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

Answer 1

You can refer to this other post on loc, iloc, at, iat

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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.

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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)

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I also included examples of using pd.DataFrame.get_value