Create a dataframe of a list of dataframes-CodePudding

I'm trying to create a dataframe of sales data from 45 different stores. I saved the data as dataframes in a list: So I have a list of length 45, each element beeing an dataframe of 143 numbers.

What I want: Create a dataframe with an index-column(date) and a seperate column for each store.

What's the easiest way to achieve my goal?

store1 = df_sales_shops[df_sales_shops['Store'] == 1]
index = pd.DataFrame(store1.index)
index = index.drop_duplicates()

stores_revenue = []
for store in range(45):

    temp = df_sales_shops[df_sales_shops['Store'] == store 1]
    stores_revenue.append(pd.DataFrame(temp.groupby(['Date']).Weekly_Sales.sum()))
    stores_revenue[store].reset_index(drop=True, inplace=True)

print(stores_revenue)
print(index)

Output stores revenue[0]:

[     Weekly_Sales
0      1643690.90
1      1641957.44
2      1611968.17
3      1409727.59
4      1554806.68
..            ...
138    1437059.26
139    1670785.97
140    1573072.81
141    1508068.77
142    1493659.74

[143 rows x 1 columns],
...

CodePudding user response：

IIUC, the code below should be equivalent of your loop and what you expect:

out = df[df['Store'].between(1, 45)].groupby(['Store', 'Date'])['Weekly_Sales'].sum() \
                                    .unstack(level='Store').reset_index(drop=True) \
                                    .rename_axis(columns=None).add_prefix('Store')