根据python中的条件取两列熊猫数据框之间的差异答案

【问题标题】：Take difference between two column of pandas dataframe based on condition in python根据python中的条件取两列熊猫数据框之间的差异
【发布时间】：2016-08-03 23:11:10
【问题描述】：

我有一个名为 pricecomp_df 的数据框，我想比较“市场价格”列的价格和“苹果价格”、“芒果价格”、“西瓜价格”等其他列的价格，但优先考虑差异条件：（西瓜价格第一，芒果第二，苹果第三）。输入数据框如下：

   code  apple price  mangoes price  watermelon price  market price
0   101          101            NaN               NaN           122
1   102          123            123               NaN           124
2   103          NaN            NaN               NaN           123
3   105          123            167               NaN           154
4   107          165            NaN               177           176
5   110          123            NaN               NaN           123

所以这里第一行只有苹果价格和市场价格，然后取它们的差异，但在第二行，我们有苹果和芒果的价格，所以我只需要取市场价格和芒果价格之间的差额。同样根据优先条件取差值。对于所有三个价格，也跳过带有 nan 的行。有人可以帮忙吗？

【问题讨论】：

标签： python conditional diff

【解决方案1】：

希望我不会太晚。这个想法是计算差异并根据您的优先级列表覆盖它们。

import numpy as np
import pandas as pd

df = pd.DataFrame({'code': [101, 102, 103, 105, 107, 110],
                   'apple price': [101, 123, np.nan, 123, 165, 123],
                   'mangoes price': [np.nan, 123, np.nan, 167, np.nan, np.nan],
                   'watermelon price': [np.nan, np.nan, np.nan, np.nan, 177, np.nan],
                   'market price': [122, 124, 123, 154, 176, 123]})

# Calculate difference to apple price
df['diff'] = df['market price'] - df['apple price']
# Overwrite with difference to mangoes price
df['diff'] = df.apply(lambda x: x['market price'] - x['mangoes price'] if not np.isnan(x['mangoes price']) else x['diff'], axis=1)
# Overwrite with difference to watermelon price
df['diff'] = df.apply(lambda x: x['market price'] - x['watermelon price'] if not np.isnan(x['watermelon price']) else x['diff'], axis=1)

print df
   apple price  code  mangoes price  market price  watermelon price  diff
0          101   101            NaN           122               NaN    21
1          123   102            123           124               NaN     1
2          NaN   103            NaN           123               NaN   NaN
3          123   105            167           154               NaN   -13
4          165   107            NaN           176               177    -1
5          123   110            NaN           123               NaN     0

【讨论】：