使用isna()方法(或者它的别名isnull()，这也兼容较旧的pandas版本< 0.21.0)，然后求和来计算NaN值。其中一列:

>>> s = pd.Series([1,2,3, np.nan, np.nan])

>>> s.isna().sum()   # or s.isnull().sum() for older pandas versions
2

对于一些列，这也适用:

>>> df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})

>>> df.isna().sum()
a    1
b    2
dtype: int64

你可以试试:

In [1]: s = pd.DataFrame('a'=[1,2,5, np.nan, np.nan,3],'b'=[1,3, np.nan, np.nan,3,np.nan])

In [4]: s.isna().sum()   
Out[4]: out = {'a'=2, 'b'=3} # the number of NaN values for each column

如果需要nan的总和:

In [5]: s.isna().sum().sum()
Out[6]: out = 5  #the inline sum of Out[4] 

import numpy as np
import pandas as pd

raw_data = {'first_name': ['Jason', np.nan, 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', np.nan, np.nan, 'Milner', 'Cooze'], 
        'age': [22, np.nan, 23, 24, 25], 
        'sex': ['m', np.nan, 'f', 'm', 'f'], 
        'Test1_Score': [4, np.nan, 0, 0, 0],
        'Test2_Score': [25, np.nan, np.nan, 0, 0]}
results = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'sex', 'Test1_Score', 'Test2_Score'])

results 
'''
  first_name last_name   age  sex  Test1_Score  Test2_Score
0      Jason    Miller  22.0    m          4.0         25.0
1        NaN       NaN   NaN  NaN          NaN          NaN
2       Tina       NaN  23.0    f          0.0          NaN
3       Jake    Milner  24.0    m          0.0          0.0
4        Amy     Cooze  25.0    f          0.0          0.0
'''

您可以使用以下函数，它将在Dataframe中提供输出

零值 缺失值 占总额的% 总零缺失值 总零缺失值% 数据类型

只需复制和粘贴下面的函数，并通过传递你的熊猫数据帧来调用它

def missing_zero_values_table(df):
        zero_val = (df == 0.00).astype(int).sum(axis=0)
        mis_val = df.isnull().sum()
        mis_val_percent = 100 * df.isnull().sum() / len(df)
        mz_table = pd.concat([zero_val, mis_val, mis_val_percent], axis=1)
        mz_table = mz_table.rename(
        columns = {0 : 'Zero Values', 1 : 'Missing Values', 2 : '% of Total Values'})
        mz_table['Total Zero Missing Values'] = mz_table['Zero Values'] + mz_table['Missing Values']
        mz_table['% Total Zero Missing Values'] = 100 * mz_table['Total Zero Missing Values'] / len(df)
        mz_table['Data Type'] = df.dtypes
        mz_table = mz_table[
            mz_table.iloc[:,1] != 0].sort_values(
        '% of Total Values', ascending=False).round(1)
        print ("Your selected dataframe has " + str(df.shape[1]) + " columns and " + str(df.shape[0]) + " Rows.\n"      
            "There are " + str(mz_table.shape[0]) +
              " columns that have missing values.")
#         mz_table.to_excel('D:/sampledata/missing_and_zero_values.xlsx', freeze_panes=(1,0), index = False)
        return mz_table

missing_zero_values_table(results)

输出

Your selected dataframe has 6 columns and 5 Rows.
There are 6 columns that have missing values.

             Zero Values  Missing Values  % of Total Values  Total Zero Missing Values  % Total Zero Missing Values Data Type
last_name              0               2               40.0                          2                         40.0    object
Test2_Score            2               2               40.0                          4                         80.0   float64
first_name             0               1               20.0                          1                         20.0    object
age                    0               1               20.0                          1                         20.0   float64
sex                    0               1               20.0                          1                         20.0    object
Test1_Score            3               1               20.0                          4                         80.0   float64

如果你想保持简单，那么你可以使用下面的函数来获取%中缺失的值

def missing(dff):
    print (round((dff.isnull().sum() * 100/ len(dff)),2).sort_values(ascending=False))


missing(results)
'''
Test2_Score    40.0
last_name      40.0
Test1_Score    20.0
sex            20.0
age            20.0
first_name     20.0
dtype: float64
'''

另一个尚未被建议的简单选项是，为了只计算NaN，将在形状中添加以返回具有NaN的行数。

df[df['col_name'].isnull()]['col_name'].shape

自从pandas 0.14.1以来，我的建议在value_counts方法中有一个关键字参数已经实现:

import pandas as pd
df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})
for col in df:
    print df[col].value_counts(dropna=False)

2     1
 1     1
NaN    1
dtype: int64
NaN    2
 1     1
dtype: int64

如果你正在使用Jupyter笔记本，如何....

 %%timeit
 df.isnull().any().any()

or

 %timeit 
 df.isnull().values.sum()

或者，数据中是否存在nan，如果有，在哪里?

 df.isnull().any()