根据列值计算两个熊猫数据框之间的值

Question

EDITED: let me copy the whole data set

df is the store sales/inventory data

  branch   daqu store     store_name       style  color  size  stocked    sold  in_stock  balance
0  huadong  wenning  C301  EE #��#��##��  EEBW52301M     39   160        7    4         3       -5
1  huadong  wenning  C301  EE #��#��##��  EEBW52301M     39   165        1    0         1        1
2  huadong  wenning  C301  EE #��#��##��  EEBW52301M     39   170        6    3         3       -3

dh is the transaction (move 'amount' from store 'from' to 'to')

    branch      daqu  from    to       style  color  size  amount  box_sum
8   huadong  shanghai  C306  C30C  EEOM52301M     59   160       1      162
18  huadong  shanghai  C306  C30C  EEOM52301M     39   160       1      162
25  huadong  shanghai  C306  C30C  EETJ52301M     52   160       9      162
26  huadong  shanghai  C306  C30C  EETJ52301M     52   155       1      162
32  huadong  shanghai  C306  C30C  EEOW52352M     19   160       2      162

What I want is the store inventory data after the transaction, which would look exactly the same format as the df, but only 'in_stock' numbers would have changed from the original df according to numbers in dh.

below is what I tried:

df['full_code'] = df['store']+df['style']+df['color'].astype(str)+df['size'].astype(str)    
dh['from_code'] = dh['from']+dh['style']+dh['color'].astype(str)+dh['size'].astype(str)
dh['to_code'] = dh['to']+dh['style']+dh['color'].astype(str)+dh['size'].astype(str)


# subtract from 'from' store
dh_from = pd.DataFrame(dh.groupby('from_code')['amount'].sum())

for code, stock in dh_from.iterrows() :
    df.loc[df['full_code'] == code, 'in_stock'] = df.loc[df['full_code'] == code, 'in_stock'] - stock

# add to 'to' store    
dh_to = pd.DataFrame(dh.groupby('to_code')['amount'].sum())

for code, stock in dh_to.iterrows() :
    df.loc[df['full_code'] == code, 'in_stock'] = df.loc[df['full_code'] == code, 'in_stock'] + stock

df.to_csv('d:/after_dh.csv')

But when I open the csv file then the 'in_stock' values for those which transaction occured are all blanks. I think df.loc[df['full_code'] == code, 'in_stock'] = df.loc[df['full_code'] == code, 'in_stock'] + stock this has some problem. What's the correct way of updating the value?

---

ORIGINAL: I have two pandas dataframe: df1 is for the inventory, df2 is for the transaction

df1 look something like this:

   full_code in_stock
1  AAA       200
2  BBB       150
3  CCC       150

df2 look something like this:

   from   to   full_code  amount
1  XX     XY   AAA        30
2  XX     XZ   AAA        35
3  ZY     OI   BBB        50
4  AQ     TR   AAA        15

What I want is the inventory after all transactions are done. In this case,

   full_code in_stock
1  AAA       120
2  BBB       100
3  CCC       150

Note that full_code is unique in df1, but not unique in df2. Is there any pandas way of doing this? I got messed up with the original dataframe and a view of the dataframe and got it solved by turning them into numpy array and finding matching full_codes. But the resulting code is also a mess and wonder if there is a simpler way of doing this not turning everything into a numpy array.

Answer 1

What I would do is to set the index in df1 to the 'full_code' column and then call sub to subtract the other df.

What we pass for the values is the result of grouping on 'full_code' and calling sum on 'amount' column.

An additional param for sub is fill_values this is because product 'CCC' does not exist on the rhs so we want this value to be preserved, otherwise it becomes NaN :

In [25]:

total = df1.set_index('full_code')['in_stock'].sub(df2.groupby('full_code')['amount'].sum(), fill_value=0)
total.reset_index()
​
Out[25]:
  full_code  in_stock
0       AAA       120
1       BBB       100
2       CCC       150