Renaming column names from a data set in pandas

Question

I am trying to rename column names from a DataFrame that have space in the name. DataFrame (df) consists of 45 columns and the majority have spaces in the name. For instance: df.column.values [1] = 'Date Release', and the name should be changed to 'Date_Release'. I tried DataFrame.rename () and DataFrame.columns.values[] but did not work. I would much appreciate it if you could help me to find out what I did wrong

for colmns in df: if ' ' in colmns: colmns_new = '_'.join(colmns.split()) df = df.rename (columns = {"\"%s\"" %colmns : "\"%s\"" %colmns_new}) else: print (colmns) print (df)

or this one:

for i in range (len(df.columns)): old= df.columns.values[i] if ' ' in old: new = '_'.join(old.split()) df = df.columns.values[i] = ['%s' % new] print ("\"%s\"" % new) print (df)

Error: AttributeError: 'list' object has no attribute 'columns'

You can just give df.columns.str.replace(' ','_') and it will replace the columns that have space with an underscore — Joe Ferndz
– Joe Ferndz, Commented Jan 15, 2021 at 10:10

Mo Huss · Accepted Answer · 2020-12-09 00:48:12Z

2

import pandas as pd df.columns = [i.replace(' ','_') for i in df.columns]

answered Dec 9, 2020 at 0:48

Mo Huss

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1 Comment

Firuz Over a year ago

Thanks a lot. As not being an experienced programmer, I struggled to find out a solution for two days.

Joe Ferndz · Accepted Answer · 2021-01-15 10:14:53Z

You can just give df.columns = df.columns.str.replace(' ','_') to replace the space with an underscore.

Here's an example. Here column a1 does not have a space. However columns b 2 and c 3 have a space.

>>> df = pd.DataFrame({'a1': range(1,5), 'b 2': list ('abcd'), 'c 3':list('pqrs')}) >>> df a1 b 2 c 3 0 1 a p 1 2 b q 2 3 c r 3 4 d s >>> df.columns = df.columns.str.replace(' ','_') >>> df a1 b_2 c_3 0 1 a p 1 2 b q 2 3 c r 3 4 d s

Vaishali · Accepted Answer · 2020-12-09 00:48:33Z

You can use regex to replace spaces with underscore

Here is an example df with some columns containing spaces,

cols = ['col {}'.format(i) for i in range(1, 10, 1)] + ['col10'] df = pd.DataFrame(columns = cols) import re df.columns = [re.sub(' ','_',i) for i in df.columns]

You get

col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8 col_9 col10

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