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DataFrame.interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', limit_area=None, downcast=None, **kwargs)[source]

Interpolate values according to different methods.

Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.


method : str, default ‘linear’

Interpolation technique to use. One of:

  • ‘linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
  • ‘time’: Works on daily and higher resolution data to interpolate given length of interval.
  • ‘index’, ‘values’: use the actual numerical values of the index.
  • ‘pad’: Fill in NaNs using existing values.
  • ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=4). These use the numerical values of the index.
  • ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’: Wrappers around the SciPy interpolation methods of similar names. See Notes.
  • ‘from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives which replaces ‘piecewise_polynomial’ interpolation method in scipy 0.18.

New in version 0.18.1: Added support for the ‘akima’ method. Added interpolate method ‘from_derivatives’ which replaces ‘piecewise_polynomial’ in SciPy 0.18; backwards-compatible with SciPy < 0.18

axis : {0 or ‘index’, 1 or ‘columns’, None}, default None

Axis to interpolate along.

limit : int, optional

Maximum number of consecutive NaNs to fill. Must be greater than 0.

inplace : bool, default False

Update the data in place if possible.

limit_direction : {‘forward’, ‘backward’, ‘both’}, default ‘forward’

If limit is specified, consecutive NaNs will be filled in this direction.

limit_area : {None, ‘inside’, ‘outside’}, default None

If limit is specified, consecutive NaNs will be filled with this restriction.

  • None: No fill restriction.
  • ‘inside’: Only fill NaNs surrounded by valid values (interpolate).
  • ‘outside’: Only fill NaNs outside valid values (extrapolate).

New in version 0.21.0.

downcast : optional, ‘infer’ or None, defaults to None

Downcast dtypes if possible.


Keyword arguments to pass on to the interpolating function.


Series or DataFrame

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

See also

Fill missing values using different methods.
Piecewise cubic polynomials (Akima interpolator).
Piecewise polynomial in the Bernstein basis.
Interpolate a 1-D function.
Interpolate polynomial (Krogh interpolator).
PCHIP 1-d monotonic cubic interpolation.
Cubic spline data interpolator.


The ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’ and ‘akima’ methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the SciPy documentation and SciPy tutorial.


Filling in NaN in a Series via linear interpolation.

>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0    0.0
1    1.0
2    NaN
3    3.0
dtype: float64
>>> s.interpolate()
0    0.0
1    1.0
2    2.0
3    3.0
dtype: float64

Filling in NaN in a Series by padding, but filling at most two consecutive NaN at a time.

>>> s = pd.Series([np.nan, "single_one", np.nan,
...                "fill_two_more", np.nan, np.nan, np.nan,
...                4.71, np.nan])
>>> s
0              NaN
1       single_one
2              NaN
3    fill_two_more
4              NaN
5              NaN
6              NaN
7             4.71
8              NaN
dtype: object
>>> s.interpolate(method='pad', limit=2)
0              NaN
1       single_one
2       single_one
3    fill_two_more
4    fill_two_more
5    fill_two_more
6              NaN
7             4.71
8             4.71
dtype: object

Filling in NaN in a Series via polynomial interpolation or splines: Both ‘polynomial’ and ‘spline’ methods require that you also specify an order (int).

>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0    0.000000
1    2.000000
2    4.666667
3    8.000000
dtype: float64

Fill the DataFrame forward (that is, going down) along each column using linear interpolation.

Note how the last entry in column ‘a’ is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column ‘b’ remains NaN, because there is no entry befofe it to use for interpolation.

>>> df = pd.DataFrame([(0.0,  np.nan, -1.0, 1.0),
...                    (np.nan, 2.0, np.nan, np.nan),
...                    (2.0, 3.0, np.nan, 9.0),
...                    (np.nan, 4.0, -4.0, 16.0)],
...                   columns=list('abcd'))
>>> df
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  NaN  2.0  NaN   NaN
2  2.0  3.0  NaN   9.0
3  NaN  4.0 -4.0  16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  1.0  2.0 -2.0   5.0
2  2.0  3.0 -3.0   9.0
3  2.0  4.0 -4.0  16.0

Using polynomial interpolation.

>>> df['d'].interpolate(method='polynomial', order=2)
0     1.0
1     4.0
2     9.0
3    16.0
Name: d, dtype: float64
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