# pandas.core.resample.Resampler.interpolate

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

  Interpolate values according to different methods.

  Fill NaN values using an interpolation method.

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

  Parameters
    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. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. `df.interpolate(method='polynomial', order=5)`.

      - ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: 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.

    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’}}, Optional

      Consecutive NaNs will be filled in this direction.

      If limit is specified:

        - If ‘method’ is ‘pad’ or ‘ffill’, ‘limit_direction’ must be ‘forward’.

        - If ‘method’ is ‘backfill’ or ‘bfill’, ‘limit_direction’ must be ‘backwards’.

      If ‘limit’ is not specified:

        - If ‘method’ is ‘backfill’ or ‘bfill’, the default is ‘backward’

        - else the default is ‘forward’

      Changed in version 1.1.0: raises ValueError if limit_direction is ‘forward’ or ‘both’ and method is ‘backfill’ or ‘bfill’. raises ValueError if limit_direction is ‘backward’ or ‘both’ and method is ‘pad’ or ‘ffill’.

    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).

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

      Downcast dtypes if possible.

    ``**kwargs`` optional

      Keyword arguments to pass on to the interpolating function.

  Returns
    Series or DataFrame or None

      Returns the same object type as the caller, interpolated at some or all `NaN` values or None if `inplace=True`.

  See also

  `fillna`

    Fill missing values using different methods.

  `scipy.interpolate.Akima1DInterpolator`

    Piecewise cubic polynomials (Akima interpolator).

  `scipy.interpolate.BPoly.from_derivatives`

    Piecewise polynomial in the Bernstein basis.

  `scipy.interpolate.interp1d`

    Interpolate a 1-D function.

  `scipy.interpolate.KroghInterpolator`

    Interpolate polynomial (Krogh interpolator).

  `scipy.interpolate.PchipInterpolator`

    PCHIP 1-d monotonic cubic interpolation.

  `scipy.interpolate.CubicSpline`

    Cubic spline data interpolator.

  Notes

  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.

  Examples

  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 before 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