如何在DataFrame中对指定列执行除法运算？（含除以100、90场景）

Hey there! Let's break down how to handle both of your DataFrame column division needs clearly:

1. 将DataFrame中选中的列除以100 #

Depending on whether you're targeting a single column or multiple columns, here are the most efficient approaches:

• Single column: If you want to divide just one column (say target_col) by 100, you can directly assign the result back to the column:

  # 修改原DataFrame
  df['target_col'] = df['target_col'] / 100
  
  # 或者创建副本，不修改原数据
  df_copy = df.copy()
  df_copy['target_col'] = df_copy['target_col'] / 100
  

• Multiple columns: For several columns (e.g., col_a, col_b, col_c), use a list to select them and perform the division in one go:

  selected_cols = ['col_a', 'col_b', 'col_c']
  # 修改原DataFrame
  df[selected_cols] = df[selected_cols] / 100
  
  # 创建副本的版本
  df_copy = df.copy()
  df_copy[selected_cols] = df_copy[selected_cols] / 100
  

注意：如果你的列包含非数值类型（比如字符串），先转换为数值类型再操作，否则会报错：

df[selected_cols] = df[selected_cols].astype(float)

2. 将df1中的col2、col3列除以90 #

This is a specific case of the multi-column scenario above. Here's the direct code you can use:

方式1：直接修改原DataFrame df1 #

# 直接对指定列执行除法并赋值
df1[['col2', 'col3']] = df1[['col2', 'col3']] / 90

方式2：保留原DataFrame，生成修改后的副本 #

If you don't want to alter the original df1, create a copy first:

df1_modified = df1.copy()
df1_modified[['col2', 'col3']] = df1_modified[['col2', 'col3']] / 90

同样，如果col2或col3不是数值类型，先转换：

df1[['col2', 'col3']] = df1[['col2', 'col3']].astype(float)

内容的提问来源于stack exchange，提问作者anil tiwari