Pandas: dtypeがobjectのカンマ区切り文字をintにしたい

Question

環境：win10,python3.6

データフレーム内にあるカンマ区切り有のobjectデータを、カンマ区切りなしのintに変換したいと考えています。

#例：　"20,000" →　20000

import pandas as pd
data1 = pd.read_csv(r"data.csv",engine = "python")

print(data1["A"].dtype)
#out:object, "20,000"のような形

data1["A"] = data1["A"].replace(',','').astype(np.int)

すると、以下のようなエラーを得ました。

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-60-bef64f82d78f> in <module>()
----> 1 data1["A"] = data1["A"].replace(',','').astype(np.int)
      
/usr/local/lib/python3.6/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
    116                 else:
    117                     kwargs[new_arg_name] = new_arg_value
--> 118             return func(*args, **kwargs)
    119         return wrapper
    120     return _deprecate_kwarg

/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
   4002         # else, only a single dtype is given
   4003         new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,
-> 4004                                      **kwargs)
   4005         return self._constructor(new_data).__finalize__(self)
   4006 

/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py in astype(self, dtype, **kwargs)
   3460 
   3461     def astype(self, dtype, **kwargs):
-> 3462         return self.apply('astype', dtype=dtype, **kwargs)
   3463 
   3464     def convert(self, **kwargs):

/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
   3327 
   3328             kwargs['mgr'] = self
-> 3329             applied = getattr(b, f)(**kwargs)
   3330             result_blocks = _extend_blocks(applied, result_blocks)
   3331 

/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py in astype(self, dtype, copy, errors, values, **kwargs)
    542     def astype(self, dtype, copy=False, errors='raise', values=None, **kwargs):
    543         return self._astype(dtype, copy=copy, errors=errors, values=values,
--> 544                             **kwargs)
    545 
    546     def _astype(self, dtype, copy=False, errors='raise', values=None,

/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py in _astype(self, dtype, copy, errors, values, klass, mgr, **kwargs)
    623 
    624                 # _astype_nansafe works fine with 1-d only
--> 625                 values = astype_nansafe(values.ravel(), dtype, copy=True)
    626                 values = values.reshape(self.shape)
    627 

/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy)
    690     elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
    691         # work around NumPy brokenness, #1987
--> 692         return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
    693 
    694     if dtype.name in ("datetime64", "timedelta64"):

pandas/_libs/lib.pyx in pandas._libs.lib.astype_intsafe()

pandas/_libs/src/util.pxd in util.set_value_at_unsafe()

ValueError: invalid literal for int() with base 10: '97,750'

どう直したらいいのかよくわからないので、教えて下さると幸いです。よろしくお願いいたします。

Answer 1

read_csvにthousands=','をオプションとして与える方法もあります。
ただし、これは3桁区切りの数値に適用できるもので、ランダムにカンマが含まれる場合はうまく機能しません

Answer 2

これで動くはずです。

data1["A"] = data1["A"].str.replace(',','').astype(np.int)