基于python条件的大熊猫两栏数据差异

Question

我有一个名为pricecomp_df的数据，我想比较一下“市场价格”栏目的价格，以及“苹果价格”、“芒果价格”、“西瓜价格”等其他栏目的价格，但根据条件将差异排序：(第一优先是西瓜价格，第二优先是芒果价格，第三栏是苹果价格)。输入数据如下：

   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

在这里，第一排只有苹果价格和市场价格，然后取他们的差额，但是在第二排，我们有苹果，芒果价格，所以我只需要取市价和芒果价格之间的差额。同样，根据优先级条件获取差异。同时跳过所有三种价格的nan行。有人能帮上忙吗？

Answer 1

希望我不会太晚。这样做的目的是根据优先级列表计算差异并覆盖它们。

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