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# pandas.Series.interpolate

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

  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. This interpolates values based on time interval between observations.

      - ‘index’: The interpolation uses the numerical values of the DataFrame’s index to linearly calculate missing values.

      - ‘values’: Interpolation based on the numerical values in the DataFrame, treating them as equally spaced along the index.

      - ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d, whereas ‘spline’ is passed to scipy.interpolate.UnivariateSpline. 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)`. Note that, slinear method in Pandas refers to the Scipy first order spline instead of Pandas first order spline.

      - ‘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.

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

      Axis to interpolate along. For Series this parameter is unused and defaults to 0.

    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, default ‘forward’

      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).

    **kwargs optional

      Keyword arguments to pass on to the interpolating function.

  Returns:
    Series or DataFrame

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

  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.

  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 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