在python中基于条件区分pandas数据帧的两列

Question

我有一个名为pricecomp_df的数据框，我想比较“市场价格”列和其他每一列的价格，如“苹果价格”、“芒果价格”、“西瓜价格”，但要根据以下条件对差异进行优先排序：（第一优先是西瓜价格，其次是芒果价格，第三优先是苹果价格）。输入数据帧如下：

   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，然后取它们的差额，但是在第二行，我们有苹果，芒果价格，所以我只能取市场价格和芒果价格之间的差额。同样地，根据优先级条件取差。同时跳过与nan的所有三个价格的行。有人能帮忙吗？

Answer 1

希望我不会太晚。我们的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