在 Pandas 中使用 read_csv 时精度丢失 [英] Precision lost while using read_csv in pandas

问题描述

我在文本文件中有以下格式的文件，我试图将其读入熊猫数据帧.

I have files of the below format in a text file which I am trying to read into a pandas dataframe.

895|2015-4-23|19|10000|LA|0.4677978806|0.4773469340|0.4089938425|0.8224291972|0.8652525793|0.6829942860|0.5139162227|

如您所见，输入文件中浮点数后有 10 个整数.

As you can see there are 10 integers after the floating point in the input file.

df = pd.read_csv('mockup.txt',header=None,delimiter='|')

当我尝试将它读入数据帧时，我没有得到最后 4 个整数

When I try to read it into dataframe, I am not getting the last 4 integers

df[5].head()

0    0.467798
1    0.258165
2    0.860384
3    0.803388
4    0.249820
Name: 5, dtype: float64

如何获得输入文件中的完整精度?我有一些需要执行的矩阵运算，所以我不能将它转换为字符串.

How can I get the complete precision as present in the input file? I have some matrix operations that needs to be performed so i cannot cast it as string.

我发现我必须对 dtype 做一些事情，但我不确定应该在哪里使用它.

I figured out that I have to do something about dtype but I am not sure where I should use it.

推荐答案

只是显示问题，见文档:

#temporaly set display precision
with pd.option_context('display.precision', 10):
    print df

     0          1   2      3   4             5            6             7   
0  895  2015-4-23  19  10000  LA  0.4677978806  0.477346934  0.4089938425   

             8             9            10            11  12  
0  0.8224291972  0.8652525793  0.682994286  0.5139162227 NaN    

(谢谢马克·狄金森):

Pandas 使用专用的十进制到二进制转换器，为了速度而牺牲了完美的准确性.将 float_precision='round_trip' 传递给 read_csv 可以解决这个问题.请参阅文档了解更多.

Pandas uses a dedicated decimal-to-binary converter that sacrifices perfect accuracy for the sake of speed. Passing float_precision='round_trip' to read_csv fixes this. See the documentation for more.

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