In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.DataFrame({'col1': [0, pd.np.nan, pd.np.nan, 3, 4],
                   'col2': [pd.np.nan, 1, 2, pd.np.nan, pd.np.nan],
                   'col3': [4, pd.np.nan, pd.np.nan, 7, 10]})


    

In [3]:

    

print(df)


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   NaN   1.0   NaN
2   NaN   2.0   NaN
3   3.0   NaN   7.0
4   4.0   NaN  10.0

In [4]:

    

print(df.interpolate())


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   1.0   1.0   5.0
2   2.0   2.0   6.0
3   3.0   2.0   7.0
4   4.0   2.0  10.0

In [5]:

    

print(df.interpolate(axis=1))


    

    




   col1  col2  col3
0   0.0   2.0   4.0
1   NaN   1.0   1.0
2   NaN   2.0   2.0
3   3.0   5.0   7.0
4   4.0   7.0  10.0

In [6]:

    

print(df.interpolate(limit=1))


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   1.0   1.0   5.0
2   NaN   2.0   NaN
3   3.0   2.0   7.0
4   4.0   NaN  10.0

In [7]:

    

print(df.interpolate(limit=1, limit_direction='forward'))


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   1.0   1.0   5.0
2   NaN   2.0   NaN
3   3.0   2.0   7.0
4   4.0   NaN  10.0

In [8]:

    

print(df.interpolate(limit=1, limit_direction='backward'))


    

    




   col1  col2  col3
0   0.0   1.0   4.0
1   NaN   1.0   NaN
2   2.0   2.0   6.0
3   3.0   NaN   7.0
4   4.0   NaN  10.0

In [9]:

    

print(df.interpolate(limit=1, limit_direction='both'))


    

    




   col1  col2  col3
0   0.0   1.0   4.0
1   1.0   1.0   5.0
2   2.0   2.0   6.0
3   3.0   2.0   7.0
4   4.0   NaN  10.0

In [10]:

    

print(df.interpolate(limit_direction='both'))


    

    




   col1  col2  col3
0   0.0   1.0   4.0
1   1.0   1.0   5.0
2   2.0   2.0   6.0
3   3.0   2.0   7.0
4   4.0   2.0  10.0

In [11]:

    

print(df.interpolate(limit_area='inside'))


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   1.0   1.0   5.0
2   2.0   2.0   6.0
3   3.0   NaN   7.0
4   4.0   NaN  10.0

In [12]:

    

print(df.interpolate(limit_area='outside'))


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   NaN   1.0   NaN
2   NaN   2.0   NaN
3   3.0   2.0   7.0
4   4.0   2.0  10.0

In [13]:

    

print(df.interpolate(limit_area='outside', limit_direction='both'))


    

    




   col1  col2  col3
0   0.0   1.0   4.0
1   NaN   1.0   NaN
2   NaN   2.0   NaN
3   3.0   2.0   7.0
4   4.0   2.0  10.0

In [14]:

    

df_copy = df.copy()
df_copy.interpolate(inplace=True)
print(df_copy)


    

    




   col1  col2  col3
0   0.0   NaN   4.0
1   1.0   1.0   5.0
2   2.0   2.0   6.0
3   3.0   2.0   7.0
4   4.0   2.0  10.0

In [15]:

    

s = pd.Series([0, pd.np.nan, pd.np.nan, pd.np.nan, 4, pd.np.nan, pd.np.nan],
              index=[0, 2, 5, 6, 8, 10, 14])
print(s)


    

    




0     0.0
2     NaN
5     NaN
6     NaN
8     4.0
10    NaN
14    NaN
dtype: float64

In [16]:

    

print(s.interpolate())


    

    




0     0.0
2     1.0
5     2.0
6     3.0
8     4.0
10    4.0
14    4.0
dtype: float64

In [17]:

    

print(s.interpolate('index'))


    

    




0     0.0
2     1.0
5     2.5
6     3.0
8     4.0
10    4.0
14    4.0
dtype: float64

In [18]:

    

print(s.interpolate('values'))


    

    




0     0.0
2     1.0
5     2.5
6     3.0
8     4.0
10    4.0
14    4.0
dtype: float64

In [19]:

    

s.index = list('abcdefg')
print(s)


    

    




a    0.0
b    NaN
c    NaN
d    NaN
e    4.0
f    NaN
g    NaN
dtype: float64

In [20]:

    

print(s.interpolate())


    

    




a    0.0
b    1.0
c    2.0
d    3.0
e    4.0
f    4.0
g    4.0
dtype: float64

In [21]:

    

# print(s.interpolate('values'))
# TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'


    

In [22]:

    

s = pd.Series([0, 10, pd.np.nan, pd.np.nan, 4, pd.np.nan, pd.np.nan],
              index=[0, 2, 5, 6, 8, 10, 14])


    

In [23]:

    

print(s.interpolate('spline', order=2))


    

    




0      0.00
2     10.00
5     13.75
6     12.00
8      4.00
10   -10.00
14   -56.00
dtype: float64

In [24]:

    

s.index = range(7)


    

In [25]:

    

print(s.interpolate('spline', order=2))


    

    




0     0.0
1    10.0
2    14.0
3    12.0
4     4.0
5   -10.0
6   -30.0
dtype: float64

In [26]:

    

s.index = list('abcdefg')


    

In [27]:

    

# print(s.interpolate('spline', order=2))
# TypeError: unsupported operand type(s) for -: 'str' and 'str'