5.7. DataFrame At

• .at[row, col] - fancy indexing

• .iat[row, col] - integer at (no fancy indexing)

• Access a single value for a row/column pair by integer position

• Use iat if you need to get or set a single value in a DataFrame or Series

Pandas Select Cell:

5.7.1. SetUp

>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(0)
>>>
>>>
>>> df = pd.DataFrame(
...     columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
...     index = pd.date_range('1999-12-30', periods=7),
...     data = np.random.randn(7, 4))
>>>
>>> df
             Morning      Noon   Evening  Midnight
1999-12-30  1.764052  0.400157  0.978738  2.240893
1999-12-31  1.867558 -0.977278  0.950088 -0.151357
2000-01-01 -0.103219  0.410599  0.144044  1.454274
2000-01-02  0.761038  0.121675  0.443863  0.333674
2000-01-03  1.494079 -0.205158  0.313068 -0.854096
2000-01-04 -2.552990  0.653619  0.864436 -0.742165
2000-01-05  2.269755 -1.454366  0.045759 -0.187184

5.7.2. Get value at specified row/column pair

• First argument is column

• Second argument is row

>>> df.iat[0,0]
np.float64(1.764052345967664)
>>>
>>> df.iat[1,0]
np.float64(1.8675579901499675)
>>>
>>> df.iat[0,1]
np.float64(0.4001572083672233)

5.7.3. Get value from row

• loc returns Series

>>> df.loc['2000-01-01'].iat[1]
np.float64(0.41059850193837233)

5.7.4. Set value at a position

>>> df.iat[0,0] = pd.NA
>>> df
             Morning      Noon   Evening  Midnight
1999-12-30       NaN  0.400157  0.978738  2.240893
1999-12-31  1.867558 -0.977278  0.950088 -0.151357
2000-01-01 -0.103219  0.410599  0.144044  1.454274
2000-01-02  0.761038  0.121675  0.443863  0.333674
2000-01-03  1.494079 -0.205158  0.313068 -0.854096
2000-01-04 -2.552990  0.653619  0.864436 -0.742165
2000-01-05  2.269755 -1.454366  0.045759 -0.187184