DataFrame-数据检查

23 minute read

Published: April 14, 2022

导入pandas库，为了方便起见也导入numpy库

import pandas as pd
import numpy as np

导入数据源

df = pd.read_csv("pokemon_data.csv",encoding="gbk")

df

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
0	Bulbasaur	Grass	Poison	318	45	49	49	45	1
1	Ivysaur	Grass	Poison	405	60	62	63	60	1
2	Venusaur	Grass	Poison	525	80	82	83	80	1
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	80	1
4	Charmander	Fire	NaN	309	39	52	43	65	1
5	Charmeleon	Fire	NaN	405	58	64	58	80	1
6	Charizard	Fire	Flying	534	78	84	78	100	1
7	CharizardMega Charizard X	Fire	Dragon	634	78	130	111	100	1
8	CharizardMega Charizard Y	Fire	Flying	634	78	104	78	100	1
9	Squirtle	Water	NaN	314	44	48	65	43	1
10	Wartortle	Water	NaN	405	59	63	80	58	1
11	Blastoise	Water	NaN	530	79	83	100	78	1
12	BlastoiseMega Blastoise	Water	NaN	630	79	103	120	78	1
13	Caterpie	Bug	NaN	195	45	30	35	45	1
14	Metapod	Bug	NaN	205	50	20	55	30	1
15	Butterfree	Bug	Flying	395	60	45	50	70	1
16	Weedle	Bug	Poison	195	40	35	30	50	1
17	Kakuna	Bug	Poison	205	45	25	50	35	1
18	Beedrill	Bug	Poison	395	65	90	40	75	1
19	BeedrillMega Beedrill	Bug	Poison	495	65	150	40	145	1
20	Pidgey	Normal	Flying	251	40	45	40	56	1
21	Pidgeotto	Normal	Flying	349	63	60	55	71	1
22	Pidgeot	Normal	Flying	479	83	80	75	101	1
23	PidgeotMega Pidgeot	Normal	Flying	579	83	80	80	121	1
24	Rattata	Normal	NaN	253	30	56	35	72	1
25	Raticate	Normal	NaN	413	55	81	60	97	1
26	Spearow	Normal	Flying	262	40	60	30	70	1
27	Fearow	Normal	Flying	442	65	90	65	100	1
28	Ekans	Poison	NaN	288	35	60	44	55	1
29	Arbok	Poison	NaN	438	60	85	69	80	1
...	...	...	...	...	...	...	...	...	...
770	Sylveon	Fairy	NaN	525	95	65	65	60	6
771	Hawlucha	Fighting	Flying	500	78	92	75	118	6
772	Dedenne	Electric	Fairy	431	67	58	57	101	6
773	Carbink	Rock	Fairy	500	50	50	150	50	6
774	Goomy	Dragon	NaN	300	45	50	35	40	6
775	Sliggoo	Dragon	NaN	452	68	75	53	60	6
776	Goodra	Dragon	NaN	600	90	100	70	80	6
777	Klefki	Steel	Fairy	470	57	80	91	75	6
778	Phantump	Ghost	Grass	309	43	70	48	38	6
779	Trevenant	Ghost	Grass	474	85	110	76	56	6
780	PumpkabooAverage Size	Ghost	Grass	335	49	66	70	51	6
781	PumpkabooSmall Size	Ghost	Grass	335	44	66	70	56	6
782	PumpkabooLarge Size	Ghost	Grass	335	54	66	70	46	6
783	PumpkabooSuper Size	Ghost	Grass	335	59	66	70	41	6
784	GourgeistAverage Size	Ghost	Grass	494	65	90	122	84	6
785	GourgeistSmall Size	Ghost	Grass	494	55	85	122	99	6
786	GourgeistLarge Size	Ghost	Grass	494	75	95	122	69	6
787	GourgeistSuper Size	Ghost	Grass	494	85	100	122	54	6
788	Bergmite	Ice	NaN	304	55	69	85	28	6
789	Avalugg	Ice	NaN	514	95	117	184	28	6
790	Noibat	Flying	Dragon	245	40	30	35	55	6
791	Noivern	Flying	Dragon	535	85	70	80	123	6
792	Xerneas	Fairy	NaN	680	126	131	95	99	6
793	Yveltal	Dark	Flying	680	126	131	95	99	6
794	Zygarde50% Forme	Dragon	Ground	600	108	100	121	95	6
795	Diancie	Rock	Fairy	600	50	100	150	50	6
796	DiancieMega Diancie	Rock	Fairy	700	50	160	110	110	6
797	HoopaHoopa Confined	Psychic	Ghost	600	80	110	60	70	6
798	HoopaHoopa Unbound	Psychic	Dark	680	80	160	60	80	6
799	Volcanion	Fire	Water	600	80	110	120	70	6

800 rows × 9 columns

1.查看前5行和后5行

head()默认返回前面5行

df.head()

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
0	Bulbasaur	Grass	Poison	318	45	49	49	45	1
1	Ivysaur	Grass	Poison	405	60	62	63	60	1
2	Venusaur	Grass	Poison	525	80	82	83	80	1
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	80	1
4	Charmander	Fire	NaN	309	39	52	43	65	1

