pandas如何統計某一列或某一行的缺失值數目

import pandas as pd

# 首先導入數據
df = pd.read_csv('123.csv' , encoding='gbk')

# 計算data每一行有多少個缺失值的值，即按行統計缺失值
rows_null = df.isnull().sum(axis=1) 

# 下面則是按列統計缺失值
col_null = df.isnull().sum(axis=0)

#統計整個df的缺失值
all_null = df.isnull().sum().sum()

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

import pandas as pd

# 首先導入數據
df = pd.read_csv('123.csv' , encoding='gbk')

# 計算data每一行有多少個非空的值，即按行統計非空值
rows_not_null = df.count(axis=1) 

# 下面則是按列統計非空值
cols_not_null = df.count(axis=0)
cols_null = df.shape[1] - cols_not_null

# 統計某一列的非空值
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