如何使Pandas DataFrame列标题全部小写？

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.

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

You could do it easily with 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

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

data.rename(
    columns=unicode.lower
)

(Python 2)

or

data.rename(
    columns=str.lower
)

(Python 3)

Here is a simple way: data.columns = data.columns.str.lower()

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]