如何在 python / pandas 中进行左内连接？ [复制]

Question

我想根据另一个数据帧中的数据从一个数据帧中删除数据。 我找到了一种方法来做到这一点（见下文），但我想知道是否有更有效的方法来做到这一点。 这是我要改进的代码：

# -*- coding: utf-8 -*-

import pandas as pd

#df1 is the dataframe where I want to remove data from
d1 = {'one' : [1., 2., 3., 4.], 'two' : [4., 3., 2., 1.], 'three' : [5.,6.,7.,8.] }
df1 = pd.DataFrame(d1)
df1.columns = ['one', 'two', 'three'] #Keeping the order of the columns as defined
print 'df1\n', df1
#print df1

#I want to remove all the rows from df1 that are also in df2
d2 = {'one' : [2., 4.], 'two' : [3., 1], 'three' : [6.,8.] }
df2 = pd.DataFrame(d2)
df2.columns = ['one', 'two', 'three'] #Keeping the order of the columns as defined
print 'df2\n', df2


#df3 is the output I want to get: it should have the same data as df1, but without the data that is in df2
df3 = df1

#Create some keys to help identify rows to be dropped from df1
df1['key'] = df1['one'].astype(str)+'-'+df1['two'].astype(str)+'-'+df1['three'].astype(str)
print 'df1 with key\n', df1
df2['key'] = df2['one'].astype(str)+'-'+df2['two'].astype(str)+'-'+df2['three'].astype(str)
print 'df2 with key\n', df2

#List of rows to remove from df1
rowsToDrop = []

#Building the list of rows to drop
for i in df1.index:
    if df1['key'].irow(i) in df2.ix[:,'key'].values:
        rowsToDrop.append(i)

#Dropping rows from df1 that are also in df2
for j in reversed(rowsToDrop):
    df3 = df3.drop(df3.index[j])   

df3.drop(['key'], axis=1, inplace=True)        

#Voilà!
print 'df3\n', df3

感谢您的帮助。

Answer 1

这将使用数据框 df1 和 dict d2 工作

df3 = df1[~df1.isin(d2)].dropna()

您可以将 df 传递给 isin() 但我认为它不会为您提供所需的结果，因为我相信它也会查看索引。

http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.isin.html

Answer 2

您正在寻找更多用于选择行而不是连接数据框的语法。'

真正的左连接应该是这样的：

import numpy as np
import pandas as pd

d1 = {'one' : [1., 2., 3., 4.], 'two' : [4., 3., 2., 1.], 'three' : [5.,6.,7.,8.] }
df1 = pd.DataFrame(d1)
df1['key'] = df1['one'].astype(str)+'-'+df1['two'].astype(str)+'-'+df1['three'].astype(str)
df1.set_index('key', inplace=True)


d2 = {'one' : [2., 4.], 'two' : [3., 1], 'three' : [6.,8.] }
df2 = pd.DataFrame(d2)
df2['key'] = df2['one'].astype(str)+'-'+df2['two'].astype(str)+'-'+df2['three'].astype(str)
df2.set_index('key', inplace=True)

df1.join(df2, how='left', lsuffix='_df1', rsuffix='_df2')


             one_df1  three_df1  two_df1  one_df2  three_df2  two_df2
key                                                                  
1.0-4.0-5.0        1          5        4      NaN        NaN      NaN
2.0-3.0-6.0        2          6        3        2          6        3
3.0-2.0-7.0        3          7        2      NaN        NaN      NaN
4.0-1.0-8.0        4          8        1        4          8        1

进行右连接：

df1.join(df2, how='right', lsuffix='_df1', rsuffix='_df2')

产生这个：

             one_df1  three_df1  two_df1  one_df2  three_df2  two_df2
key                                                                  
2.0-3.0-6.0        2          6        3        2          6        3
4.0-1.0-8.0        4          8        1        4          8        1