Rename Pandas column headers using dictionary keys?-CodePudding

I want to change the headers of my dataframe based on a dictionary, but I need to reference the columns as lower case.

dict = {'january': 'Jan', 'february': 'Feb', etc.}
df.columns = ['JANUARY','FEBRUARY', etc.]

I know I can do something like this:

df = df.rename(columns=dict)

But this wouldn't work because the columns don't equal the keys. Is there a way to map the dict values to the headers without making a new dict or lower casing all the column headers? Can I map by df.columns.str.lower() somehow?

CodePudding user response：

You could use str.lower map on df.columns to modify it:

dct = {'january':'Jan','february':'Feb', 'march':'Mar', 'april':'Apr'}
df.columns = df.columns.str.lower().map(dct)

For the following DataFrame:

    JANUARY  FEBRUARY  MARCH  APRIL
0         0         0      1      0
1         0         1      1      1

the above code produces:

    Jan  Feb  Mar  Apr
0     0    0    1    0
1     0    1    1    1

CodePudding user response：

If I am not wrong, you need to change your header to something like:

jan  feb  mar  apr  may  jun  jul  aug  sep  oct  nov  dec

I mean, not only the value of the dictionary but also all in lowercase.

So, the following approach will help you to get your answer:

import pandas as pd


dic = {'january': 'Jan', 'february': 'Feb', 'march': 'Mar', 'april': 'Apr', 'may': 'May', 'june': 'Jun', 'july': 'Jul',
       'august': 'Aug', 'september': 'Sep', 'october': 'Oct', 'november': 'Nov', 'december': 'Dec'}

data = {'JANUARY': [2, 16, 19, 15],
        'FEBRUARY': [20, 8, 4, 5],
        'MARCH': [1, 2, 4, 11],
        'APRIL': [14, 4, 20, 13],
        'MAY': [6, 4, 13, 1],
        'JUNE': [6, 2, 19, 14],
        'JULY': [17, 8, 13, 19],
        'AUGUST': [0, 8, 9, 15],
        'SEPTEMBER': [6, 3, 3, 18],
        'OCTOBER': [1, 8, 6, 15],
        'NOVEMBER': [18, 20, 8, 3],
        'DECEMBER': [18, 17, 7, 16]}

df = pd.DataFrame.from_dict(data)
print(df)

Result:

   JANUARY  FEBRUARY  MARCH  APRIL  ...  SEPTEMBER  OCTOBER  NOVEMBER  DECEMBER
0        2        20      1     14  ...          6        1        18        18
1       16         8      2      4  ...          3        8        20        17
2       19         4      4     20  ...          3        6         8         7
3       15         5     11     13  ...         18       15         3        16

df.columns = df.columns.str.lower().map(dict((k, v.lower()) for k, v in dic.items()))

print(df)

result:

   jan  feb  mar  apr  may  jun  jul  aug  sep  oct  nov  dec
0    2   20    1   14    6    6   17    0    6    1   18   18
1   16    8    2    4    4    2    8    8    3    8   20   17
2   19    4    4   20   13   19   13    9    3    6    8    7
3   15    5   11   13    1   14   19   15   18   15    3   16