Changing multiple column names but not all of them - Pandas Python

You don't need to use rename method at all.

You simply replace the old column names with new ones using lists. To rename columns 1 and 3 (with index 0 and 2), you do something like this:

df.columns.values[[0, 2]] = ['newname0', 'newname2']

or possibly if you are using older version of pandas than 0.16.0, you do:

df.keys().values[[0, 2]] = ['newname0', 'newname2']

The advantage of this approach is, that you don't need to copy the whole dataframe with syntax df = df.rename, you just change the index values.

You can use a dict comprehension and pass this to rename:

In [246]:
df = pd.DataFrame(columns=list('abc'))
new_cols=['d','e']
df.rename(columns=dict(zip(df.columns[1:], new_cols)),inplace=True)
df

Out[246]:
Empty DataFrame
Columns: [a, d, e]
Index: []

It also works if you pass a list of ordinal positions:

df.rename(columns=dict(zip(df.columns[[1,2]], new_cols)),inplace=True)

say you have a dictionary of the new column names and the name of the column they should replace:

df.rename(columns={'old_col':'new_col', 'old_col_2':'new_col_2'}, inplace=True)

But, if you don't have that, and you only have the indices, you can do this:

column_indices = [1,4,5,6]
new_names = ['a','b','c','d']
old_names = df.columns[column_indices]
df.rename(columns=dict(zip(old_names, new_names)), inplace=True)

You should be able to reference the columns by index using ..df.columns[index]

>> temp = pd.DataFrame(np.random.randn(10, 5),columns=['a', 'b', 'c', 'd', 'e'])
>> print(temp.columns[0])
   a  
>> print(temp.columns[1])
   b

So to change the value of specific columns, first assign the values to an array and change only the values you want

>> newcolumns=temp.columns.values
>> newcolumns[0] = 'New_a'

Assign the new array back to the columns and you'll have what you need

>> temp.columns = newcolumns
>> temp.columns
>> print(temp.columns[0])
   New_a