美國各州人口數據分析

案例分析：美國各州人口數據分析

- 需求：
    - 導入文件，查看原始數據
    - 將人口數據和各州簡稱數據進行合併
    - 將合併的數據中重複的abbreviation列進行刪除
    - 查看存在缺失數據的列
    - 找到有哪些state/region使得state的值爲NaN，進行去重操做
    - 爲找到的這些state/region的state項補上正確的值，從而去除掉state這一列的全部NaN
    - 合併各州面積數據areas
    - 咱們會發現area(sq.mi)這一列有缺失數據，找出是哪些行
    - 去除含有缺失數據的行
    - 找出2010年的全民人口數據
    - 計算各州的人口密度
    - 排序，並找出人口密度最高的五個州   df.sort_values()

# 1.導入文件，查看原始數據

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

abb = pd.read_csv('./data/state-abbrevs.csv')
pop = pd.read_csv('./data/state-population.csv')
area = pd.read_csv('./data/state-areas.csv')

# 查看的數據
abb.head(1)

    state   abbreviation
0   Alabama     AL


pop.head(1)
    state/region    ages    year    population
0   AL            under18   2012    1117489.0

# 2 將人口數據和各州簡稱數據進行合併
abb_pop = pd.merge(abb,pop,left_on='abbreviation',right_on='state/region',how='outer')
abb_pop.head(3)

        state   abbreviation    state/region    ages     year    population
0       Alabama     AL              AL        under18   2012    1117489.0
1       Alabama     AL              AL        total     2012    4817528.0
2       Alabama     AL              AL         under18   2010    1130966.0

# 3 將合併的數據中重複的abbreviation列進行刪除

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

# 4 查看存在缺失數據的列

abb_pop.isnull().any(axis=0)

state            True
state/region    False
ages            False
year            False
population       True
dtype: bool

# 5 找到有哪些state/region使得state的值爲NaN，進行去重操做
#    找到哪些簡稱 的全稱爲空  (就是先找到state中的空值 ，經過state在找到state/region)    
#    把簡稱找到之後 進行去重
#    找全稱爲空,用該數據找到簡稱，而後去重

abb_pop.head(5)
    state   state/region      ages       year    population
0   Alabama     AL          under18     2012    1117489.0
1   Alabama     AL          total       2012    4817528.0
2   Alabama     AL          under18     2010    1130966.0
3   Alabama     AL          total       2010    4785570.0
4   Alabama     AL          under18     2011    1125763.0


# 5.1.找出state中的空值

abb_pop['state'].isnull()


# 5.2.將布爾值做爲元數據的行索引：定位到全部state爲空對應的行數據

abb_pop.loc[abb_pop['state'].isnull()]


# 5.3.將空對應的行數據中的簡稱這一列的數據取出進行去重操做

abb_pop.loc[abb_pop['state'].isnull()]['state/region'].unique()
# array([], dtype=object)

# 6 爲找到的這些state/region的state項補上正確的值，從而去除掉state這一列的全部NaN


# 6.1.找出USA對應state列中的空值
# 返回的是bool值
abb_pop['state/region'] == 'USA'


# 6.2.取出USA對應的行數據
abb_pop.loc[abb_pop['state/region'] == 'USA']
indexs = abb_pop.loc[abb_pop['state/region'] == 'USA'].index
indexs
Int64Index([2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506,
            2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517,
            2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528,
            2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539,
            2540, 2541, 2542, 2543],
           dtype='int64')


# 6.3.將USA對應的空值覆蓋成對應的值
abb_pop.loc[indexs,'state'] = 'United States'


# 6.4 找到PR所對應的行數據
abb_pop['state/region'] == 'PR'
abb_pop.loc[abb_pop['state/region'] == 'PR']
indexs = abb_pop.loc[abb_pop['state/region'] == 'PR'].index
abb_pop.loc[indexs,'state'] = 'ppprrr'


area.head()

    state    area (sq. mi)
0   Alabama     52423
1   Alaska      656425
2   Arizona     114006
3   Arkansas    53182
4   California  163707

# 7 合併各州面積數據areas
abb_pop_area = pd.merge(abb_pop,area,how='outer')
abb_pop_area.head()


    state   state/region    ages    year    population  area (sq. mi)
0   Alabama     AL        under18   2012.0  1117489.0   52423.0
1   Alabama     AL        total     2012.0  4817528.0   52423.0
2   Alabama     AL        under18   2010.0  1130966.0   52423.0
3   Alabama     AL        total     2010.0  4785570.0   52423.0
4   Alabama     AL        under18   2011.0  1125763.0   52423.0

# 8 咱們會發現area(sq.mi)這一列有缺失數據，找出是哪些行
# 9 去除含有缺失數據的行
abb_pop_area['area (sq. mi)'].isnull()
abb_pop_area.loc[abb_pop_area['area (sq. mi)'].isnull()]

# 獲取行索引
indexs = abb_pop_area.loc[abb_pop_area['area (sq. mi)'].isnull()].index

abb_pop_area.drop(labels=indexs,axis=0,inplace=True)

# 10 找出2010年的全民人口數據
# query 作條件查詢
df_2010 = abb_pop_area.query('year == 2010 & ages == "total"')
df_2010

# 11 計算各州的人口密度
abb_pop_area['midu'] = abb_pop_area['population'] / abb_pop_area['area (sq. mi)']
abb_pop_area.head(1)


    state   state/region    ages    year    population  area (sq. mi)     midu
0   Alabama     AL        under18   2012.0  1117489.0   52423.0         21.316769

# 12 排序，並找出人口密度最高的五個州   df.sort_values()
abb_pop_area.sort_values(by='midu',axis=0,ascending=False)