How to Efficiently Create Multiple Pandas DataFrames from a List of Names?

Creating Multiple DataFrames in a Loop

In the realm of data analysis, it becomes imperative sometimes to work with separate dataframes corresponding to distinct entities. One particular scenario involves creating a new dataframe for each element in a provided list, containing company names in this specific instance.

To achieve this, the idea of dynamically adding names to the Python namespace should be strictly avoided due to potential conflicts and readability issues. A more appropriate approach involves utilizing dictionaries.

d = {}<br>for name in companies:</p>
<pre class="brush:php;toolbar:false">d[name] = pd.DataFrame()

This code effectively creates a dictionary d where company names serve as keys, each linked to an empty dataframe. To access the dataframe for a particular company x, simply reference d[x].

for name, df in d.items():</p>
<pre class="brush:php;toolbar:false"># operate on Dataframe 'df' for company 'name'

This loop iterates through each dictionary entry, allowing you to manipulate dataframes individually. Alternatively, you could employ a dictionary comprehension for a more succinct representation:

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

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