MAI_ISE-31_Andrikhov-A-S/lab1.ipynb

1. Основные возможности работы с библиотекой pandas

In [27]:

import pandas as pd

Загрузка и сохранение данных

In [28]:

df = pd.read_csv("./datasets/2022/heart_2022_no_nans.csv")

In [58]:

df.tail(10)

Out[58]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos	SleepHours-HeightInMeters
246012	Virgin Islands	Male	Fair	7.0	30.0	Within past year (anytime less than 12 months ...	No	4.0	None of them	Yes	...	117.93	33.38	Yes	Yes	No	No	No, did not receive any tetanus shot in the pa...	No	Yes	2.12
246013	Virgin Islands	Male	Excellent	0.0	7.0	Within past year (anytime less than 12 months ...	No	4.0	None of them	No	...	49.90	18.30	Yes	No	No	No	No, did not receive any tetanus shot in the pa...	No	No	2.35
246014	Virgin Islands	Female	Good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	12.0	1 to 5	No	...	52.16	19.14	No	No	No	Yes	Yes, received Tdap	No	No	10.35
246015	Virgin Islands	Female	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	1 to 5	No	...	77.11	28.29	Yes	Yes	No	No	No, did not receive any tetanus shot in the pa...	No	No	5.35
246016	Virgin Islands	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	No	6.0	1 to 5	Yes	...	118.84	36.54	Yes	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No	4.20
246017	Virgin Islands	Male	Very good	0.0	0.0	Within past 2 years (1 year but less than 2 ye...	Yes	6.0	None of them	No	...	102.06	32.28	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	No	4.22
246018	Virgin Islands	Female	Fair	0.0	7.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	90.72	24.34	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes	5.07
246019	Virgin Islands	Male	Good	0.0	15.0	Within past year (anytime less than 12 months ...	Yes	7.0	1 to 5	No	...	83.91	29.86	Yes	Yes	Yes	Yes	Yes, received tetanus shot but not sure what type	No	Yes	5.32
246020	Virgin Islands	Female	Excellent	2.0	2.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	83.01	28.66	No	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No	5.30
246021	Virgin Islands	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	5.0	None of them	Yes	...	108.86	32.55	No	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	3.17

10 rows × 41 columns

In [57]:

df.head(10)

Out[57]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos	SleepHours-HeightInMeters
0	Alabama	Female	Very good	4.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	71.67	27.99	No	No	Yes	Yes	Yes, received Tdap	No	No	7.40
1	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	6.0	None of them	No	...	95.25	30.13	No	No	Yes	Yes	Yes, received tetanus shot but not sure what type	No	No	4.22
2	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	6 or more, but not all	No	...	108.86	31.66	Yes	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	6.15
3	Alabama	Female	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	90.72	31.32	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	7.30
4	Alabama	Female	Good	3.0	15.0	Within past year (anytime less than 12 months ...	Yes	5.0	1 to 5	No	...	79.38	33.07	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No	3.45
5	Alabama	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	120.20	34.96	Yes	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No	5.15
6	Alabama	Female	Good	3.0	0.0	Within past year (anytime less than 12 months ...	Yes	8.0	6 or more, but not all	No	...	88.00	33.30	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No	6.37
7	Alabama	Male	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	8.0	1 to 5	Yes	...	74.84	24.37	No	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	6.25
8	Alabama	Male	Good	2.0	0.0	5 or more years ago	No	6.0	None of them	No	...	78.02	26.94	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes	4.30
9	Alabama	Female	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	63.50	22.60	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No	5.32

10 rows × 41 columns

In [30]:

df.to_csv("new.csv", index=False)

Получение сведений о датафрейме с данными¶

In [31]:

df.describe()

Out[31]:

	PhysicalHealthDays	MentalHealthDays	SleepHours	HeightInMeters	WeightInKilograms	BMI
count	246022.000000	246022.000000	246022.000000	246022.000000	246022.000000	246022.000000
mean	4.119026	4.167140	7.021331	1.705150	83.615179	28.668136
std	8.405844	8.102687	1.440681	0.106654	21.323156	6.513973
min	0.000000	0.000000	1.000000	0.910000	28.120000	12.020000
25%	0.000000	0.000000	6.000000	1.630000	68.040000	24.270000
50%	0.000000	0.000000	7.000000	1.700000	81.650000	27.460000
75%	3.000000	4.000000	8.000000	1.780000	95.250000	31.890000
max	30.000000	30.000000	24.000000	2.410000	292.570000	97.650000

