I would like to bring together 2 lists in one dataframe.

The first list I have contains various dates.

my_dates = ['20/12/2024', '31/08/2022', '19/08/2022', '21/06/2024']

The second list is a list of list that contains various stock names and dates.

my_stocks = [
    ["AAPL", ['20/12/2024', '31/08/2022']],
    ["MSFT", ['20/12/2024', '31/08/2022', '21/06/2024']], 
    ["TSLA", []], 
    ["META", ['20/12/2024', '31/08/2022', '19/08/2022', '21/06/2024']]
]

I would like to bring those two lists together in a dataframe where:

• The header / column names are the values of my_dates
• The first value of each row is the stock name
• The value of the "cells" is the date if it is equal to the column name

I thought about something like

new = []

for elem in my_stocks:
    new.append(elem)


df = pd.DataFrame(new)

df = pd.concat([df[0],df[1].apply(pd.Series)],axis=1)

print(df)

But this doesn't include the headers and doesn't match the dates with the headers.

As you can see I am new to Python and any help is highly appreciated!

Many thanks Mika

CodePudding user response：

here is an approach that creates each stock as it's own pandas series and then uses pd.concat to make them into a dataframe. it then uses the my_dates to select which dates to use and transposes the table to match your desired output

import pandas as pd

my_dates = ['20/12/2024', '31/08/2022', '19/08/2022', '21/06/2024']
my_stocks = [["AAPL", ['20/12/2024', '31/08/2022']],["MSFT", ['20/12/2024', '31/08/2022', '21/06/2024']], ["TSLA", []], ["META", ['20/12/2024', '31/08/2022', '19/08/2022', '21/06/2024']]]

stock_df = pd.concat((pd.Series(ds, ds, name=n, dtype='object') for n,ds in my_stocks), axis=1)
stock_df = stock_df.loc[my_dates]

stock_df = stock_df.T
print(stock_df)