How can I strip the whitespace from Pandas DataFrame headers?

I am parsing data from an Excel file that has extra white space in some of the column headings.

When I check the columns of the resulting dataframe, with df.columns, I see:

Index(['Year', 'Month ', 'Value'])
                     ^
#                    Note the unwanted trailing space on 'Month '

Consequently, I can't do:

df["Month"]

Because it will tell me the column is not found, as I asked for "Month", not "Month ".

My question, then, is how can I strip out the unwanted white space from the column headings?

You can give functions to the rename method. The str.strip() method should do what you want:

In [5]: df
Out[5]: 
   Year  Month   Value
0     1       2      3

[1 rows x 3 columns]

In [6]: df.rename(columns=lambda x: x.strip())
Out[6]: 
   Year  Month  Value
0     1      2      3

[1 rows x 3 columns]

Note: that this returns a DataFrame object and it's shown as output on screen, but the changes are not actually set on your columns. To make the changes, either use this in a method chain or re-assign the df variabe:

df = df.rename(columns=lambda x: x.strip())

Since version 0.16.1 you can just call .str.strip on the columns:

df.columns = df.columns.str.strip()

Here is a small example:

In [5]:
df = pd.DataFrame(columns=['Year', 'Month ', 'Value'])
print(df.columns.tolist())
df.columns = df.columns.str.strip()
df.columns.tolist()

['Year', 'Month ', 'Value']
Out[5]:
['Year', 'Month', 'Value']

Timings

In[26]:
df = pd.DataFrame(columns=[' year', ' month ', ' day', ' asdas ', ' asdas', 'as ', '  sa', ' asdas '])
df
Out[26]: 
Empty DataFrame
Columns: [ year,  month ,  day,  asdas ,  asdas, as ,   sa,  asdas ]


%timeit df.rename(columns=lambda x: x.strip())
%timeit df.columns.str.strip()
1000 loops, best of 3: 293 µs per loop
10000 loops, best of 3: 143 µs per loop

So str.strip is ~2X faster, I expect this to scale better for larger dfs

If you use CSV format to export from Excel and read as Pandas DataFrame, you can specify:

skipinitialspace=True

when calling pd.read_csv.

From the documentation:

> skipinitialspace : bool, default False > > Skip spaces after delimiter.

Actually can do that with

df.rename(str.strip, axis = 'columns')

Which is shown in Pandas documentation here.

If you are looking for an unbreakable way to do it, I would suggest:

data_frame.rename(columns=lambda x: x.strip() if isinstance(x, str) else x, inplace=True)