如何检查熊猫数据框是否仅包含数字列？

Question

I want to check every column in a dataframe whether it contains only numeric. How can i find it.

Answer 1

You can check that using to_numeric and coercing errors:

pd.to_numeric(df['column'], errors='coerce').notnull().all()

For all columns, you can iterate through columns or just use apply

df.apply(lambda s: pd.to_numeric(s, errors='coerce').notnull().all())

---

Eg

df = pd.DataFrame({'col' : [1,2, 10, np.nan, 'a'], 
                   'col2': ['a', 10, 30, 40 ,50],
                   'col3': [1,2,3,4,5.0]})

Outputs

col     False
col2    False
col3     True
dtype: bool

Answer 2

You can draw a True / False comparison using isnumeric()

Example:

 >>> df
       A      B
0      1      1
1    NaN      6
2    NaN    NaN
3      2      2
4    NaN    NaN
5      4      4
6   some   some
7  value  other

Results:

>>> df.A.str.isnumeric()
0     True
1      NaN
2      NaN
3     True
4      NaN
5     True
6    False
7    False
Name: A, dtype: object

# df.B.str.isnumeric()

with apply() method which seems more robust in case you need corner to corner comparison:

DataFrame having two different columns one with mixed type another with numbers only for test:

>>> df
       A   B
0      1   1
1    NaN   6
2    NaN  33
3      2   2
4    NaN  22
5      4   4
6   some  66
7  value  11

Result:

>>> df.apply(lambda x: x.str.isnumeric())
       A     B
0   True  True
1    NaN  True
2    NaN  True
3   True  True
4    NaN  True
5   True  True
6  False  True
7  False  True

Another example:

Let's consider the below dataframe with different data-types as follows..

>>> df
   num  rating    name  age
0    0    80.0  shakir   33
1    1   -22.0   rafiq   37
2    2   -10.0     dev   36
3  num     1.0   suraj   30

Based on the comment from OP on this answer, where it has negative value and 0's in it.

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

>>> df._get_numeric_data()
   rating  age
0    80.0   33
1   -22.0   37
2   -10.0   36
3     1.0   30

OR

2- there is an option to use method select_dtypes in module pandas.core.frame which return a subset of the DataFrame's columns based on the column dtypes . One can use Parameters with include, exclude options.

>>> df.select_dtypes(include=['int64','float64']) # choosing int & float
   rating  age
0    80.0   33
1   -22.0   37
2   -10.0   36
3     1.0   30

>>> df.select_dtypes(include=['int64'])  # choose int
   age
0   33
1   37
2   36
3   30

Answer 3

This will return True if all columns are numeric, False otherwise.

df.shape[1] == df.select_dtypes(include=np.number).shape[1]

To select numeric columns:

new_df = df.select_dtypes(include=np.number)

Answer 4

Let's say you have a dataframe called df , if you do:

df.select_dtypes(include=["float", 'int'])

This will return all the numeric columns, you can check if this is the same as the original df .

Otherwise, you can also use the exclude parameter:

df.select_dtypes(exclude=["float", 'int'])

and check if this gives you an empty dataframe.

Answer 5

The accepted answers seem bit overkill, as they sub-select the entire dataframe.

To check types only metadata should be used, which can be done with pd.api.types.is_numeric_dtype .

import pandas as pd
df = pd.DataFrame(data=[[1,'a']],columns=['numeruc_col','string_col'])

print(df.columns[list(map(pd.api.types.is_numeric_dtype,df.dtypes))]) # one way
print(df.dtypes.map(pd.api.types.is_numeric_dtype)) # another way