How can I make pandas dataframe column headers all lowercase?

Question

I want to make all column headers in my pandas data frame lower case

Example

If I have:

data =
 country country isocode year XRAT tcgdp
0 Canada CAN 2001 1.54876 924909.44207
1 Canada CAN 2002 1.56932 957299.91586
2 Canada CAN 2003 1.40105 1016902.00180
....

I would like to change XRAT to xrat by doing something like:

data.headers.lowercase()

So that I get:

 country country isocode year xrat tcgdp
0 Canada CAN 2001 1.54876 924909.44207
1 Canada CAN 2002 1.56932 957299.91586
2 Canada CAN 2003 1.40105 1016902.00180
3 Canada CAN 2004 1.30102 1096000.35500
....

I will not know the names of each column header ahead of time.

• Alex Montoya: More easiest df.columns = df.columns.str.lower()
• Trenton McKinney: Hello Nasuki, while the original answer was acceptable at the time of the original question, this answer below, is better. Please consider marking it as the accepted answer. Best Regards.

Answer 1

You can do it like this:

data.columns = map(str.lower, data.columns)

or

data.columns = [x.lower() for x in data.columns]

example:

>>> data = pd.DataFrame({'A':range(3), 'B':range(3,0,-1), 'C':list('abc')})
>>> data
 A B C
0 0 3 a
1 1 2 b
2 2 1 c
>>> data.columns = map(str.lower, data.columns)
>>> data
 a b c
0 0 3 a
1 1 2 b
2 2 1 c

• william_grisaitis: Note that this can lead to duplicate column names when lower(column1) == lower(column2) (e.g., 'a' and 'A'). This can have unintended consequences when referencing columns later. (e.g. data['a'] will return a DataFrame, not a Series, with all columns named 'a'). See this gist for an example: gist.github.com/grisaitis/170e82a008480acb4fa3
• joctee: [x.lower() for x in data.columns] is equivalent to: [x.lower() for x in data]
• Pawel Kranzberg: While you are at it, it's good to use [x.lower().strip() for x in df0]
• curious_nustian: @PawelKranzberg Do you have any idea how to lower the column names of MultiIndex
• Pawel Kranzberg: @curious_nustian - Yes, e.g.: df.index.names = [x.lower().strip() for x in df.index.names]

Answer 2

You could do it easily with str.lower for columns:

df.columns = df.columns.str.lower()

Example:

In [63]: df
Out[63]: 
 country country isocode year XRAT tcgdp
0 Canada CAN 2001 1.54876 9.249094e+05
1 Canada CAN 2002 1.56932 9.572999e+05
2 Canada CAN 2003 1.40105 1.016902e+06
In [64]: df.columns = df.columns.str.lower()
In [65]: df
Out[65]: 
 country country isocode year xrat tcgdp
0 Canada CAN 2001 1.54876 9.249094e+05
1 Canada CAN 2002 1.56932 9.572999e+05
2 Canada CAN 2003 1.40105 1.016902e+06

Answer 3

If you want to do the rename using a chained method call, you can use

data.rename(columns=str.lower)

If you're not chaining method calls, you can add inplace=True

data.rename(columns=str.lower, inplace=True)

Answer 4

df.columns = df.columns.str.lower()

is the easiest but will give an error if some headers are numeric

if you have numeric headers then use this:

df.columns = [str(x).lower() for x in df.columns]

• Anton Protopopov: I guess it's easier to write df.columns.astype(str).str.lower() in that case but maybe a bit verbose.
• Daniel: These two methods have already been mentioned by the answers above. The only noteworthy comment you make is the case of numeric headers which really should be added to the existing generator answer.
• Chadee Fouad: @Daniel My method is better because it will not break if there are numeric columns. I used the first one for simplicity but frankly the first one should be rejected as it is prone to errors. In short, my method can be a standalone answer without referring to the first one.

Answer 5

I noticed some of the other answers will fail if a column name is made of digits (e.g. "123"). Try these to handle such cases too.

Option 1: Use df.rename

def rename_col(old_name):
 return str(old_name).lower()
df.rename(rename_col)

Option 2 (from this comment):

df.columns.astype(str).str.lower()

Answer 6

Another convention based on the official documentation:

frame.rename(mapper=lambda x:x.lower(), axis='columns', inplace=True)

Parameters: mapper: Dict-like or function transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.

• Daniel: I'd say using frame.rename(columns=str.lower, inplace=True) is much cleaner and to the point.

Answer 7

Here is one from my end, 
# import pandas lib as needed 
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'Column One': [1, 2, 3],
'Column Two': [4, 5, 6]
})
# Function to transform column names
def transform_col_name(col_name):
 return col_name.lower().replace(' ', '_')

Rename columns using the transform_col_name function

inplace =True will make changes to df

df.rename(columns=lambda x: transform_col_name(x), inplace=True)
# Check the updated DataFrame
print(df)