pandas DataFrame:获取列中的NaN、None、空字符串/列表等单元格

import pandas as pd

# 创建一个DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, None, 30],
        'City': ['New York', '', 'London']}
df = pd.DataFrame(data)

# 获取Age列中的NaN单元格
nan_cells = df['Age'].isnull()
print(nan_cells)

0    False
1     True
2    False
Name: Age, dtype: bool

import pandas as pd

# 创建一个DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, None, 30],
        'City': ['New York', '', 'London']}
df = pd.DataFrame(data)

# 填充Age列中的NaN单元格为0
df['Age'].fillna(0, inplace=True)
print(df)

      Name   Age      City
0    Alice  25.0  New York
1      Bob   0.0          
2  Charlie  30.0    London

import pandas as pd

# 创建一个DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, None, 30],
        'City': ['New York', '', 'London']}
df = pd.DataFrame(data)

# 删除包含NaN单元格的行
df.dropna(inplace=True)
print(df)

    Name   Age    City
0  Alice  25.0    New York
2  Charlie  30.0   London