When to apply(pd.to_numeric) and when to astype(np.float64) in python?
If you already have numeric dtypes (int8|16|32|64
,float64
,boolean
) you can convert it to another "numeric" dtype using Pandas .astype() method.
Demo:
In [90]: df = pd.DataFrame(np.random.randint(10**5,10**7,(5,3)),columns=list('abc'), dtype=np.int64)
In [91]: df
Out[91]:
a b c
0 9059440 9590567 2076918
1 5861102 4566089 1947323
2 6636568 162770 2487991
3 6794572 5236903 5628779
4 470121 4044395 4546794
In [92]: df.dtypes
Out[92]:
a int64
b int64
c int64
dtype: object
In [93]: df['a'] = df['a'].astype(float)
In [94]: df.dtypes
Out[94]:
a float64
b int64
c int64
dtype: object
It won't work for object
(string) dtypes, that can't be converted to numbers:
In [95]: df.loc[1, 'b'] = 'XXXXXX'
In [96]: df
Out[96]:
a b c
0 9059440.0 9590567 2076918
1 5861102.0 XXXXXX 1947323
2 6636568.0 162770 2487991
3 6794572.0 5236903 5628779
4 470121.0 4044395 4546794
In [97]: df.dtypes
Out[97]:
a float64
b object
c int64
dtype: object
In [98]: df['b'].astype(float)
...
skipped
...
ValueError: could not convert string to float: 'XXXXXX'
So here we want to use pd.to_numeric() method:
In [99]: df['b'] = pd.to_numeric(df['b'], errors='coerce')
In [100]: df
Out[100]:
a b c
0 9059440.0 9590567.0 2076918
1 5861102.0 NaN 1947323
2 6636568.0 162770.0 2487991
3 6794572.0 5236903.0 5628779
4 470121.0 4044395.0 4546794
In [101]: df.dtypes
Out[101]:
a float64
b float64
c int64
dtype: object