熊猫组合数据框答案

【问题标题】：pandas combining dataframe熊猫组合数据框
【发布时间】：2016-10-03 22:36:05
【问题描述】：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle

java = pickle.load(open('JavaSafe.p','rb')) ##import 2d array
python = pickle.load(open('PythonSafe.p','rb')) ##import 2d array

javaFrame = pd.DataFrame(java,columns=['Town','Java Jobs'])
pythonFrame = pd.DataFrame(python,columns=['Town','Python Jobs'])
javaFrame = javaFrame.sort_values(by='Java Jobs',ascending=False)
pythonFrame = pythonFrame.sort_values(by='Python Jobs',ascending=False)
print(javaFrame,"\n",pythonFrame)

此代码带有以下内容：

                Town  Java Jobs
435          York,NY       3593
212       NewYork,NY       3585
584       Seattle,WA       2080
624       Chicago,IL       1920
301        Boston,MA       1571
...
79        Holland,MI          5
38      Manhattan,KS          5
497        Vernon,IL          5
30        Clayton,MO          5
90       Waukegan,IL          5

[653 rows x 2 columns] 

                 Town  Python Jobs
160       NewYork,NY         2949
11           York,NY         2938
349       Seattle,WA         1321
91        Chicago,IL         1312
167        Boston,MA         1117

383       Hanover,NH            5
209      Bulverde,TX            5
203     Salisbury,NC            5
67       Rockford,IL            5
256       Ventura,CA            5

[416 rows x 2 columns]

我想创建一个新的数据框，它使用城镇名称作为索引，并且每个 java 和 python 都有一个列。但是，有些城镇只会提供其中一种语言的结果。

【问题讨论】：

你也可以给你的原始代码result = pd.merge(pythonFrame, javeFrame, on='Town', how='outer').set_index('Town')

标签： python pandas

【解决方案1】：

import pandas as pd

javaFrame = pd.DataFrame({'Java Jobs': [3593, 3585, 2080, 1920, 1571, 5, 5, 5, 5, 5],
     'Town': ['York,NY', 'NewYork,NY', 'Seattle,WA', 'Chicago,IL', 'Boston,MA', 'Holland,MI', 'Manhattan,KS', 'Vernon,IL', 'Clayton,MO', 'Waukegan,IL']}, index=[435, 212, 584, 624, 301, 79, 38, 497, 30, 90])
pythonFrame = pd.DataFrame({'Python Jobs': [2949, 2938, 1321, 1312, 1117, 5, 5, 5, 5, 5],
     'Town': ['NewYork,NY', 'York,NY', 'Seattle,WA', 'Chicago,IL', 'Boston,MA', 'Hanover,NH', 'Bulverde,TX', 'Salisbury,NC', 'Rockford,IL', 'Ventura,CA']}, index=[160, 11, 349, 91, 167, 383, 209, 203, 67, 256])

result = pd.merge(javaFrame, pythonFrame, how='outer').set_index('Town')
#               Java Jobs  Python Jobs
# Town                                
# York,NY          3593.0       2938.0
# NewYork,NY       3585.0       2949.0
# Seattle,WA       2080.0       1321.0
# Chicago,IL       1920.0       1312.0
# Boston,MA        1571.0       1117.0
# Holland,MI          5.0          NaN
# Manhattan,KS        5.0          NaN
# Vernon,IL           5.0          NaN
# Clayton,MO          5.0          NaN
# Waukegan,IL         5.0          NaN
# Hanover,NH          NaN          5.0
# Bulverde,TX         NaN          5.0
# Salisbury,NC        NaN          5.0
# Rockford,IL         NaN          5.0
# Ventura,CA          NaN          5.0

pd.merge 默认情况下会在所有共享的列上连接两个 DataFrame。在这种情况下，javaFrame 和 pythonFrame 仅共享 Town 列。所以默认情况下pd.merge 将加入Town 列上的两个DataFrame。

how='outer 导致pd.merge 使用union of the keys from both frames。换句话说，它会导致pd.merge 返回其数据来自javaFrame 或pythonFrame 的行，即使只有一个DataFrame 包含Town。缺失的数据用NaNs 填充。

【讨论】：

result = pd.merge(javaFrame, pythonFrame, how='outer').set_index('Town') 是，我认为，他们所期待的！

【解决方案2】：

使用pd.concat

df = pd.concat([df.set_index('Town') for df in [javaFrame, pythonFrame]], axis=1)

              Java Jobs  Python Jobs
Boston,MA        1571.0       1117.0
Bulverde,TX         NaN          5.0
Chicago,IL       1920.0       1312.0
Clayton,MO          5.0          NaN
Hanover,NH          NaN          5.0
Holland,MI          5.0          NaN
Manhattan,KS        5.0          NaN
NewYork,NY       3585.0       2949.0
Rockford,IL         NaN          5.0
Salisbury,NC        NaN          5.0
Seattle,WA       2080.0       1321.0
Ventura,CA          NaN          5.0
Vernon,IL           5.0          NaN
Waukegan,IL         5.0          NaN
York,NY          3593.0       2938.0

【讨论】：