Python excel dataset transformation-CodePudding

I am reading excel data columns Item, Month and Index value...

LowestCostItems = pd.read_excel('lowest_cost_items_dataset.xlsx')

Item    Date    Index value (April 2021 = 100)
All items   2021-04 100
All items   2021-05 100.2
All items   2021-06 99.7
All items   2021-07 100
All items   2021-08 100.9
All items   2021-09 99.5
All items   2021-10 103.4
All items   2021-11 103.2
All items   2021-12 104.9
All items   2022-01 105.6
Apples  2021-04 100
Apples  2021-05 100.1
Apples  2021-06 100.3
Apples  2021-07 99.6
Apples  2021-08 101.2
Apples  2021-09 88.6
Apples  2021-10 99.3
Apples  2021-11 93.5
Apples  2021-12 97.6
Apples  2022-01 99.4
Baked beans 2021-04 100
Baked beans 2021-05 100
Baked beans 2021-06 100
Baked beans 2021-07 100.1
Baked beans 2021-08 101.4
Baked beans 2021-09 101.4
Baked beans 2021-10 106.1
Baked beans 2021-11 111.6
Baked beans 2021-12 112
Baked beans 2022-01 115.1

I am trying to switch columns and rows to create a new csv file. I can aggregate data by Date and Item column

LowestCostItems.groupby(['Date','Item']).agg({"Index value (April 2021 = 100)": lambda x:(x)})

i am unable to figure out what i need to do so that First column should become Date, data in Item row should become a columns headings and their respected values in Index value... should appear underneath each column.

The end result should be :

Any help will be much appreciated.

CodePudding user response：

Try:

df = df.pivot(
    index="Date", columns="Item", values="Index value (April 2021 = 100)"
).reset_index()
df.columns.name, df.index.name = None, None

print(df)

Prints:

      Date  All items  Apples  Baked beans
0  2021-04      100.0   100.0        100.0
1  2021-05      100.2   100.1        100.0
2  2021-06       99.7   100.3        100.0
3  2021-07      100.0    99.6        100.1
4  2021-08      100.9   101.2        101.4
5  2021-09       99.5    88.6        101.4
6  2021-10      103.4    99.3        106.1
7  2021-11      103.2    93.5        111.6
8  2021-12      104.9    97.6        112.0
9  2022-01      105.6    99.4        115.1