pandas 中语法 `df[&#column&#] = expression` 的解释

import pandas as pddf = pd.DataFrame({'a': [1, 2, 3]})print(df)# 输出：#    a# 0  1# 1  2# 2  3# 创建一个名为'b'的新列，所有值都设置为0df['b'] = 0print(df)# 输出：#    a  b# 0  1  0# 1  2  0# 2  3  0

df['b'] = [4, 5, 6]  # 替换'b'列的值print(df)# 输出：#    a  b# 0  1  4# 1  2  5# 2  3  6

df['c'] = df['a'] + df['b']  # 创建'c'列，值为'a'和'b'的和print(df)# 输出：#    a  b  c# 0  1  4  5# 1  2  5  7# 2  3  6  9

df['d'] = df['a'].apply(lambda x: 'even' if x % 2 == 0 else 'odd')print(df)# 输出：#    a  b  c     d# 0  1  4  5   odd# 1  2  5  7  even# 2  3  6  9   odd

df['e'] = (df['a'] + df['b']) * df['c']print(df)# 输出：#    a  b  c     d    e# 0  1  4  5   odd   25# 1  2  5  7  even   49# 2  3  6  9   odd   81

df['f'] = df['a'] ** 2 + df['b'] ** 2  # 快速的向量化计算print(df)# 输出：#    a  b  c     d    e   f# 0  1  4  5   odd   25  17# 1  2  5  7  even   49  29# 2  3  6  9   odd   81  45

import numpy as npdf['g'] = np.where(df['a'] > 2, 'high', 'low')print(df)# 输出：#    a  b  c     d    e   f     g# 0  1  4  5   odd   25  17   low# 1  2  5  7  even   49  29   low# 2  3  6  9   odd   81  45  high

def custom_function(row):    return row['a'] * row['b']df['h'] = df.apply(custom_function, axis=1)print(df)# 输出： (输出结果会根据之前的操作结果变化)

df['i'] = df['a'].add(df['b']).mul(df['c'])print(df)# 输出： (输出结果会根据之前的操作结果变化)

df = df.assign(    j=df['a'] + df['b'],    k=lambda x: x['j'] * 2)print(df)# 输出： (输出结果会根据之前的操作结果变化)

columns_to_add = ['l', 'm']for col in columns_to_add:    df[col] = df['a'] + df['b']print(df)# 输出： (输出结果会根据之前的操作结果变化)

mapping = {1: 'Low', 2: 'Medium', 3: 'High'}df['N'] = df['a'].map(mapping) # 假设'a'列存在print(df)# 输出： (输出结果会根据之前的操作结果变化)