pandas 美国人口案例分析

import numpy as np
import pandas as pd
from pandas import DataFrame,Series

areas = pd.read_csv('./state-areas.csv')
areas

	state	area (sq. mi)
0	Alabama	52423
1	Alaska	656425
2	Arizona	114006
3	Arkansas	53182
4	California	163707
5	Colorado	104100
6	Connecticut	5544
7	Delaware	1954
8	Florida	65758
9	Georgia	59441
10	Hawaii	10932
11	Idaho	83574
12	Illinois	57918
13	Indiana	36420
14	Iowa	56276
15	Kansas	82282
16	Kentucky	40411
17	Louisiana	51843
18	Maine	35387
19	Maryland	12407
20	Massachusetts	10555
21	Michigan	96810
22	Minnesota	86943
23	Mississippi	48434
24	Missouri	69709
25	Montana	147046
26	Nebraska	77358
27	Nevada	110567
28	New Hampshire	9351
29	New Jersey	8722
30	New Mexico	121593
31	New York	54475
32	North Carolina	53821
33	North Dakota	70704
34	Ohio	44828
35	Oklahoma	69903
36	Oregon	98386
37	Pennsylvania	46058
38	Rhode Island	1545
39	South Carolina	32007
40	South Dakota	77121
41	Tennessee	42146
42	Texas	268601
43	Utah	84904
44	Vermont	9615
45	Virginia	42769
46	Washington	71303
47	West Virginia	24231
48	Wisconsin	65503
49	Wyoming	97818
50	District of Columbia	68
51	Puerto Rico	3515

ab = pd.read_csv('./state-abbrevs.csv')
ab

	state	abbreviation
0	Alabama	AL
1	Alaska	AK
2	Arizona	AZ
3	Arkansas	AR
4	California	CA
5	Colorado	CO
6	Connecticut	CT
7	Delaware	DE
8	District of Columbia	DC
9	Florida	FL
10	Georgia	GA
11	Hawaii	HI
12	Idaho	ID
13	Illinois	IL
14	Indiana	IN
15	Iowa	IA
16	Kansas	KS
17	Kentucky	KY
18	Louisiana	LA
19	Maine	ME
20	Montana	MT
21	Nebraska	NE
22	Nevada	NV
23	New Hampshire	NH
24	New Jersey	NJ
25	New Mexico	NM
26	New York	NY
27	North Carolina	NC
28	North Dakota	ND
29	Ohio	OH
30	Oklahoma	OK
31	Oregon	OR
32	Maryland	MD
33	Massachusetts	MA
34	Michigan	MI
35	Minnesota	MN
36	Mississippi	MS
37	Missouri	MO
38	Pennsylvania	PA
39	Rhode Island	RI
40	South Carolina	SC
41	South Dakota	SD
42	Tennessee	TN
43	Texas	TX
44	Utah	UT
45	Vermont	VT
46	Virginia	VA
47	Washington	WA
48	West Virginia	WV
49	Wisconsin	WI
50	Wyoming	WY

pop = pd.read_csv('./state-population.csv')
pop

	state/region	ages	year	population
0	AL	under18	2012	1117489.0
1	AL	total	2012	4817528.0
2	AL	under18	2010	1130966.0
3	AL	total	2010	4785570.0
4	AL	under18	2011	1125763.0
...	...	...	...	...
2539	USA	total	2010	309326295.0
2540	USA	under18	2011	73902222.0
2541	USA	total	2011	311582564.0
2542	USA	under18	2012	73708179.0
2543	USA	total	2012	313873685.0

2544 rows × 4 columns

pop.head()

	state/region	ages	year	population
0	AL	under18	2012	1117489.0
1	AL	total	2012	4817528.0
2	AL	under18	2010	1130966.0
3	AL	total	2010	4785570.0
4	AL	under18	2011	1125763.0

pop.shape

(2544, 4)

将地名全称与人口相对应

n1 = pd.merge(pop,ab,left_on='state/region',right_on='abbreviation',how = 'outer')
n1.head()

	state/region	ages	year	population	state	abbreviation
0	AL	under18	2012	1117489.0	Alabama	AL
1	AL	total	2012	4817528.0	Alabama	AL
2	AL	under18	2010	1130966.0	Alabama	AL
3	AL	total	2010	4785570.0	Alabama	AL
4	AL	under18	2011	1125763.0	Alabama	AL

n1.shape

(2544, 6)

n1.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2544 entries, 0 to 2543
Data columns (total 6 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   state/region  2544 non-null   object 
 1   ages          2544 non-null   object 
 2   year          2544 non-null   int64  
 3   population    2524 non-null   float64
 4   state         2448 non-null   object 
 5   abbreviation  2448 non-null   object 
dtypes: float64(1), int64(1), object(4)
memory usage: 139.1+ KB

n1.drop(labels = 'abbreviation',axis = 1,inplace=True)

n1.head()

	state/region	ages	year	population	state
0	AL	under18	2012	1117489.0	Alabama
1	AL	total	2012	4817528.0	Alabama
2	AL	under18	2010	1130966.0	Alabama
3	AL	total	2010	4785570.0	Alabama
4	AL	under18	2011	1125763.0	Alabama

查找空数据并填充

n1.info()