What\'s the Best Way to Create Multiple Pandas DataFrames in a Loop?

Creating Multiple Dataframes in a Loop: An Analysis of Approaches

In data analysis, it's often necessary to create multiple dataframes for different entities. This can be achieved using a loop, but the best approach depends on the specific requirements.

One method is to create a new dataframe for each entry in a list of company names:

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

This approach is straightforward but doesn't prevent naming conflicts with variables already in use. Additionally, relying on dynamic techniques for data retrieval may compromise code readability.

A more suitable approach is to use a dictionary to store the dataframes, where the keys are the company names:

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

or using a more concise dict comprehension:

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

This approach ensures unique names for the dataframes and allows for easy lookup and iteration:

for name, df in d.items():
    # operate on dataframe 'df' for company 'name'

In Python 2, using iteritems() is preferable to avoid instantiating a list of tuples.

In summary, while creating multiple dataframes in a loop is a common task, the choice of approach depends on factors such as namespace management, data retrieval methods, and code readability. Using a dictionary is generally considered best practice for organizing and accessing the dataframes by entity names.

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