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
This question is related to
python
pandas
casting
type-conversion
dtype
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').
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
Source: Stackoverflow.com