从头开始指定查看行数

df.head(20)

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
0	Bulbasaur	Grass	Poison	318	45	49	49	45	1
1	Ivysaur	Grass	Poison	405	60	62	63	60	1
2	Venusaur	Grass	Poison	525	80	82	83	80	1
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	80	1
4	Charmander	Fire	NaN	309	39	52	43	65	1
5	Charmeleon	Fire	NaN	405	58	64	58	80	1
6	Charizard	Fire	Flying	534	78	84	78	100	1
7	CharizardMega Charizard X	Fire	Dragon	634	78	130	111	100	1
8	CharizardMega Charizard Y	Fire	Flying	634	78	104	78	100	1
9	Squirtle	Water	NaN	314	44	48	65	43	1
10	Wartortle	Water	NaN	405	59	63	80	58	1
11	Blastoise	Water	NaN	530	79	83	100	78	1
12	BlastoiseMega Blastoise	Water	NaN	630	79	103	120	78	1
13	Caterpie	Bug	NaN	195	45	30	35	45	1
14	Metapod	Bug	NaN	205	50	20	55	30	1
15	Butterfree	Bug	Flying	395	60	45	50	70	1
16	Weedle	Bug	Poison	195	40	35	30	50	1
17	Kakuna	Bug	Poison	205	45	25	50	35	1
18	Beedrill	Bug	Poison	395	65	90	40	75	1
19	BeedrillMega Beedrill	Bug	Poison	495	65	150	40	145	1

tail()默认返回后面5行

df.tail()

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
795	Diancie	Rock	Fairy	600	50	100	150	50	6
796	DiancieMega Diancie	Rock	Fairy	700	50	160	110	110	6
797	HoopaHoopa Confined	Psychic	Ghost	600	80	110	60	70	6
798	HoopaHoopa Unbound	Psychic	Dark	680	80	160	60	80	6
799	Volcanion	Fire	Water	600	80	110	120	70	6

从尾查看指定查看行数

df.tail(20)

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
780	PumpkabooAverage Size	Ghost	Grass	335	49	66	70	51	6
781	PumpkabooSmall Size	Ghost	Grass	335	44	66	70	56	6
782	PumpkabooLarge Size	Ghost	Grass	335	54	66	70	46	6
783	PumpkabooSuper Size	Ghost	Grass	335	59	66	70	41	6
784	GourgeistAverage Size	Ghost	Grass	494	65	90	122	84	6
785	GourgeistSmall Size	Ghost	Grass	494	55	85	122	99	6
786	GourgeistLarge Size	Ghost	Grass	494	75	95	122	69	6
787	GourgeistSuper Size	Ghost	Grass	494	85	100	122	54	6
788	Bergmite	Ice	NaN	304	55	69	85	28	6
789	Avalugg	Ice	NaN	514	95	117	184	28	6
790	Noibat	Flying	Dragon	245	40	30	35	55	6
791	Noivern	Flying	Dragon	535	85	70	80	123	6
792	Xerneas	Fairy	NaN	680	126	131	95	99	6
793	Yveltal	Dark	Flying	680	126	131	95	99	6
794	Zygarde50% Forme	Dragon	Ground	600	108	100	121	95	6
795	Diancie	Rock	Fairy	600	50	100	150	50	6
796	DiancieMega Diancie	Rock	Fairy	700	50	160	110	110	6
797	HoopaHoopa Confined	Psychic	Ghost	600	80	110	60	70	6
798	HoopaHoopa Unbound	Psychic	Dark	680	80	160	60	80	6
799	Volcanion	Fire	Water	600	80	110	120	70	6

2.查看列名

df.columns

Index(['姓名', '类型1', '类型2', '总计', '生命值', '攻击力', '防御力', '速度', '时代'], dtype='object')

for i in df.columns:
    print(i)

姓名
类型1
类型2
总计
生命值
攻击力
防御力
速度
时代

3.查看索引

df.index

RangeIndex(start=0, stop=800, step=1)

[print(i) for i in df.index]

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4.查看行列数

df.shape

(800, 9)

5.查看数据类型

查看所有列的数据类型

df.dtypes

姓名     object
类型1    object
类型2    object
总计      int64
生命值     int64
攻击力     int64
防御力     int64
速度      int64
时代      int64
dtype: object

查看某列的数据类型

df["攻击力"].dtypes

dtype('int64')

6.查看空值

df.isnull().head()

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
0	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False
4	False	False	True	False	False	False	False	False	False

df[df["类型2"].isnull()]

