Question

我有以下两个（简化的）数据框：

df1=
         origin destination  val1  val2
    0      1           A      0.8   0.9
    1      1           B      0.3   0.5
    2      1           c      0.4   0.2
    3      2           A      0.4   0.7
    4      2           B      0.2   0.1
    5      2           c      0.5   0.1
df2=
  org  price
0   1     50
1   2     45

我需要做的是从df2的每个来源中选择价格，将其乘以df1中val1 + val2的总和，然后将其写入csv文件。

A的计算如下：

A =>（0.8 + 0.9）* 50 +（0.4+ 0.7）* 45 = 134.5

此处，值0.8、0.9、0.4和0.7来自df1，它们对应于A的val1和val2 其中，值50和45来自分别对应于原点1和原点2的df2。对于B，计算将为

B =>（0.3 + 0.5）* 50 +（0.2 + 0.1）* 45 = 53.5

对于C，计算公式为：

C =>（0.4 + 0.2）* 50 +（0.5 + 0.1）* 45 = 57

最终的CSV文件应如下所示：

A，134.5

B，53.5

C，57 我为此编写了以下python代码：

# first convert the second table into a python dictionary so that I can refer price value at each origin
df2_dictionary = {}
for ind in df2.index:
    df2_dictionary[df2['org'][ind]] = float(df2['price'][ind])    

# now go through df1, add up val1 and val2 and add the result to the result dictionary. 
result = {}
for ind in df1.index:
    origin = df1['origin'][ind] 
    price = df2_dictionary[origin] # figure out the price from the dictionary.
    r = (df1['val1'][ind] + df1['val2'][ind])*price # this is the needed calculation 
    destination = df1['destination'][ind] # store the result in destination
    if(destination in result.keys()):
        result[destination] = result[destination]+r
    else:
        result[destination] = r
f = open("result.csv", "w")
for key in result:
    f.write(key+","+str(result[key])+"\n")
f.close()

这是很多工作，并且不使用pandas内置函数。我该如何简化呢？我并不担心效率。

Answer 1

可以先使用map然后使用groupby解决您的问题：

df1['total'] = (df1[['val1','val2']].sum(1)
                   .mul(df1['origin']
                            .map(df2.set_index('org').price)
                       )
               )

summary = df1.groupby('destination')['total'].sum()

# save to csv
summary.to_csv('/path/to/file.csv')

输出（summary）：

destination
A    134.5
B     53.5
c     57.0
Name: total, dtype: float64

使用两个熊猫数据框进行的计算

1 个答案: