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将pandas dataframe列导入为字符串而不是int

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问题：将pandas dataframe列导入为字符串而不是int
回答 0
回答 1
回答 2

问题：将pandas dataframe列导入为字符串而不是int

我想将以下csv作为字符串而不是int64导入。熊猫read_csv自动将其转换为int64，但我需要将此列作为字符串。

ID
00013007854817840016671868
00013007854817840016749251
00013007854817840016754630
00013007854817840016781876
00013007854817840017028824
00013007854817840017963235
00013007854817840018860166


df = read_csv('sample.csv')

df.ID
>>

0   -9223372036854775808
1   -9223372036854775808
2   -9223372036854775808
3   -9223372036854775808
4   -9223372036854775808
5   -9223372036854775808
6   -9223372036854775808
Name: ID

不幸的是，使用转换器会得到相同的结果。 

df = read_csv('sample.csv', converters={'ID': str})
df.ID
>>

0   -9223372036854775808
1   -9223372036854775808
2   -9223372036854775808
3   -9223372036854775808
4   -9223372036854775808
5   -9223372036854775808
6   -9223372036854775808
Name: ID
点击查看英文原文

I would like to import the following csv as strings not as int64. Pandas read_csv automatically converts it to int64, but I need this column as string.

ID
00013007854817840016671868
00013007854817840016749251
00013007854817840016754630
00013007854817840016781876
00013007854817840017028824
00013007854817840017963235
00013007854817840018860166


df = read_csv('sample.csv')

df.ID
>>

0   -9223372036854775808
1   -9223372036854775808
2   -9223372036854775808
3   -9223372036854775808
4   -9223372036854775808
5   -9223372036854775808
6   -9223372036854775808
Name: ID

Unfortunately using converters gives the same result. 

df = read_csv('sample.csv', converters={'ID': str})
df.ID
>>

0   -9223372036854775808
1   -9223372036854775808
2   -9223372036854775808
3   -9223372036854775808
4   -9223372036854775808
5   -9223372036854775808
6   -9223372036854775808
Name: ID

回答 0

只是想重申一下，这将适用于> = 0.9.1的熊猫：

In [2]: read_csv('sample.csv', dtype={'ID': object})
Out[2]: 
                           ID
0  00013007854817840016671868
1  00013007854817840016749251
2  00013007854817840016754630
3  00013007854817840016781876
4  00013007854817840017028824
5  00013007854817840017963235
6  00013007854817840018860166

我还在创建一个有关检测整数溢出的问题。

编辑：在这里查看分辨率：https : //github.com/pydata/pandas/issues/2247

点击查看英文原文

Just want to reiterate this will work in pandas >= 0.9.1:

In [2]: read_csv('sample.csv', dtype={'ID': object})
Out[2]: 
                           ID
0  00013007854817840016671868
1  00013007854817840016749251
2  00013007854817840016754630
3  00013007854817840016781876
4  00013007854817840017028824
5  00013007854817840017963235
6  00013007854817840018860166

I’m creating an issue about detecting integer overflows also.

EDIT: See resolution here: https://github.com/pydata/pandas/issues/2247

回答 1

这可能不是最优雅的方法，但是可以完成工作。

In[1]: import numpy as np

In[2]: import pandas as pd

In[3]: df = pd.DataFrame(np.genfromtxt('/Users/spencerlyon2/Desktop/test.csv', dtype=str)[1:], columns=['ID'])

In[4]: df
Out[4]: 
                       ID
0  00013007854817840016671868
1  00013007854817840016749251
2  00013007854817840016754630
3  00013007854817840016781876
4  00013007854817840017028824
5  00013007854817840017963235
6  00013007854817840018860166

只需替换'/Users/spencerlyon2/Desktop/test.csv'为文件的路径

点击查看英文原文

This probably isn’t the most elegant way to do it, but it gets the job done.

In[1]: import numpy as np

In[2]: import pandas as pd

In[3]: df = pd.DataFrame(np.genfromtxt('/Users/spencerlyon2/Desktop/test.csv', dtype=str)[1:], columns=['ID'])

In[4]: df
Out[4]: 
                       ID
0  00013007854817840016671868
1  00013007854817840016749251
2  00013007854817840016754630
3  00013007854817840016781876
4  00013007854817840017028824
5  00013007854817840017963235
6  00013007854817840018860166

Just replace '/Users/spencerlyon2/Desktop/test.csv' with the path to your file

回答 2

从pandas 1.0开始，它变得更加简单。这会将列“ ID”读取为dtype“ string”： 

pd.read_csv('sample.csv',dtype={'ID':'string'})

如我们在本入门指南中所见，引入了’string’dtype（在将字符串视为dtype’object’之前）。

点击查看英文原文

Since pandas 1.0 it became much more straightforward. This will read column ‘ID’ as dtype ‘string’: 

pd.read_csv('sample.csv',dtype={'ID':'string'})

As we can see in this Getting started guide, ‘string’ dtype has been introduced (before strings were treated as dtype ‘object’).