How to Efficiently Create Multiple Pandas DataFrames in a Loop?

Creating Multiple DataFrames in a Loop

When working with large datasets, it can be necessary to create multiple dataframes based on different criteria. One way to do this is to use a loop to iterate through a list or array of company names and create a new dataframe for each entry.

However, attempting to create a dataframe named after a dynamically generated variable can be problematic. Python's dynamic nature allows for the creation of variables and data structures during runtime. However, assigning a dataframe directly to a variable named after a company, as shown in the pseudocode below, is not recommended.

for c in companies:
    c = pd.DataFrame()

To avoid naming conflicts and preserve clarity, it is advisable to use a dictionary, d, to hold the dataframes indexed by company name.

d = {}
for name in companies:
    d[name] = pd.DataFrame()

# Retrieve a specific dataframe
dataframe_of_company_x = d[x]

# Operate on all companies
for name, df in d.items():
    # ...

This approach ensures that the names of the dataframes are static and explicitly linked to the company names. It also allows for easy retrieval and manipulation of individual and multiple dataframes.

The above is the detailed content of How to Efficiently Create Multiple Pandas DataFrames in a Loop?. For more information, please follow other related articles on the PHP Chinese website!