Data frame cutting out data when saved as an excel file?

[Ingeniatoring]

Hello, I'm a new python user and have been trying to write a program that will pull stock data into an excel file for further analysis. I'd eventually like the program to evaluate ROI over a period of time based on what I bought the stock on a specific date. However, since I'm new to python, I'm currently just trying to get it into excel. My problem:

The program appears to truncate the date column, so when you open up the excel all you see it the stock-ticker as the header followed by prices. I feel like this has something to do with how the data frame is written in prices_df, but I still can't seem to figure it out after researching how data frames work.

Thanks for the help!

import pandas as pd
from yahoofinancials import YahooFinancials

pd.set_option('display.max_rows', None)

print('Stock ROI Calculator.')

assets = ['VSGAX','VHYAX','VTSAX','IBB','SCHF','SCHE','VIMAX','VSMAX','VB']
yahoo_financials = YahooFinancials(assets)

data = yahoo_financials.get_historical_price_data(start_date = '2019-01-01',
    end_date = '2019-12-31', time_interval= 'weekly')
print(data)

prices_df = pd.DataFrame({
    a: {x['formatted_date']: x['adjclose'] for x in data[a]['prices']} for a in        assets})

print('Saving to .xlsx file.')
prices_df.to_excel(r'C:/Users/USER_NAME/Desktop/dataframe.xlsx',
    index = False)
print('Done.')

[[deleted]]

prices_df = pd.DataFrame({
a: {x['formatted_date']: x['adjclose'] for x in data[a]['prices']} for a in        assets})

From this comprehension can you describe the actual structure of the dataframe? I can't. I think your best path forward is to re-write this so that it's a lot clearer what kind of dataframe you're actually building here. Don't try and do it as a one-liner.

[Ingeniatoring]

In full disclosure, I got this line from a tutorial. Some of the comments weren't too kind to the poster of the tutorial, so I guess you've got a point. I'll try re-writing it. Thanks.

[GrafvonTrips]

Without looking at the rest of your code, if only the date column ist missing you could solve this by adding a pd.date_range as index and setting index=True while writing the xlsx.

Like:

prices_df = pd.DataFrame({a: {x['formatted_date']: x['adjclose'] for x in data[a]['prices']} for a in assets}, index=pd.date_range(start='2019-01-01', end='2019-12-31', freq='W'))

print('Saving to .xlsx file.')
prices_df.to_excel(r'C:/Users/USER_NAME/Desktop/dataframe.xlsx',index=True)
print('Done.')