In [32]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 246022 entries, 0 to 246021
Data columns (total 40 columns):
 #   Column                     Non-Null Count   Dtype  
---  ------                     --------------   -----  
 0   State                      246022 non-null  object 
 1   Sex                        246022 non-null  object 
 2   GeneralHealth              246022 non-null  object 
 3   PhysicalHealthDays         246022 non-null  float64
 4   MentalHealthDays           246022 non-null  float64
 5   LastCheckupTime            246022 non-null  object 
 6   PhysicalActivities         246022 non-null  object 
 7   SleepHours                 246022 non-null  float64
 8   RemovedTeeth               246022 non-null  object 
 9   HadHeartAttack             246022 non-null  object 
 10  HadAngina                  246022 non-null  object 
 11  HadStroke                  246022 non-null  object 
 12  HadAsthma                  246022 non-null  object 
 13  HadSkinCancer              246022 non-null  object 
 14  HadCOPD                    246022 non-null  object 
 15  HadDepressiveDisorder      246022 non-null  object 
 16  HadKidneyDisease           246022 non-null  object 
 17  HadArthritis               246022 non-null  object 
 18  HadDiabetes                246022 non-null  object 
 19  DeafOrHardOfHearing        246022 non-null  object 
 20  BlindOrVisionDifficulty    246022 non-null  object 
 21  DifficultyConcentrating    246022 non-null  object 
 22  DifficultyWalking          246022 non-null  object 
 23  DifficultyDressingBathing  246022 non-null  object 
 24  DifficultyErrands          246022 non-null  object 
 25  SmokerStatus               246022 non-null  object 
 26  ECigaretteUsage            246022 non-null  object 
 27  ChestScan                  246022 non-null  object 
 28  RaceEthnicityCategory      246022 non-null  object 
 29  AgeCategory                246022 non-null  object 
 30  HeightInMeters             246022 non-null  float64
 31  WeightInKilograms          246022 non-null  float64
 32  BMI                        246022 non-null  float64
 33  AlcoholDrinkers            246022 non-null  object 
 34  HIVTesting                 246022 non-null  object 
 35  FluVaxLast12               246022 non-null  object 
 36  PneumoVaxEver              246022 non-null  object 
 37  TetanusLast10Tdap          246022 non-null  object 
 38  HighRiskLastYear           246022 non-null  object 
 39  CovidPos                   246022 non-null  object 
dtypes: float64(6), object(34)
memory usage: 75.1+ MB

Получение сведений о колонках датафрейма¶

In [33]:

df.columns

Out[33]:

Index(['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays',
       'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities',
       'SleepHours', 'RemovedTeeth', 'HadHeartAttack', 'HadAngina',
       'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',
       'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',
       'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',
       'DifficultyConcentrating', 'DifficultyWalking',
       'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',
       'ECigaretteUsage', 'ChestScan', 'RaceEthnicityCategory', 'AgeCategory',
       'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers',
       'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver', 'TetanusLast10Tdap',
       'HighRiskLastYear', 'CovidPos'],
      dtype='object')

Вывод отельных строки и столбцов из датафрейма

In [34]:

df[["Sex", "HadHeartAttack", "WeightInKilograms"]]

Out[34]:

	Sex	HadHeartAttack	WeightInKilograms
0	Female	No	71.67
1	Male	No	95.25
2	Male	No	108.86
3	Female	No	90.72
4	Female	No	79.38
...	...	...	...
246017	Male	No	102.06
246018	Female	No	90.72
246019	Male	No	83.91
246020	Female	No	83.01
246021	Male	Yes	108.86

246022 rows × 3 columns

In [35]:

df.iloc[3:6]

Out[35]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	HeightInMeters	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos
3	Alabama	Female	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	1.70	90.72	31.32	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
4	Alabama	Female	Good	3.0	15.0	Within past year (anytime less than 12 months ...	Yes	5.0	1 to 5	No	...	1.55	79.38	33.07	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No
5	Alabama	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	1.85	120.20	34.96	Yes	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No

