 No 8.0 6 or more, but not all No \n",
"3 Yes 9.0 None of them No \n",
"4 Yes 5.0 1 to 5 No \n",
"... ... ... ... ... \n",
"246017 Yes 6.0 None of them No \n",
"246018 Yes 7.0 None of them No \n",
"246019 Yes 7.0 1 to 5 No \n",
"246020 Yes 7.0 None of them No \n",
"246021 No 5.0 None of them Yes \n",
"\n",
" ... WeightInKilograms BMI AlcoholDrinkers HIVTesting FluVaxLast12 \\\n",
"0 ... 71.67 27.99 No No Yes \n",
"1 ... 95.25 30.13 No No Yes \n",
"2 ... 108.86 31.66 Yes No No \n",
"3 ... 90.72 31.32 No No Yes \n",
"4 ... 79.38 33.07 No No Yes \n",
"... ... ... ... ... ... ... \n",
"246017 ... 102.06 32.28 Yes No No \n",
"246018 ... 90.72 24.34 No No No \n",
"246019 ... 83.91 29.86 Yes Yes Yes \n",
"246020 ... 83.01 28.66 No Yes Yes \n",
"246021 ... 108.86 32.55 No Yes Yes \n",
"\n",
" PneumoVaxEver TetanusLast10Tdap \\\n",
"0 Yes Yes, received Tdap \n",
"1 Yes Yes, received tetanus shot but not sure what type \n",
"2 Yes No, did not receive any tetanus shot in the pa... \n",
"3 Yes No, did not receive any tetanus shot in the pa... \n",
"4 Yes No, did not receive any tetanus shot in the pa... \n",
"... ... ... \n",
"246017 No Yes, received tetanus shot but not sure what type \n",
"246018 No No, did not receive any tetanus shot in the pa... \n",
"246019 Yes Yes, received tetanus shot but not sure what type \n",
"246020 No Yes, received tetanus shot but not sure what type \n",
"246021 Yes No, did not receive any tetanus shot in the pa... \n",
"\n",
" HighRiskLastYear CovidPos SleepHours-HeightInMeters \n",
"0 No No 7.40 \n",
"1 No No 4.22 \n",
"2 No Yes 6.15 \n",
"3 No Yes 7.30 \n",
"4 No No 3.45 \n",
"... ... ... ... \n",
"246017 No No 4.22 \n",
"246018 No Yes 5.07 \n",
"246019 No Yes 5.32 \n",
"246020 No No 5.30 \n",
"246021 No Yes 3.17 \n",
"\n",
"[246022 rows x 41 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление строк с пустыми значениями"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"State 0\n",
"Sex 0\n",
"GeneralHealth 0\n",
"PhysicalHealthDays 0\n",
"MentalHealthDays 0\n",
"LastCheckupTime 0\n",
"PhysicalActivities 0\n",
"SleepHours 0\n",
"RemovedTeeth 0\n",
"HadHeartAttack 0\n",
"HadAngina 0\n",
"HadStroke 0\n",
"HadAsthma 0\n",
"HadSkinCancer 0\n",
"HadCOPD 0\n",
"HadDepressiveDisorder 0\n",
"HadKidneyDisease 0\n",
"HadArthritis 0\n",
"HadDiabetes 0\n",
"DeafOrHardOfHearing 0\n",
"BlindOrVisionDifficulty 0\n",
"DifficultyConcentrating 0\n",
"DifficultyWalking 0\n",
"DifficultyDressingBathing 0\n",
"DifficultyErrands 0\n",
"SmokerStatus 0\n",
"ECigaretteUsage 0\n",
"ChestScan 0\n",
"RaceEthnicityCategory 0\n",
"AgeCategory 0\n",
"HeightInMeters 0\n",
"WeightInKilograms 0\n",
"BMI 0\n",
"AlcoholDrinkers 0\n",
"HIVTesting 0\n",
"FluVaxLast12 0\n",
"PneumoVaxEver 0\n",
"TetanusLast10Tdap 0\n",
"HighRiskLastYear 0\n",
"CovidPos 0\n",
"SleepHours-HeightInMeters 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(df.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>State</th>\n",
" <th>Sex</th>\n",
" <th>GeneralHealth</th>\n",
" <th>PhysicalHealthDays</th>\n",
" <th>MentalHealthDays</th>\n",
" <th>LastCheckupTime</th>\n",
" <th>PhysicalActivities</th>\n",
" <th>SleepHours</th>\n",
" <th>RemovedTeeth</th>\n",
" <th>HadHeartAttack</th>\n",
" <th>...</th>\n",
" <th>WeightInKilograms</th>\n",
" <th>BMI</th>\n",
" <th>AlcoholDrinkers</th>\n",
" <th>HIVTesting</th>\n",
" <th>FluVaxLast12</th>\n",
" <th>PneumoVaxEver</th>\n",
" <th>TetanusLast10Tdap</th>\n",
" <th>HighRiskLastYear</th>\n",
" <th>CovidPos</th>\n",
" <th>SleepHours-HeightInMeters</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Very good</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>71.67</td>\n",
" <td>27.99</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received Tdap</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>7.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>95.25</td>\n",
