Transpose Dataframe problem: For each df.index and df.column combination create a row in new dataframe

Question

I have a data frame that looks like this:

The index of my data frame is the "Dates" column.

Dates   3M INDIA LTD    ALKYL AMINES CHEMICALS LTD  AAVAS FINANCIERS LTD    ABB INDIA LTD   ADITYA BIRLA CAPITAL LTD
01-01-2020  1.738819    -0.054496   -0.600676   -0.535873   -1.837524   0.514004    -0.853701   -0.101420   2.192982
02-01-2020  -1.110939   3.668744    1.371749    1.346907    4.367026    2.930212    3.540222    4.080081    1.185880
03-01-2020  -0.862856   0.008598    2.543608    2.104247    0.795136    -0.290943   -0.726246   -1.021898   1.368421
06-01-2020  -2.135963   -1.952790   -2.201474   -2.643822   -4.166667   -2.250709   -1.815881   -2.933202   0.300000
07-01-2020  1.692019    8.431578    -1.116379   0.674114    0.097800    -3.166751   0.677638    -1.873767   0.837922

I want to create a new data frame such that for each Date, and Company Name Combination, I'll have 1 row in the data frame.

The resulting data frame will look like this: Dates CompanyName Value

How Can I achieve this transformation using python pandas operations?

Answer 1

df = df.set_index('Dates').stack().reset_index()
df.columns = ['Dates','Company Name','Value']

df.sort_values(by=['Company Name', 'Dates'])

Or

pd.melt(df, 
        id_vars=['Dates'], 
        value_vars=[x for x in df.columns if x!='Dates'],
        var_name='Company Name',
        value_name='Values')