3 rows × 40 columns

In [36]:

df[df['WeightInKilograms'] > 100]

Out[36]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	HeightInMeters	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos
2	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	6 or more, but not all	No	...	1.85	108.86	31.66	Yes	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
5	Alabama	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	1.85	120.20	34.96	Yes	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No
10	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	8.0	1 to 5	No	...	1.83	122.47	36.62	Yes	No	Yes	Yes	Yes, received Tdap	No	No
11	Alabama	Female	Good	3.0	4.0	Within past year (anytime less than 12 months ...	Yes	5.0	None of them	No	...	1.52	108.86	46.87	No	No	No	No	Yes, received tetanus shot, but not Tdap	No	Yes
12	Alabama	Male	Good	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	5.0	6 or more, but not all	Yes	...	1.88	115.67	32.74	No	No	Yes	Yes	Yes, received tetanus shot but not sure what type	No	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
246002	Virgin Islands	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	6.0	1 to 5	No	...	1.88	106.59	30.17	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	Tested positive using home test without a heal...
246012	Virgin Islands	Male	Fair	7.0	30.0	Within past year (anytime less than 12 months ...	No	4.0	None of them	Yes	...	1.88	117.93	33.38	Yes	Yes	No	No	No, did not receive any tetanus shot in the pa...	No	Yes
246016	Virgin Islands	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	No	6.0	1 to 5	Yes	...	1.80	118.84	36.54	Yes	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No
246017	Virgin Islands	Male	Very good	0.0	0.0	Within past 2 years (1 year but less than 2 ye...	Yes	6.0	None of them	No	...	1.78	102.06	32.28	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	No
246021	Virgin Islands	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	5.0	None of them	Yes	...	1.83	108.86	32.55	No	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes

44646 rows × 40 columns

Группировка и агрегация данных в датафрейме¶

In [37]:

group = df.groupby(['State'])['WeightInKilograms'].mean()
group.to_frame()

Out[37]:

	WeightInKilograms
State
Alabama	85.225899
Alaska	83.937201
Arizona	82.626862
Arkansas	85.361796
California	81.334135
Colorado	80.805505
Connecticut	82.192881
Delaware	84.224436
District of Columbia	78.593038
Florida	83.155785
Georgia	84.332240
Guam	77.294261
Hawaii	76.419335
Idaho	84.648567
Illinois	83.459467
Indiana	85.703237
Iowa	86.970651
Kansas	85.864583
Kentucky	86.781960
Louisiana	85.162787
Maine	82.949232
Maryland	83.543344
Massachusetts	80.591010
Michigan	83.629868
Minnesota	84.954303
Mississippi	88.322797
Missouri	85.836119
Montana	84.231140
Nebraska	85.961696
Nevada	82.784771
New Hampshire	80.702764
New Jersey	81.270844
New Mexico	80.529087
New York	80.960180
North Carolina	83.730953
North Dakota	85.924972
Ohio	86.938279
Oklahoma	85.517429
Oregon	83.802043
Pennsylvania	83.831872
Puerto Rico	79.152187
Rhode Island	80.675832
South Carolina	84.046443
South Dakota	86.868195
Tennessee	86.237325
Texas	84.894035
Utah	83.888474
Vermont	80.557657
Virgin Islands	82.131440
Virginia	83.822634
Washington	83.077369
West Virginia	86.697505
Wisconsin	86.167571
Wyoming	83.844357

Сортировка данных в датафрейме

In [38]:

