pandas concat ignore

本文介绍了 pandas concat ignore_index不起作用的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在尝试对数据框进行列绑定，并遇到熊猫concat的问题，因为ignore_index=True似乎不起作用:

I am trying to column-bind dataframes and having issue with pandas concat, as ignore_index=True doesn't seem to work:

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3'], 'D': ['D0', 'D1', 'D2', 'D3']}, index=[0, 2, 3,4]) df2 = pd.DataFrame({'A1': ['A4', 'A5', 'A6', 'A7'], 'C': ['C4', 'C5', 'C6', 'C7'], 'D2': ['D4', 'D5', 'D6', 'D7']}, index=[ 5, 6, 7,3]) df1 # A B D # 0 A0 B0 D0 # 2 A1 B1 D1 # 3 A2 B2 D2 # 4 A3 B3 D3 df2 # A1 C D2 # 5 A4 C4 D4 # 6 A5 C5 D5 # 7 A6 C6 D6 # 3 A7 C7 D7 dfs = [df1,df2] df = pd.concat( dfs,axis=1,ignore_index=True) print df

结果是

0 1 2 3 4 5 0 A0 B0 D0 NaN NaN NaN 2 A1 B1 D1 NaN NaN NaN 3 A2 B2 D2 A7 C7 D7 4 A3 B3 D3 NaN NaN NaN 5 NaN NaN NaN A4 C4 D4 6 NaN NaN NaN A5 C5 D5 7 NaN NaN NaN A6 C6 D6

即使我使用重置索引

df1.reset_index() df2.reset_index()

然后尝试

pd.concat([df1,df2],axis=1)

它仍然产生相同的结果！

it still produces the same result!

推荐答案

如果我正确理解了您的意思，这就是您想要做的.

If I understood you correctly, this is what you would like to do.

import pandas as pd df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3'], 'D': ['D0', 'D1', 'D2', 'D3']}, index=[0, 2, 3,4]) df2 = pd.DataFrame({'A1': ['A4', 'A5', 'A6', 'A7'], 'C': ['C4', 'C5', 'C6', 'C7'], 'D2': ['D4', 'D5', 'D6', 'D7']}, index=[ 4, 5, 6 ,7]) df1.reset_index(drop=True, inplace=True) df2.reset_index(drop=True, inplace=True) df = pd.concat( [df1, df2], axis=1)

哪个给:

A B D A1 C D2 0 A0 B0 D0 A4 C4 D4 1 A1 B1 D1 A5 C5 D5 2 A2 B2 D2 A6 C6 D6 3 A3 B3 D3 A7 C7 D7

实际上，我希望df = pd.concat(dfs,axis=1,ignore_index=True)会得到相同的结果.

Actually, I would have expected that df = pd.concat(dfs,axis=1,ignore_index=True) gives the same result.

这是 jreback 的出色解释:

ignore_index=True忽略"，表示未在连接轴上对齐.只需按照传递顺序将它们粘贴在一起，然后为实际索引重新分配一个范围(例如range(len(index))) 因此，联接非重叠索引(在示例中假设为axis=1)之间的区别在于，使用ignore_index=False(默认值)，您可以得到索引的连接，而使用ignore_index=True，您可以得到范围. /p>

ignore_index=True ‘ignores’, meaning doesn’t align on the joining axis. it simply pastes them together in the order that they are passed, then reassigns a range for the actual index (e.g. range(len(index))) so the difference between joining on non-overlapping indexes (assume axis=1 in the example), is that with ignore_index=False (the default), you get the concat of the indexes, and with ignore_index=True you get a range.