Reputation: 821
I have the following dataframe, where I want to rename the index fromsummary
to id
:
summary student count
0 error 6
1 yes 1
2 no 1
3 other 9
I have tried:
newdf = df.reset_index().rename(columns={df.index.name:'foo'})
which gives:
summary index student count
0 0 error 6
1 1 yes 1
2 2 no 1
3 3 other 9
I have also tried: df.index.rename('foo', inplace = True)
which gives:
summary student count
foo
0 error 6
1 yes 1
2 no 1
3 other 9
I have also tried: df.rename_axis('why', inplace = True)
which gives:
summary student count
why
0 error 6
1 yes 1
2 no 1
3 other 9
When I do df.dtypes
:
summary
student object
count init64
dtype: object
What I would like:
id student count
0 error 6
1 yes 1
2 no 1
3 other 9
OR:
student count
0 error 6
1 yes 1
2 no 1
3 other 9
Upvotes: 6
Views: 33166
Reputation: 6091
you need to access the index's properties
df.index.name = 'id'
original
student count
summary
0 error 6
1 yes 1
2 no 1
3 other 9
fixed df:
student count
id
0 error 6
1 yes 1
2 no 1
3 other 9
Update: seems like you had a name for the column's index. You should remove it with
df.columns.names = ''
Upvotes: 6
Reputation: 59549
You need to remove the column name:
df.rename_axis(None, axis=1).rename_axis('id', axis=0)
##if pd.__version__ == 0.24.0
#df.rename_axis([None], axis=1).rename_axis('id')
The problem is that 'summary'
is your column name. When there is no index name, the column name is placed directly above the index, which can be misleading:
import pandas as pd
df = pd.DataFrame([[1]*2]*4, columns=['A', 'B'])
df.columns.name = 'col_name'
print(df)
#col_name A B
#0 1 1
#1 1 1
#2 1 1
#3 1 1
When you then try to add an index name, it becomes clear that 'col_name'
was really the column name.
df.index.name = 'idx_name'
print(df)
#col_name A B
#idx_name
#0 1 1
#1 1 1
#2 1 1
#3 1 1
There is no ambiguity though: when you have an index name, the columns are raised one level, which allows you to distinguish between an index name and a column name.
df = pd.DataFrame([[1]*2]*4, columns=['A', 'B'])
df.index.name = 'idx_name'
print(df)
# A B
#idx_name
#0 1 1
#1 1 1
#2 1 1
#3 1 1
Upvotes: 13
Reputation: 143
First you can drop the column:
df = df.drop('summary', axis=1)
df['id'] = np.arange(df.shape[0])
df.set_index('id', inplace=True)
Then you can get the desired result.
Upvotes: 1