	姓名	类型1	类型2	总计	生命值	攻击力	防御力	速度	时代
4	Charmander	Fire	NaN	309	39	52	43	65	1
5	Charmeleon	Fire	NaN	405	58	64	58	80	1
9	Squirtle	Water	NaN	314	44	48	65	43	1
10	Wartortle	Water	NaN	405	59	63	80	58	1
11	Blastoise	Water	NaN	530	79	83	100	78	1
12	BlastoiseMega Blastoise	Water	NaN	630	79	103	120	78	1
13	Caterpie	Bug	NaN	195	45	30	35	45	1
14	Metapod	Bug	NaN	205	50	20	55	30	1
24	Rattata	Normal	NaN	253	30	56	35	72	1
25	Raticate	Normal	NaN	413	55	81	60	97	1
28	Ekans	Poison	NaN	288	35	60	44	55	1
29	Arbok	Poison	NaN	438	60	85	69	80	1
30	Pikachu	Electric	NaN	320	35	55	40	90	1
31	Raichu	Electric	NaN	485	60	90	55	110	1
32	Sandshrew	Ground	NaN	300	50	75	85	40	1
33	Sandslash	Ground	NaN	450	75	100	110	65	1
34	Nidoran♀	Poison	NaN	275	55	47	52	41	1
35	Nidorina	Poison	NaN	365	70	62	67	56	1
37	Nidoran♂	Poison	NaN	273	46	57	40	50	1
38	Nidorino	Poison	NaN	365	61	72	57	65	1
40	Clefairy	Fairy	NaN	323	70	45	48	35	1
41	Clefable	Fairy	NaN	483	95	70	73	60	1
42	Vulpix	Fire	NaN	299	38	41	40	65	1
43	Ninetales	Fire	NaN	505	73	76	75	100	1
55	Diglett	Ground	NaN	265	10	55	25	95	1
56	Dugtrio	Ground	NaN	405	35	80	50	120	1
57	Meowth	Normal	NaN	290	40	45	35	90	1
58	Persian	Normal	NaN	440	65	70	60	115	1
59	Psyduck	Water	NaN	320	50	52	48	55	1
60	Golduck	Water	NaN	500	80	82	78	85	1
...	...	...	...	...	...	...	...	...	...
721	Fennekin	Fire	NaN	307	40	45	40	60	6
722	Braixen	Fire	NaN	409	59	59	58	73	6
724	Froakie	Water	NaN	314	41	56	40	71	6
725	Frogadier	Water	NaN	405	54	63	52	97	6
727	Bunnelby	Normal	NaN	237	38	36	38	57	6
732	Scatterbug	Bug	NaN	200	38	35	40	35	6
733	Spewpa	Bug	NaN	213	45	22	60	29	6
737	Flabébé	Fairy	NaN	303	44	38	39	42	6
738	Floette	Fairy	NaN	371	54	45	47	52	6
739	Florges	Fairy	NaN	552	78	65	68	75	6
740	Skiddo	Grass	NaN	350	66	65	48	52	6
741	Gogoat	Grass	NaN	531	123	100	62	68	6
742	Pancham	Fighting	NaN	348	67	82	62	43	6
744	Furfrou	Normal	NaN	472	75	80	60	102	6
745	Espurr	Psychic	NaN	355	62	48	54	68	6
746	MeowsticMale	Psychic	NaN	466	74	48	76	104	6
747	MeowsticFemale	Psychic	NaN	466	74	48	76	104	6
752	Spritzee	Fairy	NaN	341	78	52	60	23	6
753	Aromatisse	Fairy	NaN	462	101	72	72	29	6
754	Swirlix	Fairy	NaN	341	62	48	66	49	6
755	Slurpuff	Fairy	NaN	480	82	80	86	72	6
762	Clauncher	Water	NaN	330	50	53	62	44	6
763	Clawitzer	Water	NaN	500	71	73	88	59	6
770	Sylveon	Fairy	NaN	525	95	65	65	60	6
774	Goomy	Dragon	NaN	300	45	50	35	40	6
775	Sliggoo	Dragon	NaN	452	68	75	53	60	6
776	Goodra	Dragon	NaN	600	90	100	70	80	6
788	Bergmite	Ice	NaN	304	55	69	85	28	6
789	Avalugg	Ice	NaN	514	95	117	184	28	6
792	Xerneas	Fairy	NaN	680	126	131	95	99	6

386 rows × 9 columns

7.查看数据表信息

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 800 entries, 0 to 799
Data columns (total 9 columns):
姓名     800 non-null object
类型1    800 non-null object
类型2    414 non-null object
总计     800 non-null int64
生命值    800 non-null int64
攻击力    800 non-null int64
防御力    800 non-null int64
速度     800 non-null int64
时代     800 non-null int64
dtypes: int64(6), object(3)
memory usage: 56.3+ KB

8.查看唯一值

df["类型1"].unique()

array(['Grass', 'Fire', 'Water', 'Bug', 'Normal', 'Poison', 'Electric',
       'Ground', 'Fairy', 'Fighting', 'Psychic', 'Rock', 'Ghost', 'Ice',
       'Dragon', 'Dark', 'Steel', 'Flying'], dtype=object)

所有唯一值的数量

len(df["类型1"].unique())

每个值出现的次数（频次）

df["类型1"].value_counts()

Water       112
Normal       98
Grass        70
Bug          69
Psychic      57
Fire         52
Rock         44
Electric     44
Ghost        32
Dragon       32
Ground       32
Dark         31
Poison       28
Fighting     27
Steel        27
Ice          24
Fairy        17
Flying        4
Name: 类型1, dtype: int64

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