创建从分析的数据框派生的两个列表

Posted 2023-03-12

【中文标题】创建从分析的数据框派生的两个列表

【英文标题】：Creating two lists derived from an analysed dataframe

【发布时间】：2022-01-18 21:00:52

【问题描述】：

我正在创建一个函数，该函数可以检查我的数据集的多个条件，然后返回两个列表。

清单 1：执行 X 的项目

清单 2：要执行的项目 Y

样本数据：

   Product   Variable     Value
0      A       Sell       0.1
1      B       Keep      -1.2
2      C       Sell       0.3
3      D       Swap       0.1
4      E       Swap       0.1

理想的结果：

Sell; A, C 
Swap; 'Nothing to swap this week'

运行时出错：

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

到目前为止我的代码：

def weekly_forcasting(df):

    a = df['Swap or Sell']
    b = df['Current Value']

    if (a == 'Sell') and (b > 0).any():
        return ('Product_Label')
        if (a == 'Sell') and (b < 0).any():
          return 'Nothing to sell this week'
        if (a == 'Swap') and (b > 0).any():
          return 'Nothing to swap this week'
        if (a == 'Swap') and (b < 0).any():
          return ('Product_Label')

【参考方案1】：

b = df['Current Value'] 使b 成为错误消息所暗示的系列。所以理想情况下你想使用.loc，例如你可能想要

selling_list = (
    df.loc[
        (df["Swap or Sell"]=="Sell") & (df["Current Value"]>0)
    ]["Product_Label"].tolist() if 
    len(df.loc[
        (df["Swap or Sell"]=="Sell") & (df["Current Value"]>0)
    ]["Product_Label"].tolist()) > 0 
    else "nothing to sell this week"
)

同样你可以定义swapping_list并做一个return selling_list, swapping_list

【参考方案2】：

尝试使用numpy.select

import numpy as np

# create a list of condtions
conditions = [
    (df['Swap or Sell'] == 'Sell') & (df['Current Value'] > 0),
    (df['Swap or Sell'] == 'Sell') & (df['Current Value'] < 0),
    (df['Swap or Sell'] == 'Swap') & (df['Current Value'] > 0),
    (df['Swap or Sell'] == 'Swap') & (df['Current Value'] < 0),
]
# create a list of chices that mtach a statisifed condition
choices = [
    df['Product_Label'],
    'There are no Sell recommendations today',
    'There are no Swap recommendations today',
    df['Product_Label'],
]

# create a new by using numpy.select
df['data'] = np.select(conditions, choices, 'Keep')

然后只需使用布尔索引访问您想要的信息

df.loc[df['Swap or Sell'] == 'Sell', 'data']