GroupBy - Sum = Error [datetime64 type does not support sum operations]

[BSDevo]

Hi,
Im trying to solve this particular issue.
Im using two columns to display Price and Market.

        lftcolumn, rghtcolumn = st.columns((9, 1))
        with lftcolumn:
            pme_df = filtered_df.groupby(by = ["Market"], as_index = False)["Price"].sum().sort_values(by= ["Price"], ascending=False).reset_index()
            pmefig = px.bar(pme_df, x = "Market", y = "Price", text = ['${:,.2f}'.format(x) for x in pme_df["Price"]],template = "seaborn") 
            pmefig.update_layout(xaxis_title="Market",yaxis_title="Price")
            st.plotly_chart(pmefig,use_container_width=True, height = 200)
        with rghtcolumn:
            # # st.subheader("Percentage")
            st.subheader(' "%" dif')
            procentai = pme_df['Price']
  
            st.write(procentai.pct_change() * 100)

Which is fine - it works but with % column i have numbers instead of Market as index.
Im trying to make Index for this as Market.

            pme_df = filtered_df[["Market", "Price"]]
            pme_df = filtered_df.set_index(["Market", "Price"], inplace=True)
            pme_df = filtered_df.groupby(["Market", "Price"]).sum()

Error:
TypeError: datetime64 type does not support sum operations

            pme_df = filtered_df[["Market", "Price"]]
            pme_df = filtered_df.set_index(["Market", "Price"], inplace=True)
            pme_df = filtered_df.groupby(["Market"])["Price"].sum()

Error:
KeyError: 'Column not found: Price'

            pme_df = filtered_df[["Market", "Price"]]
            pme_df = filtered_df.set_index(["Market", "Price"], inplace=True)
            pme_df = filtered_df.groupby(["Market"]).sum()

Error:
TypeError: datetime64 type does not support sum operations

Market contains market areas( string/object type column) and price ( float)
filtered_df - various columns including date column
Whats causing this issue for me ?
Thank You.