sorted_df = df.sort_values(by='WeightInKilograms', ascending = False)
sorted_df

Out[38]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	HeightInMeters	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos
9060	Arizona	Male	Fair	15.0	15.0	Within past year (anytime less than 12 months ...	No	8.0	None of them	No	...	1.85	292.57	85.10	No	No	No	No	Yes, received Tdap	No	No
48969	Hawaii	Male	Poor	30.0	30.0	Within past year (anytime less than 12 months ...	Yes	4.0	None of them	No	...	1.93	276.24	74.13	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes
75697	Kentucky	Male	Very good	0.0	0.0	5 or more years ago	No	7.0	None of them	No	...	1.91	273.52	75.37	No	No	No	No	Yes, received tetanus shot but not sure what type	No	No
143147	New York	Male	Very good	3.0	1.0	Within past year (anytime less than 12 months ...	Yes	8.0	None of them	No	...	1.88	273.06	77.29	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	No
76244	Kentucky	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	1.83	272.16	81.37	No	Yes	No	No	Yes, received tetanus shot but not sure what type	No	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
203695	Vermont	Female	Poor	30.0	3.0	Within past 2 years (1 year but less than 2 ye...	No	18.0	All	No	...	1.60	30.84	12.05	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	No
242632	Puerto Rico	Female	Fair	30.0	7.0	Within past year (anytime less than 12 months ...	No	7.0	6 or more, but not all	No	...	1.35	30.39	16.77	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No
11614	Arkansas	Female	Poor	30.0	30.0	Within past year (anytime less than 12 months ...	No	8.0	All	No	...	1.52	29.48	12.69	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
127404	Nebraska	Female	Poor	30.0	0.0	Within past year (anytime less than 12 months ...	Yes	6.0	None of them	No	...	1.52	29.48	12.69	No	No	No	Yes	Yes, received tetanus shot but not sure what type	No	No
179326	South Carolina	Female	Very good	0.0	0.0	5 or more years ago	No	8.0	None of them	No	...	1.52	28.12	12.11	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	No

246022 rows × 40 columns

Удаление строк/столбцов

In [39]:

df_dropped_columns = df.drop(columns=['AlcoholDrinkers', 'BMI'])  # Удаление столбцов 'AlcoholDrinkers' и 'BMI'

In [40]:

df_dropped_columns

Out[40]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	RaceEthnicityCategory	AgeCategory	HeightInMeters	WeightInKilograms	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos
0	Alabama	Female	Very good	4.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	White only, Non-Hispanic	Age 65 to 69	1.60	71.67	No	Yes	Yes	Yes, received Tdap	No	No
1	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	6.0	None of them	No	...	White only, Non-Hispanic	Age 70 to 74	1.78	95.25	No	Yes	Yes	Yes, received tetanus shot but not sure what type	No	No
2	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	6 or more, but not all	No	...	White only, Non-Hispanic	Age 75 to 79	1.85	108.86	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
3	Alabama	Female	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	White only, Non-Hispanic	Age 80 or older	1.70	90.72	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
4	Alabama	Female	Good	3.0	15.0	Within past year (anytime less than 12 months ...	Yes	5.0	1 to 5	No	...	White only, Non-Hispanic	Age 80 or older	1.55	79.38	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
246017	Virgin Islands	Male	Very good	0.0	0.0	Within past 2 years (1 year but less than 2 ye...	Yes	6.0	None of them	No	...	White only, Non-Hispanic	Age 60 to 64	1.78	102.06	No	No	No	Yes, received tetanus shot but not sure what type	No	No
246018	Virgin Islands	Female	Fair	0.0	7.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	Black only, Non-Hispanic	Age 25 to 29	1.93	90.72	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes
246019	Virgin Islands	Male	Good	0.0	15.0	Within past year (anytime less than 12 months ...	Yes	7.0	1 to 5	No	...	Multiracial, Non-Hispanic	Age 65 to 69	1.68	83.91	Yes	Yes	Yes	Yes, received tetanus shot but not sure what type	No	Yes
246020	Virgin Islands	Female	Excellent	2.0	2.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	Black only, Non-Hispanic	Age 50 to 54	1.70	83.01	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No
246021	Virgin Islands	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	5.0	None of them	Yes	...	Black only, Non-Hispanic	Age 70 to 74	1.83	108.86	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes

246022 rows × 38 columns

In [41]:

df_dropped_rows = df.drop([0, 1])  # Удаление строк с индексами 0 и 1
df_dropped_rows

