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.
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.)
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
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
If you're using pd.ExcelFile to read all the excel file sheets then:
df = pd.ExcelFile("path_to_file.xlsx")
df.sheet_names # Provide the sheet names in the excel file
df = df.parse(2, header=None) # Parsing the 2nd sheet in the file with header = None
df
Output:
0 1
0 a b
1 1 1
2 0 1
3 5 2
You can set custom column name in order to prevent this:
Let say if you have two columns in your dataset then:
df = pd.read_csv(your_file_path, names = ['first column', 'second column'])
You can also generate programmatically column names if you have more than and can pass a list in front of names attribute.
