为什么`pandas.read_csv`不是`pandas.DataFrame.to_csv`的倒数？ [英] Why is `pandas.read_csv` not the reciprocal of `pandas.DataFrame.to_csv`?

问题描述

对于我来说，对于 pandas.read_csv 并不是直接相互作用的函数，对于 df.to_csv 来说似乎很奇怪。在本例中，请注意如何使用所有默认设置，原始和最终的DataFrames由未命名列不同。

It seems strange to me that pandas.read_csv is not a direct reciprocal function to df.to_csv. In this illustration, notice how when using all the default settings the original and final DataFrames differ by the "Unnamed" column.

In [1]: import pandas as pd

In [2]: orig_df = pd.DataFrame({'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]}); orig_df
Out[2]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

[4 rows x 3 columns]

In [3]: orig_df.to_csv('test.csv')

In [4]: final_df = pd.read_csv('test.csv'); final_df
Out[4]: 
   Unnamed: 0  AAA  BBB  CCC
0           0    4   10  100
1           1    5   20   50
2           2    6   30  -30
3           3    7   40  -50

[4 rows x 4 columns]

似乎默认的 read_csv 应该是

In [6]: final2_df = pd.read_csv('test.csv', index_col=0); final2_df
Out[7]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

[4 rows x 3 columns]

或默认 to_csv 应该是

In [8]: df.to_csv('test2.csv', index=False)

读取时给出

In [9]: pd.read_csv('test2.csv')
Out[9]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

[4行x 3列]

（也许这应该发送给开发人员，但我真的很感兴趣为什么这样这是默认的行为，希望它也可以帮助别人避免我的困惑）。

(Perhaps this should instead be sent to the developer/s but I am genuinely interested why this is the default behavior. Hopefully it also can help someone else avoid the confusion I had).

推荐答案

感谢提示发贴 github 页面@EdChum。这导致我进入 pandas.DataFrame.from_csv 函数，它确实是 pandas.DataFrame.to_csv 的倒数。

Thanks for the tip to post to the github page @EdChum. This led me to the pandas.DataFrame.from_csv function which is indeed the reciprocal of pandas.DataFrame.to_csv.

In [6]: final_df = pd.DataFrame.from_csv('test.csv')

In [7]: final_df
Out[7]: 
   AAA  BBB  CCC
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50

[4 rows x 3 columns]

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