Pandas / NumPy에서 열 / 변수가 숫자인지 여부를 확인하는 방법은 무엇입니까?

Pandas / NumPy에서 열 / 변수가 숫자인지 여부를 확인하는 방법은 무엇입니까?

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인지 여부를 변수를 결정하는 더 좋은 방법이 있나요 Pandas및 / 또는 NumPy입니다 numeric여부는?

나는 정의 자체가 dictionary가진 dtypes키와 같은 numeric/ not값으로한다.

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np.issubdtypedtype이의 하위 dtype인지 확인 하는 데 사용할 수 있습니다 np.number. 예 :

np.issubdtype(arr.dtype, np.number)  # where arr is a numpy array
np.issubdtype(df['X'].dtype, np.number)  # where df['X'] is a pandas Series

이것은 numpy의 dtypes에서 작동하지만 Thomas가 지적한 것처럼 pd.Categorical과 같은 팬더 특정 유형에서는 실패합니다 . is_numeric_dtypepandas의 categoricals 함수를 사용 하는 경우 np.issubdtype보다 나은 대안입니다.

df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 
                   'C': [1j, 2j, 3j], 'D': ['a', 'b', 'c']})
df
Out: 
   A    B   C  D
0  1  1.0  1j  a
1  2  2.0  2j  b
2  3  3.0  3j  c

df.dtypes
Out: 
A         int64
B       float64
C    complex128
D        object
dtype: object

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np.issubdtype(df['A'].dtype, np.number)
Out: True

np.issubdtype(df['B'].dtype, np.number)
Out: True

np.issubdtype(df['C'].dtype, np.number)
Out: True

np.issubdtype(df['D'].dtype, np.number)
Out: False

여러 열의 경우 np.vectorize를 사용할 수 있습니다.

is_number = np.vectorize(lambda x: np.issubdtype(x, np.number))
is_number(df.dtypes)
Out: array([ True,  True,  True, False], dtype=bool)

그리고 선택을 위해 pandas에는 select_dtypes다음 이 있습니다 .

df.select_dtypes(include=[np.number])
Out: 
   A    B   C
0  1  1.0  1j
1  2  2.0  2j
2  3  3.0  3j

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에서 pandas 0.20.2당신이 할 수 있습니다 :

import pandas as pd
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype

df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': [1.0, 2.0, 3.0]})

is_string_dtype(df['A'])
>>>> True

is_numeric_dtype(df['B'])
>>>> True

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Based on @jaime's answer in the comments, you need to check .dtype.kind for the column of interest. For example;

>>> import pandas as pd
>>> df = pd.DataFrame({'numeric': [1, 2, 3], 'not_numeric': ['A', 'B', 'C']})
>>> df['numeric'].dtype.kind in 'bifc'
>>> True
>>> df['not_numeric'].dtype.kind in 'bifc'
>>> False

NB bifc is b bool, i int, f float, c complex - I'm not sure what u might be.

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How about just checking type for one of the values in the column? We've always had something like this:

isinstance(x, (int, long, float, complex))

When I try to check the datatypes for the columns in below dataframe, I get them as 'object' and not a numerical type I'm expecting:

df = pd.DataFrame(columns=('time', 'test1', 'test2'))
for i in range(20):
    df.loc[i] = [datetime.now() - timedelta(hours=i*1000),i*10,i*100]
df.dtypes

time     datetime64[ns]
test1            object
test2            object
dtype: object

When I do the following, it seems to give me accurate result:

isinstance(df['test1'][len(df['test1'])-1], (int, long, float, complex))

returns

True

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This is a pseudo-internal method to return only the numeric type data

In [27]: df = DataFrame(dict(A = np.arange(3), 
                             B = np.random.randn(3), 
                             C = ['foo','bar','bah'], 
                             D = Timestamp('20130101')))

In [28]: df
Out[28]: 
   A         B    C                   D
0  0 -0.667672  foo 2013-01-01 00:00:00
1  1  0.811300  bar 2013-01-01 00:00:00
2  2  2.020402  bah 2013-01-01 00:00:00

In [29]: df.dtypes
Out[29]: 
A             int64
B           float64
C            object
D    datetime64[ns]
dtype: object

In [30]: df._get_numeric_data()
Out[30]: 
   A         B
0  0 -0.667672
1  1  0.811300
2  2  2.020402

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Just to add to all other answers, one can also use df.info() to get whats the data type of each column.

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You can also try:

df_dtypes = np.array(df.dtypes)
df_numericDtypes= [x.kind in 'bifc' for x in df_dtypes]

It returns a list of booleans: True if numeric, False if not.

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Pandas has select_dtype function. You can easily filter your columns on int64, and float64 like this:

df.select_dtypes(include=['int64','float64'])

ReferenceURL : https://stackoverflow.com/questions/19900202/how-to-determine-whether-a-column-variable-is-numeric-or-not-in-pandas-numpy