bigframes.pandas.DataFrame.interpolate#

DataFrame.interpolate(method: str = 'linear') → DataFrame[source]#

Fill NA (NULL in BigQuery) values using an interpolation method.

Examples:

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3, None, None, 6],
...     'B': [None, 6, None, 2, None, 3],
...     }, index=[0, 0.1, 0.3, 0.7, 0.9, 1.0])
>>> df.interpolate()
       A     B
0.0  1.0  <NA>
0.1  2.0   6.0
0.3  3.0   4.0
0.7  4.0   2.0
0.9  5.0   2.5
1.0  6.0   3.0

[6 rows x 2 columns]
>>> df.interpolate(method="values")
            A         B
0.0       1.0      <NA>
0.1       2.0       6.0
0.3       3.0  4.666667
0.7  4.714286       2.0
0.9  5.571429  2.666667
1.0       6.0       3.0

[6 rows x 2 columns]

Parameters:

method (str, default 'linear') Interpolation technique to use. Only linear supported. linear: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. index, values: use the actual numerical values of the index. pad: Fill in NaNs using existing values. nearest, zero, slinear: Emulates scipy.interpolate.interp1d

Returns:

Returns the same object type as the caller, interpolated at some or all NaN values

Return type:

bigframes.pandas.DataFrame