Getting a scalar by integer location and column label (mixed indexing)
Question:
To get a scalar at integer location 0 and column label 'A' in a data frame df, I do chained indexing: df.iloc[0]['A']. This works, but pandas documentation says that chained indexing should be avoided.
An alternative I could come up with is df.iat[0, df.columns.get_loc('A')], which is just too much typing compared with the chained indexing. Is there a shorter way to do this?
Note: .ix indexer is deprecated.
Example:
df=pd.DataFrame({'A':[10,20,30,40]}, index=[3,2,1,0])
A
------
3 10
2 20
1 30
0 40
The scalar at integer location 0 in column A is 10 and not 40:
df.iat[0, df.columns.get_loc('A')]
Otuput: 10
Answers:
You can refer to this other post on loc, iloc, at, iat
To answer your question directly:
This is called mixed type indexing. You want to access one dimension by position and the other by label.
To solve this problem, we need to translate either:
- the position into a label then use
loc (or at) for label indexing.
- the label into a position then use
iloc (or iat) for position indexing.
Using loc
We get the label at the 0 position
df.loc[df.index[0], 'A']
Or
df.at[df.index[0], 'A']
Or
df.get_value(df.index[0], 'A')
Using iloc
We get the position of the label using pd.Index.get_loc
df.iloc[0, df.columns.get_loc('A')]
Or
df.iat[0, df.columns.get_loc('A')]
Or
df.get_value(0, df.columns.get_loc('A'), takable=True)
I also included examples of using pd.DataFrame.get_value
Here’s Pandas Official Guide on doing Indexing with both Integer and Label. Hope this is helpful.
To get a scalar at integer location 0 and column label 'A' in a data frame df, I do chained indexing: df.iloc[0]['A']. This works, but pandas documentation says that chained indexing should be avoided.
An alternative I could come up with is df.iat[0, df.columns.get_loc('A')], which is just too much typing compared with the chained indexing. Is there a shorter way to do this?
Note: .ix indexer is deprecated.
Example:
df=pd.DataFrame({'A':[10,20,30,40]}, index=[3,2,1,0])
A
------
3 10
2 20
1 30
0 40
The scalar at integer location 0 in column A is 10 and not 40:
df.iat[0, df.columns.get_loc('A')]
Otuput: 10
You can refer to this other post on loc, iloc, at, iat
To answer your question directly:
This is called mixed type indexing. You want to access one dimension by position and the other by label.
To solve this problem, we need to translate either:
- the position into a label then use
loc(orat) for label indexing. - the label into a position then use
iloc(oriat) for position indexing.
Using loc
We get the label at the 0 position
df.loc[df.index[0], 'A']
Or
df.at[df.index[0], 'A']
Or
df.get_value(df.index[0], 'A')
Using iloc
We get the position of the label using pd.Index.get_loc
df.iloc[0, df.columns.get_loc('A')]
Or
df.iat[0, df.columns.get_loc('A')]
Or
df.get_value(0, df.columns.get_loc('A'), takable=True)
I also included examples of using pd.DataFrame.get_value
Here’s Pandas Official Guide on doing Indexing with both Integer and Label. Hope this is helpful.