數據私信我獲取spa
1,導入文件,查看原始數據3d
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')
2,將人口數據和各州簡稱數據進行合併blog
display(abb.head(1),pop.head(1)) abb_pop = pd.merge(abb,pop,left_on='abbreviation',right_on='state/region',how='outer') abb_pop.head()
3,將合併的數據中重複的abbreviation列進行刪除排序
abb_pop.drop(labels='abbreviation',axis=1,inplace=True) abb_pop.head()
4,查看存在缺失數據的列pandas
abb_pop.isnull().any(axis=0)
5,找到有哪些state/region使得state的值爲NaN,進行去重操做 it
#1.檢測state列中的空值 abb_pop['state'].isnull() #2.將1的返回值做用的state_region這一列中 abb_pop['state/region'][abb_pop['state'].isnull()] #3.去重 abb_pop['state/region'][abb_pop['state'].isnull()].unique()
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 io
abb_pop['state/region'] == 'USA' indexs = abb_pop['state'][abb_pop['state/region'] == 'USA'].index abb_pop.loc[indexs,'state'] = 'United State' pr_index = abb_pop['state'][abb_pop['state/region'] == 'PR'].index abb_pop.loc[pr_index,'state'] = 'PPPRRR'
7,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()class
#合併各州面積數據areas abb_pop_area = pd.merge(abb_pop,area,how='outer') abb_pop_area.head()
#咱們會發現area(sq.mi)這一列有缺失數據,找出是哪些行 abb_pop_area['area (sq. mi)'].isnull() a_index = abb_pop_area.loc[abb_pop_area['area (sq. mi)'].isnull()].index #去除含有缺失數據的行 abb_pop_area.drop(labels=a_index,axis=0,inplace=True) #找出2010年的全民人口數據 abb_pop_area.query('year == 2010 & ages == "total"') #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()
8,排序,並找出人口密度最高的五個州 df.sort_values()
import
abb_pop_area.sort_values(by='midu',axis=0,ascending=False).head()