How to sort pandas dataframe in ascending order using Python

I have a dataframe like this :

        Date          Price
0 2021-02-12 00:00:00 50
1 2021-02-11 00:00:00 2
2 2021-02-10 00:00:00 40.4
3 2021-02-09 00:00:00 775.6
4 2021-02-08 00:00:00 1000.2
5 2021-02-07 00:00:00 500

Columns’ types with print(df.dtypes) :

Date         datetime64[ns]
Price               float64

Expected Output :

        Date          Price
0 2021-02-07 00:00:00 500
1 2021-02-08 00:00:00 1000.2
2 2021-02-09 00:00:00 775.6
3 2021-02-10 00:00:00 40.4
4 2021-02-11 00:00:00 2
5 2021-02-12 00:00:00 50

I have a dataframe like df. When I do :

df = df.sort_values(by='Date')

But nothing happen even by adding ascending = True or False.

Could you give the way pls to order this dataframe as above ?

If possible can you give the 2 possibilites like ordering by index and date but I’m looking to order by ascending date directly without touching to the index.

EDIT for more clarity :

# Converted list of dictionaries to a Dataframe
extracted_data_List_DataFrames = [pd.DataFrame(x) for x in extracted_data_List]

# Convert string to their respectiv types
for dfs in extracted_data_List_DataFrames:
    dfs['Date'] = pd.to_datetime(dfs['Date'])
    dfs['Price'] = dfs['Price'].astype('float64')

    # Sort dataframes by 'Date'
    dfs = dfs.sort_values(['Date'], ascending=False)

print(extracted_data_List_DataFrames)

You have my code above. I’m not able to make the sort method to work correctly.

Answer

Problem is if modify values in loop there is no change if original list, you can assign ouput to original list of DataFrames like:

for i, dfs in enumerate(extracted_data_List_DataFrames):
    dfs['Date'] = pd.to_datetime(dfs['Date'])
    dfs['Price'] = dfs['Price'].astype('float64')

    # Sort dataframes by 'Date'
    dfs = dfs.sort_values(['Date'], ascending=False)
    extracted_data_List_DataFrames[i] = dfs

Another idea is use inplace=True:

for dfs in extracted_data_List_DataFrames:
    dfs['Date'] = pd.to_datetime(dfs['Date'])
    dfs['Price'] = dfs['Price'].astype('float64')

    # Sort dataframes by 'Date'
    dfs.sort_values(['Date'], ascending=False, inplace=True)