How To Change Pandas Column Names to Lower Case?

Cleaning up the column names of a dataframe often can save a lot of head aches while doing data analysis. In this post, we will learn how to change column names of a Pandas dataframe to lower case. And then we will do additional clean up of columns and see how to remove empty spaces around column names.

Let us load Pandas and scipy.stats.

import pandas as pd
from scipy.stats import poisson

We will create a toy dataframe with three columns. We will first name the dataframe’s columns with upper cases.

c1= poisson.rvs(mu=10, size=5)
c2= poisson.rvs(mu=15, size=5)
c3= poisson.rvs(mu=20, size=5)
df=pd.DataFrame({"COLUMN1":c1,
                 "COLUMN2":c2,
                 "COLUMN3":c3})

Our data frame’s column names starts with uppercase.

df.head()
COLUMN1 COLUMN2 COLUMN3
0       16      12      16
1       12      14      11
2       15      15      23
3       8       14      24
4       11      15      32

How To Convert Pandas Column Names to lowercase?

We can convert the names into lower case using Pandas’ str.lower() function. We first take the column names and convert it to lower case.

And then rename the Pandas columns using the lowercase names. Now our dataframe’s names are all in lower case.

# rename Pandas columns to lower case
df.columns= df.columns.str.lower()
df.columns
Index(['column1', 'column2', 'column3'], dtype='object')

Cleaning up Pandas Column Names

In addition to upper cases, sometimes column names can have both leading and trailing empty spaces. Let us create a toy dataframe with column names having trailing spaces.

df=pd.DataFrame({" C1 ":c1,
                 "C2":c2,
                 "C3 ":c3})

By inspecting column names we can see the spaces.

df.columns
Index([' C1 ', 'C2', 'C3 '], dtype='object')

We can use str.strip() function Pandas to strip the leading and trailing white spaces. Here we also convert the column names into lower cases using str.lower() as before.

# Column names: remove white spaces and convert to lower case
df.columns= df.columns.str.strip().str.lower()
df.columns

Index(['c1', 'c2', 'c3'], dtype='object')

We use Pandas chaining operation to do both and re-assign the cleaned column names.

df

c1      c2      c3
0       16      12      16
1       12      14      11
2       15      15      23
3       8       14      24
4       11      15      32

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