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pythonpandas
notIntoXS
notIntoXS

Reputation: 129

I do not understand when to use a Pandas Series and when to use a Pandas Single Column Dataframe

I’ve done quite a lot of searching around and seen many posts that explain the differences but I have not come across clear use cases. I do understand the differences in general but I would like to know why I should learn how to use Series when it seems that a single column Dataframe might perform everything a Series can.

Essentially I cannot extrapolate my understanding of their differences into “when I should use Series or Dataframe for a task in front of me?”.

Upvotes: 1

Views: 1583

Answers (1)

U13-Forward
U13-Forward

Reputation: 71580

Here are my short explanations:

  • Series: Series are for one-dimensional data, just like lists with a lot of functions.

  • DataFrame: DataFrames are for multi-dimensional data, just like nested lists with a lot of functions.

Go to the docs to learn more.

Series from the docs:

Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index.

DataFrame from the docs:

DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object.

Upvotes: 5

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