pandas.DataFrame.interpolate#

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

Fill NaN values using an interpolation method.

Please note that onlymethod='linear' is supported forDataFrame/Series with a MultiIndex.

Parameters:

methodstr, default ‘linear’

Interpolation technique to use. One of:

• ‘linear’: Ignore the index and treat the values as equallyspaced. This is the only method supported on MultiIndexes.

• ‘time’: Works on daily and higher resolution data to interpolategiven 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’,‘barycentric’, ‘polynomial’: Passed toscipy.interpolate.interp1d, whereas ‘spline’ is passed toscipy.interpolate.UnivariateSpline. These methods use the numericalvalues of the index. Both ‘polynomial’ and ‘spline’ require thatyou also specify anorder (int), e.g.df.interpolate(method='polynomial',order=5). Note that,slinear method in Pandas refers to the Scipy first ordersplineinstead of Pandas first orderspline.

• ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’,‘cubicspline’: Wrappers around the SciPy interpolation methods ofsimilar names. SeeNotes.

• ‘from_derivatives’: Refers toscipy.interpolate.BPoly.from_derivatives.

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

Axis to interpolate along. ForSeries this parameter is unusedand defaults to 0.

limitint, optional

Maximum number of consecutive NaNs to fill. Must be greater than0.

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’

raises ValueError iflimit_direction is ‘forward’ or ‘both’ and

method is ‘backfill’ or ‘bfill’.

raises ValueError iflimit_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 thisrestriction.

• 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.

Deprecated since version 2.1.0.

``**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 atsome or allNaN 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 ofsimilar names. These use the actual numerical values of the index.For more information on their behavior, see theSciPy documentation.

Examples

Filling inNaN in aSeries via linearinterpolation.

>>>s=pd.Series([0,1,np.nan,3])>>>s0    0.01    1.02    NaN3    3.0dtype: float64>>>s.interpolate()0    0.01    1.02    2.03    3.0dtype: float64

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

>>>s=pd.Series([0,2,np.nan,8])>>>s.interpolate(method='polynomial',order=2)0    0.0000001    2.0000002    4.6666673    8.000000dtype: float64

Fill the DataFrame forward (that is, going down) along each columnusing 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’ remainsNaN, because thereis 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     d0  0.0  NaN -1.0   1.01  NaN  2.0  NaN   NaN2  2.0  3.0  NaN   9.03  NaN  4.0 -4.0  16.0>>>df.interpolate(method='linear',limit_direction='forward',axis=0)     a    b    c     d0  0.0  NaN -1.0   1.01  1.0  2.0 -2.0   5.02  2.0  3.0 -3.0   9.03  2.0  4.0 -4.0  16.0

Using polynomial interpolation.

>>>df['d'].interpolate(method='polynomial',order=2)0     1.01     4.02     9.03    16.0Name: d, dtype: float64