用多个条件过滤 Pandas DataFrame

  1. 根据多个条件使用索引过滤 DataFrame 的元素
  2. 使用 query() 方法基于多个条件过滤 DataFrame 的元素

本教程解释了如何基于多个条件从 DataFrame 中过滤元素。

我们将在本文中使用下面的 DataFrame。

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Tesla","Moderna Inc","Facebook","Boeing"],
    'Price': [835,112,267,209],
    'Sector':["Technology","Health Technology","Technology","Aircraft"]
})

print(stocks_df)

输出:

         Stock  Price             Sector
0        Tesla    835         Technology
1  Moderna Inc    112  Health Technology
2     Facebook    267         Technology
3       Boeing    209           Aircraft

根据多个条件使用索引过滤 DataFrame 的元素

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Tesla","Moderna Inc","Facebook","Boeing"],
    'Price': [835,112,267,209],
    'Sector':["Technology","Health Technology","Technology","Aircraft"]
})

print("Stocks DataFrame:")
print(stocks_df,"\n")

reqd_stocks = stocks_df[(stocks_df.Sector == "Technology") & (stocks_df.Price <500)]

print("The stocks of technology sector with price less than 500 are:")
print(reqd_stocks)

输出:

Stocks DataFrame:
         Stock  Price             Sector
0        Tesla    835         Technology
1  Moderna Inc    112  Health Technology
2     Facebook    267         Technology
3       Boeing    209           Aircraft

The stocks of technology sector with price less than 500 are:
      Stock  Price      Sector
2  Facebook    267  Technology

它过滤了 stocks_df 中的所有元素,其中 Sector 列的值是 TechnologyPrice 列的值小于 500。

我们在 [] 内指定条件,用&|运算符连接条件,根据多个条件对数值进行索引。&运算符代表逻辑,意思是这两个条件必须为真才能选择一个元素。|运算符代表逻辑,意思是如果满足任何条件就可以选择一个元素。

使用 query() 方法基于多个条件过滤 DataFrame 的元素

我们将由&|运算符连接的多个条件作为参数传递给 query() 方法。

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Tesla","Moderna Inc","Facebook","Boeing"],
    'Price': [835,112,267,209],
    'Sector':["Technology","Health Technology","Technology","Aircraft"]
})
print("Stocks DataFrame:")
print(stocks_df,"\n")

reqd_stocks = stocks_df.query("Sector == 'Technology' & Price <500")

print("The stocks of technology sector with price less than 500 are:")
print(reqd_stocks)

输出:

Stocks DataFrame:
         Stock Price             Sector
0        Tesla    835         Technology
1 Moderna Inc    112 Health Technology
2     Facebook    267         Technology
3       Boeing    209           Aircraft

The stocks of technology sector with price less than 500 are:
      Stock Price      Sector
2 Facebook    267 Technology

相关文章 - Pandas Filter

  • Pandas loc vs iloc
  • Pandas DataFrame 排除列