Out[41]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	HeightInMeters	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos
2	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	6 or more, but not all	No	...	1.85	108.86	31.66	Yes	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
3	Alabama	Female	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	1.70	90.72	31.32	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes
4	Alabama	Female	Good	3.0	15.0	Within past year (anytime less than 12 months ...	Yes	5.0	1 to 5	No	...	1.55	79.38	33.07	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No
5	Alabama	Male	Good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	1.85	120.20	34.96	Yes	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No
6	Alabama	Female	Good	3.0	0.0	Within past year (anytime less than 12 months ...	Yes	8.0	6 or more, but not all	No	...	1.63	88.00	33.30	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
246017	Virgin Islands	Male	Very good	0.0	0.0	Within past 2 years (1 year but less than 2 ye...	Yes	6.0	None of them	No	...	1.78	102.06	32.28	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	No
246018	Virgin Islands	Female	Fair	0.0	7.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	1.93	90.72	24.34	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes
246019	Virgin Islands	Male	Good	0.0	15.0	Within past year (anytime less than 12 months ...	Yes	7.0	1 to 5	No	...	1.68	83.91	29.86	Yes	Yes	Yes	Yes	Yes, received tetanus shot but not sure what type	No	Yes
246020	Virgin Islands	Female	Excellent	2.0	2.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	1.70	83.01	28.66	No	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No
246021	Virgin Islands	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	5.0	None of them	Yes	...	1.83	108.86	32.55	No	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes

246020 rows × 40 columns

Создание новых столбцов на основе данных из существующих столбцов датафрейма¶

In [42]:

df['SleepHours-HeightInMeters'] = df['SleepHours'] - df['HeightInMeters']

In [43]:

df

Out[43]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos	SleepHours-HeightInMeters
0	Alabama	Female	Very good	4.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	71.67	27.99	No	No	Yes	Yes	Yes, received Tdap	No	No	7.40
1	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	6.0	None of them	No	...	95.25	30.13	No	No	Yes	Yes	Yes, received tetanus shot but not sure what type	No	No	4.22
2	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	6 or more, but not all	No	...	108.86	31.66	Yes	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	6.15
3	Alabama	Female	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	90.72	31.32	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	7.30
4	Alabama	Female	Good	3.0	15.0	Within past year (anytime less than 12 months ...	Yes	5.0	1 to 5	No	...	79.38	33.07	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No	3.45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
246017	Virgin Islands	Male	Very good	0.0	0.0	Within past 2 years (1 year but less than 2 ye...	Yes	6.0	None of them	No	...	102.06	32.28	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	No	4.22
246018	Virgin Islands	Female	Fair	0.0	7.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	90.72	24.34	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes	5.07
246019	Virgin Islands	Male	Good	0.0	15.0	Within past year (anytime less than 12 months ...	Yes	7.0	1 to 5	No	...	83.91	29.86	Yes	Yes	Yes	Yes	Yes, received tetanus shot but not sure what type	No	Yes	5.32
246020	Virgin Islands	Female	Excellent	2.0	2.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	83.01	28.66	No	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No	5.30
246021	Virgin Islands	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	5.0	None of them	Yes	...	108.86	32.55	No	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	3.17

246022 rows × 41 columns

Удаление строк с пустыми значениями

In [44]:

print(df.isna().sum())

State                        0
Sex                          0
GeneralHealth                0
PhysicalHealthDays           0
MentalHealthDays             0
LastCheckupTime              0
PhysicalActivities           0
SleepHours                   0
RemovedTeeth                 0
HadHeartAttack               0
HadAngina                    0
HadStroke                    0
HadAsthma                    0
HadSkinCancer                0
HadCOPD                      0
HadDepressiveDisorder        0
HadKidneyDisease             0
HadArthritis                 0
HadDiabetes                  0
DeafOrHardOfHearing          0
BlindOrVisionDifficulty      0
DifficultyConcentrating      0
DifficultyWalking            0
DifficultyDressingBathing    0
DifficultyErrands            0
SmokerStatus                 0
ECigaretteUsage              0
ChestScan                    0
RaceEthnicityCategory        0
AgeCategory                  0
HeightInMeters               0
WeightInKilograms            0
BMI                          0
AlcoholDrinkers              0
HIVTesting                   0
FluVaxLast12                 0
PneumoVaxEver                0
TetanusLast10Tdap            0
HighRiskLastYear             0
CovidPos                     0
SleepHours-HeightInMeters    0
dtype: int64

In [45]:

df.dropna() #Тк.пустых строк нет, мы ничего не удалили

Out[45]:

