这是join方法的理想情况

join方法正是为这些类型的情况构建的。你可以加入任意数量的数据框架。调用的DataFrame与传递的DataFrame集合的索引连接。要使用多个dataframe，必须将连接列放在索引中。

代码看起来像这样:

filenames = ['fn1', 'fn2', 'fn3', 'fn4',....]
dfs = [pd.read_csv(filename, index_col=index_col) for filename in filenames)]
dfs[0].join(dfs[1:])

使用@zero的数据，你可以这样做:

df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12'])
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22'])
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32'])

dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
dfs[0].join(dfs[1:])

     attr11 attr12 attr21 attr22 attr31 attr32
name                                          
a         5      9      5     19     15     49
b         4     61     14     16      4     36
c        24      9      4      9     14      9

0的答案基本上是一个约简运算。如果我有很多数据框架，我会把它们放在一个这样的列表中(通过列表推导或循环或诸如此类的东西生成):

dfs = [df0, df1, df2, ..., dfN]

假设他们有一个共同的列，就像你的例子中的name一样，我会做以下事情:

import functools as ft
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name'), dfs)

这样，您的代码就可以处理您想合并的任何数量的数据框架。

在python 3.6.3和pandas 0.22.0中，你也可以使用concat，只要你将你想要用于连接的列设置为index:

pd.concat(
    objs=(iDF.set_index('name') for iDF in (df1, df2, df3)),
    axis=1, 
    join='inner'
).reset_index()

其中df1, df2和df3定义为John Galt的答案:

import pandas as pd
df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12']
)
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['b', 14, 16],
    ['c', 4, 9]]),
    columns=['name', 'attr21', 'attr22']
)
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['b', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr31', 'attr32']
)

简单的解决方案:

如果列名相似:

 df1.merge(df2,on='col_name').merge(df3,on='col_name')

如果列名不同:

df1.merge(df2,left_on='col_name1', right_on='col_name2').merge(df3,left_on='col_name1', right_on='col_name3').drop(columns=['col_name2', 'col_name3']).rename(columns={'col_name1':'col_name'})

df1 = pd.DataFrame(np.array([
    ['a', 5, 9],
    ['b', 4, 61],
    ['c', 24, 9]]),
    columns=['name', 'attr11', 'attr12']
)
df2 = pd.DataFrame(np.array([
    ['a', 5, 19],
    ['d', 14, 16]]

),
    columns=['name', 'attr21', 'attr22']
)
df3 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['c', 4, 36],
    ['d', 14, 9]]),
    columns=['name', 'attr31', 'attr32']
)
df4 = pd.DataFrame(np.array([
    ['a', 15, 49],
    ['c', 4, 36],
    ['c', 14, 9]]),
    columns=['name', 'attr41', 'attr42']
)

加入列表数据框架的三种方法

pandas.concat

dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
# cant not run if index not unique 
dfs = pd.concat(dfs, join='outer', axis = 1) 

functools.reduce

dfs = [df1, df2, df3, df4]
# still run with index not unique 
import functools as ft
df_final = ft.reduce(lambda left, right: pd.merge(left, right, on='name', how = 'outer'), dfs)

加入

# cant not run if index not unique 
dfs = [df1, df2, df3]
dfs = [df.set_index('name') for df in dfs]
dfs[0].join(dfs[1:], how = 'outer')

不需要多索引来执行连接操作。 我们只需要正确地设置索引列来执行连接操作(例如哪个命令df.set_index('Name'))

默认情况下，连接操作在索引上执行。 在本例中，只需指定Name列对应于索引。 下面是一个例子

教程可能会有用。

# Simple example where dataframes index are the name on which to perform
# the join operations
import pandas as pd
import numpy as np
name = ['Sophia' ,'Emma' ,'Isabella' ,'Olivia' ,'Ava' ,'Emily' ,'Abigail' ,'Mia']
df1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=name)
df2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'],         index=name)
df3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'],     index=name)
df = df1.join(df2)
df = df.join(df3)

# If you have a 'Name' column that is not the index of your dataframe,
# one can set this column to be the index
# 1) Create a column 'Name' based on the previous index
df1['Name'] = df1.index
# 1) Select the index from column 'Name'
df1 = df1.set_index('Name')

# If indexes are different, one may have to play with parameter how
gf1 = pd.DataFrame(np.random.randn(8, 3), columns=['A','B','C'], index=range(8))
gf2 = pd.DataFrame(np.random.randn(8, 1), columns=['D'], index=range(2,10))
gf3 = pd.DataFrame(np.random.randn(8, 2), columns=['E','F'], index=range(4,12))

gf = gf1.join(gf2, how='outer')
gf = gf.join(gf3, how='outer')