python : pd.to_numeric() VS astype(np.float64)

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(10**5, 10**7, (5,3)), columns=list('abc'), dtype=np.int64)

df

	a	b	c
0	2368596	282593	7649457
1	6486779	5348256	790672
2	8468404	4682970	2904873
3	2271514	2908642	9272301
4	7811256	3652968	6715015

df.dtypes

a    int64
b    int64
c    int64
dtype: object

df['a'] = df['a'].astype(float)
df.dtypes

a    float64
b      int64
c      int64
dtype: object

## 이렇게 온전한 숫자 타입에서 다른 숫자타입으로 변경시에는 'astype'을 사용 
## 하지만 온전한 숫자 타입이 아닐 경우에는 ??

df.loc[1, 'b'] = 'xxxxx'

df

	a	b	c
0	2368596.0	282593	7649457
1	6486779.0	xxxxx	790672
2	8468404.0	4682970	2904873
3	2271514.0	2908642	9272301
4	7811256.0	3652968	6715015

df['b'] = df['b'].astype(float)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-11-06b413f6e48a> in <module>
----> 1 df['b'] = df['b'].astype(float)


~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
   5880             # else, only a single dtype is given
   5881             new_data = self._data.astype(
-> 5882                 dtype=dtype, copy=copy, errors=errors, **kwargs
   5883             )
   5884             return self._constructor(new_data).__finalize__(self)


~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in astype(self, dtype, **kwargs)
    579 
    580     def astype(self, dtype, **kwargs):
--> 581         return self.apply("astype", dtype=dtype, **kwargs)
    582 
    583     def convert(self, **kwargs):


~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
    436                     kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
    437 
--> 438             applied = getattr(b, f)(**kwargs)
    439             result_blocks = _extend_blocks(applied, result_blocks)
    440 


~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors, values, **kwargs)
    557 
    558     def astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
--> 559         return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
    560 
    561     def _astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):


~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/blocks.py in _astype(self, dtype, copy, errors, values, **kwargs)
    641                     # _astype_nansafe works fine with 1-d only
    642                     vals1d = values.ravel()
--> 643                     values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
    644 
    645                 # TODO(extension)


~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
    727     if copy or is_object_dtype(arr) or is_object_dtype(dtype):
    728         # Explicit copy, or required since NumPy can't view from / to object.
--> 729         return arr.astype(dtype, copy=True)
    730 
    731     return arr.view(dtype)


ValueError: could not convert string to float: 'xxxxx'

## 이럴때에 to_numeric 사용

df['b'] = pd.to_numeric(df['b'], errors='coerce')

df

	a	b	c
0	2368596.0	282593.0	7649457
1	6486779.0	NaN	790672
2	8468404.0	4682970.0	2904873
3	2271514.0	2908642.0	9272301
4	7811256.0	3652968.0	6715015

df.dtypes

a    float64
b    float64
c      int64
dtype: object