	State	Sex	GeneralHealth	PhysicalHealthDays	MentalHealthDays	LastCheckupTime	PhysicalActivities	SleepHours	RemovedTeeth	HadHeartAttack	...	WeightInKilograms	BMI	AlcoholDrinkers	HIVTesting	FluVaxLast12	PneumoVaxEver	TetanusLast10Tdap	HighRiskLastYear	CovidPos	SleepHours-HeightInMeters
0	Alabama	Female	Very good	4.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	71.67	27.99	No	No	Yes	Yes	Yes, received Tdap	No	No	7.40
1	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	Yes	6.0	None of them	No	...	95.25	30.13	No	No	Yes	Yes	Yes, received tetanus shot but not sure what type	No	No	4.22
2	Alabama	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	8.0	6 or more, but not all	No	...	108.86	31.66	Yes	No	No	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	6.15
3	Alabama	Female	Fair	5.0	0.0	Within past year (anytime less than 12 months ...	Yes	9.0	None of them	No	...	90.72	31.32	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	7.30
4	Alabama	Female	Good	3.0	15.0	Within past year (anytime less than 12 months ...	Yes	5.0	1 to 5	No	...	79.38	33.07	No	No	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	No	3.45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
246017	Virgin Islands	Male	Very good	0.0	0.0	Within past 2 years (1 year but less than 2 ye...	Yes	6.0	None of them	No	...	102.06	32.28	Yes	No	No	No	Yes, received tetanus shot but not sure what type	No	No	4.22
246018	Virgin Islands	Female	Fair	0.0	7.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	90.72	24.34	No	No	No	No	No, did not receive any tetanus shot in the pa...	No	Yes	5.07
246019	Virgin Islands	Male	Good	0.0	15.0	Within past year (anytime less than 12 months ...	Yes	7.0	1 to 5	No	...	83.91	29.86	Yes	Yes	Yes	Yes	Yes, received tetanus shot but not sure what type	No	Yes	5.32
246020	Virgin Islands	Female	Excellent	2.0	2.0	Within past year (anytime less than 12 months ...	Yes	7.0	None of them	No	...	83.01	28.66	No	Yes	Yes	No	Yes, received tetanus shot but not sure what type	No	No	5.30
246021	Virgin Islands	Male	Very good	0.0	0.0	Within past year (anytime less than 12 months ...	No	5.0	None of them	Yes	...	108.86	32.55	No	Yes	Yes	Yes	No, did not receive any tetanus shot in the pa...	No	Yes	3.17

246022 rows × 41 columns

In [46]:

#df.fillna(df.mean(), inplace=True)
#df.fillna(df.median(), inplace=True)

Мы обрабатываем пустые значения для каждого столбца отдельно

Мы можем заполнить пропуски средним или медианой, если это числовой столбец

Мы заполняем средним, если в колонке нет выбросов

Если столбец категориальный, то мы можем заполнить пропуски модой (самым часто встречающимся значением)

Если пропусков мало, то их можно просто удалить.

2. Возможности визуализации

In [47]:

import matplotlib.pyplot as plt

In [48]:

#Линейная диаграмма
plt.figure(figsize=(10, 5))
df['WeightInKilograms'].plot(title='Line Plot (WeightInKilograms)')
plt.show()

In [49]:

#Гистограмма
plt.figure(figsize=(8, 5))
df.plot.hist(column=["SleepHours"], bins=80)
plt.show()

<Figure size 800x500 with 0 Axes>

In [50]:

plt.figure(figsize=(8, 5))
df['AgeCategory'].value_counts().plot(kind='bar', title='Bar Plot (AgeCategory)')
plt.show()

In [53]:

plt.figure(figsize=(8, 5))
df["BMI"].plot(kind = "box", title='Ящик с усами')
plt.show()

In [ ]:

plt.figure(figsize=(8, 5))
df[['AgeCategory', 'BMI']].plot(kind='area', alpha=0.2, title='Area Plot (AgeCategory, BMI)')
plt.show()

<Figure size 800x500 with 0 Axes>

In [ ]:

df.plot.scatter(x="BMI", y="WeightInKilograms")

Out[ ]:

<Axes: xlabel='BMI', ylabel='WeightInKilograms'>

In [ ]:

plt.figure(figsize=(8, 5))
df['AgeCategory'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Pie Chart (AgeCategory)')
plt.show()