pandas.core.resample.Resampler.interpolate¶
- Resampler.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.
Fill NaN values using an interpolation method.
Please note that only
method='linear'
is supported for DataFrame/Series with a MultiIndex.- Parameters
- methodstr, 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.
- limitint, optional
Maximum number of consecutive NaNs to fill. Must be greater than 0.
- inplacebool, 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).
- downcastoptional, ‘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 ifinplace=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 aSeries
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 consecutiveNaN
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 anorder
(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