pandas.DataFrame.interpolate — pandas 2.2.3 documentation (original) (raw)

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 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’, ‘barycentric’, ‘polynomial’: Passed toscipy.interpolate.interp1d, whereas ‘spline’ is passed toscipy.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 splineinstead 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 toscipy.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.

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’

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.

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 at some or all NaN values or None if inplace=True.

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 theSciPy 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