I would like to know if there is a function to change specific column names but without selecting a specific name or without changing all of them.
I have the code:
df=df.rename(columns = {'nameofacolumn':'newname'})
But with it i have to manually change each one of them writing each name. Also to change all of them I have
df = df.columns['name1','name2','etc']
I would like to have a function to change columns 1 and 3 without writing their names just stating their location.
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)
colunms is wrong. You put n before m. It should be columns!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)
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 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
dict = {'First Name': 'Name', 'Location': 'City', 'Pay': 'Salary'}
df.rename(columns=dict,inplace=True)
if you have a dict of {position: new_name}, you can use items()
e.g.,
new_columns = {3: 'fourth_column'}
df.rename(columns={df.columns[i]: new_col for i, new_col in new_cols.items()})
full example:
$ ipython
Python 3.7.10 | packaged by conda-forge | (default, Feb 19 2021, 16:07:37)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.24.1 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import numpy as np
...: import pandas as pd
...:
...: rng = np.random.default_rng(seed=0)
...: df = pd.DataFrame({key: rng.uniform(size=3) for key in list('abcde')})
...: df
Out[1]:
a b c d e
0 0.636962 0.016528 0.606636 0.935072 0.857404
1 0.269787 0.813270 0.729497 0.815854 0.033586
2 0.040974 0.912756 0.543625 0.002739 0.729655
In [2]: new_columns = {3: 'fourth_column'}
...: df.rename(columns={df.columns[i]: new_col for i, new_col in new_columns.items()})
Out[2]:
a b c fourth_column e
0 0.636962 0.016528 0.606636 0.935072 0.857404
1 0.269787 0.813270 0.729497 0.815854 0.033586
2 0.040974 0.912756 0.543625 0.002739 0.729655
In [3]:
df.columns[index_num].