numpy 基于多个条件更新列值

nxagd54h 于 2022-11-10 发布在其他

关注(0)|答案(2)|浏览(123)

在下面的DataFrame中

df = pd.DataFrame([('Ve_Paper', 'Buy', '-','Canada',np.NaN),
                  ('Ve_Gasoline', 'Sell', 'Done','Britain',np.NaN),
                  ('Ve_Water', 'Sell','-','Canada,np.NaN),
                  ('Ve_Plant', 'Buy', 'Good','China',np.NaN),
                  ('Ve_Soda', 'Sell', 'Process','Germany',np.NaN)], columns=['Name', 'Action','Status','Country','Value'])

我正在尝试根据以下条件更新Value列如果Action为Sell检查Status是否不是-如果为真，则需要将Country的前两个字符更新为Value列否则，如果Status列为-，则Value列需要更新为Name列，不包含字符Ve_，如果Action不是Sell，则将Value列保留为np.NaN
到目前为止，我尝试过的是

import numpy as np
df['Value'] = np.where(df['Action']== 'Sell',df['Country'].str[:2] if df['Status'].str != '-' else df['Name'].str[3:],df['Value'])

但我得到的输出是<pandas.core.strings.StringMethods object at 0x000001EDB8F662B0>，无论我在哪里尝试提取子字符串，因此输出如下所示

Name     Action    Status      Country       Value
   Ve_Paper        Buy         -       Canada      np.NaN
Ve_Gasoline       Sell      Done      Britain  <pandas.core.strings.StringMethods object at 662B0>
   Ve_Water       Sell         -       Canada  <pandas.core.strings.StringMethods object at 0x000001EDB8F662B0>
   Ve_Plant        Buy      Good        China      np.NaN  
    Ve_Soda       Sell    Process     Germany  <pandas.core.strings.StringMethods object at 0x000001EDB8F662B0>

但我期待的output是

Name     Action    Status      Country       Value
   Ve_Paper        Buy         -       Canada      np.NaN # Because Action is not Sell
Ve_Gasoline       Sell      Done      Britain          Br # The first two characters of Country Since Action is sell and Status is not "-"
   Ve_Water       Sell         -       Canada       Water # The Name value without 'Ve_' since Action is Sell and the Status is '-'
   Ve_Plant        Buy      Good        China      np.NaN  
    Ve_Soda       Sell    Process     Germany          Ge

正如在下面的答案中提到的，我尝试的方法对某人有效，但对我无效。有没有比我试过的更好或更好的方法，因为那对我不管用

numpy

来源：https://stackoverflow.com/questions/74256848/updating-column-values-based-on-multiple-conditions

2条答案

按热度按时间

9gm1akwq1#

我看不出这里有什么问题。如果可能的话，请您再详细说明一下好吗？
我在下面分享我的这段代码：

import pandas as pd
import numpy as np
df = pd.DataFrame([('Ve_Paper', 'Buy', '-','Canada',np.NaN),
                  ('Ve_Gasoline', 'Sell', 'Done','Britain',np.NaN),
                  ('Ve_Water', 'Sell','-','Canada',np.NaN),
                  ('Ve_Plant', 'Buy', 'Good','China',np.NaN),
                  ('Ve_Soda', 'Sell', 'Process','Germany',np.NaN)], columns=['Name', 'Action','Status','Country','Value'])

df['Value'] = np.where(df['Action']== 'Sell',df['Country'].str[:2] if df['Status'].str != '-' else df['Name'].str[3:],df['Value'])
print(df)

您也可以尝试使用以下代码：

df['Value'] = np.where(df['Action']== 'Sell', df['Country'].astype(str).str[0:2], df['Value'])

赞(0）回复(0）举报 2022-11-10

9rygscc12#

使用np.select是一个很好的选择

conda = df.Action.eq('Sell')
condb = df.Status.eq('-')
df['value'] = np.select([conda&condb, conda&~condb],
                        [df.Name.str.split('_').str[1],df.Country.str[:2]],
                        default = np.nan)
df
Out[343]: 
          Name Action   Status  Country  Value  value
0     Ve_Paper    Buy        -   Canada    NaN    NaN
1  Ve_Gasoline   Sell     Done  Britain    NaN     Br
2     Ve_Water   Sell        -   Canada    NaN  Water
3     Ve_Plant    Buy     Good    China    NaN    NaN
4      Ve_Soda   Sell  Process  Germany    NaN     Ge

赞(0）回复(0）举报 2022-11-10

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numpy 基于多个条件更新列值

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