How can I make pandas dataframe column headers all lowercase?
PythonPandasDataframePython Problem Overview
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.
Python Solutions
Solution 1 - Python
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
Solution 2 - Python
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
Solution 3 - Python
If you want to do the rename using a chained method call, you can use
data.rename(columns=str.lower)
Solution 4 - Python
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]
Solution 5 - Python
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()
Solution 6 - Python
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.