Empty column in Pandas Dataframe
How to ignore trailing spaces in a string when converting to Pandas DataFrame?
I have a CSV file in the following format:
Column #1 : Type
Column #2 : Total Length
Column #3 : Found
Column #4 : Grand Total
1;2;1;7.00;
2;32;2;0.76;
3;4;6;6.00;
4;1;5;4.00;
I loop through the "Column #" lines to create the column names first (so 4 columns), then I parse the following lines to create my DataFrame with ';' as a separator. However, some of my files contain a trailing ';' at the end of each row as shown above, so my Pandas DataFrame thinks there is a 5th column containing spaces and hence throws an error to say there are not enough column names.
Is there a mechanism in Pandas to remove / ignore the trailing ';' or a space when creating a DataFrame? I am using read_csv to create a DataFrame.
Thank.
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Just pass the parameter to usecols
:
In [160]:
t="""1;2;1;7.00;
2;32;2;0.76;
3;4;6;6.00;
4;1;5;4.00;"""
import pandas as pd
import io
df = pd.read_csv(io.StringIO(t), sep=';', header=None, usecols=range(4))
df
Out[160]:
0 1 2 3
0 1 2 1 7.00
1 2 32 2 0.76
2 3 4 6 6.00
3 4 1 5 4.00
Here I am creating a list [0,1,2,3]
to indicate which columns I am interested in.
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