在 Python 中按多个条件对数据进行分组

Question

I think I have a quick question, but I didn't find a way to google it in simple words.

I've got a raw dataset like this:

 Number of account     Value
      123               100
      456               300
      789               400
      910               100
      674               250

And I've got a methodological table to consolidate this raw data into something useful. It looks like:

 Variable              Number of account
    "a"                  123, 456, 910
    "b"                    789,674

So, in the end I would like to get a table like this:

 Variable              Number of account
    "a"                  Sum of values for(123, 456, 910)
    "b"                  Sum of values for(789,674)

My initial idea is to do something like: For each row in methodological table, For each Number of account in methodological table, Sum values in raw data .

Two questions:

1. What is the best way to consolidate it?
2. What if in methodological table number of accounts are comma-delimited strings? ("123,456,910"). Can I store multiple numbers in one cell in pandas DataFrame

Answer 1

Assuming I have data in two dataframes:

df is:

Number_of_account     Value
      123               100
      456               300
      789               400
      910               100
      674               250

and table_2 is:

Variable              Number_of_account
    "a"                  123,456,910
    "b"                    789,674

First, I'll create a lookup table out of table2:

lookup_table = pd.concat([pd.Series(row['Variable'], row['Number_of_account'].split(','))              
                         for _, row in table_2.iterrows()]).reset_index()
lookup_table.columns = ["Number_of_account", "variable"]
lookup_table.Number_of_account = pd.to_numeric(lookup_table.Number_of_account)

The result is:

   Number_of_account variable
0                123        a
1                456        a
2                910        a
3                789        b
4                674        b

Then, I merge the main dataframe ( df ) with the lookup table, and use groupby to calculate the sum of the values.

df = pd.merge(df, lookup_table, on="Number_of_account")
df.groupby("variable")["Value"].sum()

The result is:

variable
a    500
b    650