Prevent pandas read_csv treating first row as header of column names

Posted on

Question :

Prevent pandas read_csv treating first row as header of column names

I’m reading in a pandas DataFrame using pd.read_csv. I want to keep the first row as data, however it keeps getting converted to column names.

  • I tried header=False but this just deleted it entirely.

(Note on my input data: I have a string (st = 'n'.join(lst)) that I convert to a file-like object (io.StringIO(st)), then build the csv from that file object.)

Asked By: Rafael

||

Answer #1:

You want header=None the False gets type promoted to int into 0 see the docs emphasis mine:

header : int or list of ints, default ‘infer’ Row number(s) to use as
the column names, and the start of the data. Default behavior is as if
set to 0 if no names passed, otherwise None. Explicitly pass header=0
to be able to replace existing names. The header can be a list of
integers that specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be skipped
(e.g. 2 in this example is skipped). Note that this parameter ignores
commented lines and empty lines if skip_blank_lines=True, so header=0
denotes the first line of data rather than the first line of the file.

You can see the difference in behaviour, first with header=0:

In [95]:
import io
import pandas as pd
t="""a,b,c
0,1,2
3,4,5"""
pd.read_csv(io.StringIO(t), header=0)

Out[95]:
   a  b  c
0  0  1  2
1  3  4  5

Now with None:

In [96]:
pd.read_csv(io.StringIO(t), header=None)

Out[96]:
   0  1  2
0  a  b  c
1  0  1  2
2  3  4  5

Note that in latest version 0.19.1, this will now raise a TypeError:

In [98]:
pd.read_csv(io.StringIO(t), header=False)

TypeError: Passing a bool to header is invalid. Use header=None for no
header or header=int or list-like of ints to specify the row(s) making
up the column names

Answered By: EdChum

Answer #2:

I think you need parameter header=None to read_csv:

Sample:

import pandas as pd
from pandas.compat import StringIO

temp=u"""a,b
2,1
1,1"""

df = pd.read_csv(StringIO(temp),header=None)
print (df)
   0  1
0  a  b
1  2  1
2  1  1
Answered By: jezrael

Leave a Reply

Your email address will not be published.