How to determine whether a column/variable is numeric or not in Pandas/NumPy?

Question

Is there a better way to determine whether a variable in Pandas and/or NumPy is numeric or not ?

I have a self defined dictionary with dtypes as keys and numeric / not as values.

Answer 1

In pandas 0.20.2 you can do:

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 

Answer 2

You can use np.issubdtype to check if the dtype is a sub dtype of np.number. Examples:

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 

This works for numpy's dtypes but fails for pandas specific types like pd.Categorical as Thomas noted. If you are using categoricals is_numeric_dtype function from pandas is a better alternative than np.issubdtype.

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 

For multiple columns you can use np.vectorize:

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

And for selection, pandas now has select_dtypes:

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