" <td>30.13</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>4.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>8.0</td>\n",
" <td>6 or more, but not all</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>108.86</td>\n",
" <td>31.66</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>6.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>9.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>90.72</td>\n",
" <td>31.32</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>Female</td>\n",
" <td>Good</td>\n",
" <td>3.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>5.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>79.38</td>\n",
" <td>33.07</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>3.45</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",
" <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>246017</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past 2 years (1 year but less than 2 ye...</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>102.06</td>\n",
" <td>32.28</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>4.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246018</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Fair</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>90.72</td>\n",
" <td>24.34</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>5.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246019</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Good</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>1 to 5</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>83.91</td>\n",
" <td>29.86</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>5.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246020</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Female</td>\n",
" <td>Excellent</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>Yes</td>\n",
" <td>7.0</td>\n",
" <td>None of them</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>83.01</td>\n",
" <td>28.66</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Yes, received tetanus shot but not sure what type</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>5.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246021</th>\n",
" <td>Virgin Islands</td>\n",
" <td>Male</td>\n",
" <td>Very good</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Within past year (anytime less than 12 months ...</td>\n",
" <td>No</td>\n",
" <td>5.0</td>\n",
" <td>None of them</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>108.86</td>\n",
" <td>32.55</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No, did not receive any tetanus shot in the pa...</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>3.17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>246022 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" State Sex GeneralHealth PhysicalHealthDays \\\n",
"0 Alabama Female Very good 4.0 \n",
"1 Alabama Male Very good 0.0 \n",
"2 Alabama Male Very good 0.0 \n",
"3 Alabama Female Fair 5.0 \n",
"4 Alabama Female Good 3.0 \n",
"... ... ... ... ... \n",
"246017 Virgin Islands Male Very good 0.0 \n",
"246018 Virgin Islands Female Fair 0.0 \n",
"246019 Virgin Islands Male Good 0.0 \n",
"246020 Virgin Islands Female Excellent 2.0 \n",
"246021 Virgin Islands Male Very good 0.0 \n",
"\n",
" MentalHealthDays LastCheckupTime \\\n",
"0 0.0 Within past year (anytime less than 12 months ... \n",
"1 0.0 Within past year (anytime less than 12 months ... \n",
"2 0.0 Within past year (anytime less than 12 months ... \n",
"3 0.0 Within past year (anytime less than 12 months ... \n",
"4 15.0 Within past year (anytime less than 12 months ... \n",
"... ... ... \n",
"246017 0.0 Within past 2 years (1 year but less than 2 ye... \n",
"246018 7.0 Within past year (anytime less than 12 months ... \n",
"246019 15.0 Within past year (anytime less than 12 months ... \n",
"246020 2.0 Within past year (anytime less than 12 months ... \n",
"246021 0.0 Within past year (anytime less than 12 months ... \n",
