pandas如何统计某一列或某一行的缺失值数目

import pandas as pd

# 首先导入数据
df = pd.read_csv('123.csv' , encoding='gbk')

# 计算data每一行有多少个缺失值的值，即按行统计缺失值
rows_null = df.isnull().sum(axis=1) 

# 下面则是按列统计缺失值
col_null = df.isnull().sum(axis=0)

#统计整个df的缺失值
all_null = df.isnull().sum().sum()

# 统计某一列的缺失值
idx_null = df['列名'].isnull().sum(axis=0)

import pandas as pd

# 首先导入数据
df = pd.read_csv('123.csv' , encoding='gbk')

# 计算data每一行有多少个非空的值，即按行统计非空值
rows_not_null = df.count(axis=1) 

# 下面则是按列统计非空值
cols_not_null = df.count(axis=0)
cols_null = df.shape[1] - cols_not_null

# 统计某一列的非空值
col_not_null = df['列名'].count(axis=0)

def missing_values(dataframe):
    missing_ratio = (dataframe.isnull().sum() / len(dataframe))*100
    missing_ratio = missing_ratio.drop(missing_ratio[missing_ratio == 0].index).sort_values(ascending=False)
    missing_count = dataframe.isnull().sum()
    missing_count = missing_count.drop(missing_count[missing_count == 0].index).sort_values(ascending=False)
    info = pd.DataFrame({'Missing Ratio': missing_ratio, 'Missing Count': missing_count})
    return info