How Can I Efficiently Create Multiple Pandas DataFrames in a Loop Based on a List of Values?

Creating Multiple Dataframes within a Loop

Within a Python script, you may encounter a scenario where you desire to construct multiple dataframes based on a given list of values. This task can be accomplished efficiently using the Pandas library.

Consider the following code snippet:

companies = ['AA', 'AAPL', 'BA', ....., 'YHOO']

# Create an empty dictionary
df_dict = {}

# Iterate over the companies
for company in companies:
    # Create a new dataframe for the current company
    df_dict[company] = pd.DataFrame()

Rather than dynamically assigning names to variables, as in your initial approach, this solution utilizes a dictionary to store the dataframes. Each dataframe is assigned a unique key corresponding to the company name.

To access a specific dataframe, simply use the following syntax:

df_dict['AA'] # dataframe for company 'AA'

You can also iterate over all dataframes using the items() method:

for name, df in df_dict.items():
    # Operate on the dataframe for company 'name'

This method provides a structured and efficient approach to managing multiple dataframes while ensuring that each dataframe remains associated with its respective company identifier.

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