"\n",
" PhysicalActivities SleepHours RemovedTeeth HadHeartAttack \\\n",
"0 Yes 9.0 None of them No \n",
"1 Yes 6.0 None of them No \n",
"2 No 8.0 6 or more, but not all No \n",
"3 Yes 9.0 None of them No \n",
"4 Yes 5.0 1 to 5 No \n",
"... ... ... ... ... \n",
"246017 Yes 6.0 None of them No \n",
"246018 Yes 7.0 None of them No \n",
"246019 Yes 7.0 1 to 5 No \n",
"246020 Yes 7.0 None of them No \n",
"246021 No 5.0 None of them Yes \n",
"\n",
" ... WeightInKilograms BMI AlcoholDrinkers HIVTesting FluVaxLast12 \\\n",
"0 ... 71.67 27.99 No No Yes \n",
"1 ... 95.25 30.13 No No Yes \n",
"2 ... 108.86 31.66 Yes No No \n",
"3 ... 90.72 31.32 No No Yes \n",
"4 ... 79.38 33.07 No No Yes \n",
"... ... ... ... ... ... ... \n",
"246017 ... 102.06 32.28 Yes No No \n",
"246018 ... 90.72 24.34 No No No \n",
"246019 ... 83.91 29.86 Yes Yes Yes \n",
"246020 ... 83.01 28.66 No Yes Yes \n",
"246021 ... 108.86 32.55 No Yes Yes \n",
"\n",
" PneumoVaxEver TetanusLast10Tdap \\\n",
"0 Yes Yes, received Tdap \n",
"1 Yes Yes, received tetanus shot but not sure what type \n",
"2 Yes No, did not receive any tetanus shot in the pa... \n",
"3 Yes No, did not receive any tetanus shot in the pa... \n",
"4 Yes No, did not receive any tetanus shot in the pa... \n",
"... ... ... \n",
"246017 No Yes, received tetanus shot but not sure what type \n",
"246018 No No, did not receive any tetanus shot in the pa... \n",
"246019 Yes Yes, received tetanus shot but not sure what type \n",
"246020 No Yes, received tetanus shot but not sure what type \n",
"246021 Yes No, did not receive any tetanus shot in the pa... \n",
"\n",
" HighRiskLastYear CovidPos SleepHours-HeightInMeters \n",
"0 No No 7.40 \n",
"1 No No 4.22 \n",
"2 No Yes 6.15 \n",
"3 No Yes 7.30 \n",
"4 No No 3.45 \n",
"... ... ... ... \n",
"246017 No No 4.22 \n",
"246018 No Yes 5.07 \n",
"246019 No Yes 5.32 \n",
"246020 No No 5.30 \n",
"246021 No Yes 3.17 \n",
"\n",
"[246022 rows x 41 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna() #Тк.пустых строк нет, мы ничего не удалили"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"#df.fillna(df.mean(), inplace=True)\n",
"#df.fillna(df.median(), inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Мы обрабатываем пустые значения для каждого столбца отдельно\n",
"\n",
"Мы можем заполнить пропуски средним или медианой, если это числовой столбец\n",
"\n",
"Мы заполняем средним, если в колонке нет выбросов\n",
"\n",
"Если столбец категориальный, то мы можем заполнить пропуски модой (самым часто встречающимся значением)\n",
"\n",
"Если пропусков мало, то их можно просто удалить."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Возможности визуализации"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Линейная диаграмма\n",
"plt.figure(figsize=(10, 5))\n",
"df['WeightInKilograms'].plot(title='Line Plot (WeightInKilograms)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Гистограмма\n",
"plt.figure(figsize=(8, 5))\n",
"df.plot.hist(column=[\"SleepHours\"], bins=80)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df['AgeCategory'].value_counts().plot(kind='bar', title='Bar Plot (AgeCategory)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df[\"BMI\"].plot(kind = \"box\", title='Ящик с усами')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df[['AgeCategory', 'BMI']].plot(kind='area', alpha=0.2, title='Area Plot (AgeCategory, BMI)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='BMI', ylabel='WeightInKilograms'>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x=\"BMI\", y=\"WeightInKilograms\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df['AgeCategory'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Pie Chart (AgeCategory)')\n",
"plt.show()\n"
]
}
],
"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": "3.12.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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