Examples
Filling in NaN in a Series via linear interpolation.

In [1]:
import numpy as np
import pandas as pd
In [2]:
s = pd.Series([0, 2, np.nan, 5])
s
Out[2]:
0    0.0
1    2.0
2    NaN
3    5.0
dtype: float64

In [3]:
s.interpolate()
Out[3]:
0    0.0
1    2.0
2    3.5
3    5.0
dtype: float64

Filling in NaN in a Series by padding, but filling at most two consecutive NaN at a time.

In [4]:
s = pd.Series([np.nan, "single_one", np.nan,
               "fill_two_more", np.nan, np.nan,
               3.71, np.nan])
s
Out[4]:
0              NaN
1       single_one
2              NaN
3    fill_two_more
4              NaN
5              NaN
6             3.71
7              NaN
dtype: object
In [5]:
s.interpolate(method='pad', limit=2)
Out[5]:
0              NaN
1       single_one
2       single_one
3    fill_two_more
4    fill_two_more
5    fill_two_more
6             3.71
7             3.71
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).

In [6]:
s = pd.Series([0, 4, np.nan, 8])
s.interpolate(method='polynomial', order=2)
Out[6]:
0    0.000000
1    4.000000
2    6.666667
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 ‘p’ is interpolated differently, because there is no entry
after it to use for interpolation. Note how the first entry in column ‘q’ remains NaN, because
there is no entry before it to use for interpolation.

In [7]:
df = pd.DataFrame([(0.0, np.nan, -2.0, 2.0),
                   (np.nan, 3.0, np.nan, np.nan),
                   (2.0, 3.0, np.nan, 7.0),
                   (np.nan, 4.0, -4.0, 16.0)],
                  columns=list('pqrs'))
df
Out[7]:
p q r s
0 0.0 NaN -2.0 2.0
1 NaN 3.0 NaN NaN
2 2.0 3.0 NaN 7.0
3 NaN 4.0 -4.0 16.0
In [8]:
df.interpolate(method='linear', limit_direction='forward', axis=0)
Out[8]:
p q r s
0 0.0 NaN -2.000000 2.0
1 1.0 3.0 -2.666667 4.5
2 2.0 3.0 -3.333333 7.0
3 2.0 4.0 -4.000000 16.0

Using polynomial interpolation.

In [9]:
df['s'].interpolate(method='polynomial', order=2)
Out[9]:
0     2.000000
1     2.333333
2     7.000000
3    16.000000
Name: s, dtype: float64