How is a Pandas series different from a dataframe?

Frequently Asked Questions Python FAQ
1

Question

How is a Pandas series different from a dataframe?

Answer

In Pandas a series is a one-dimensional object that contains any type of data, similar in ways to a Numpy array.

Series objects have a single axis label, like a column title, which is the index of the series. A series is essentially a single column.

# Creating a series
clinic_east = pd.Series([100, 51, 81, 80, 51, 112])

A dataframe is a two-dimensional object that can hold multiple columns of different types of data. They are similar to a table in SQL.

A single column of a dataframe is a series, and a dataframe is a container of two or more series objects.

# Creating a DataFrame
df = pd.DataFrame ([
  ['January', 100, 100],
  ['February', 51, 45],
  ['March', 81, 96]],
  columns=["month", "clinic_east", "clinic_north"]
)
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What do we call a single row of data frame ?

3

It’s known as ‘Series’

4

A single row is like a ‘datapoint’ of your data.

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5

Seems like no particular name for 3-dimensional object (cannot find from panda documents), am I correct? Just curious. :slight_smile:

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6

This might be helpful, though I have not yet tried it out: Pandas: MultiIndex / advanced indexing.

Let us know whether you find it suitable for the task.

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7

A series object would be the column. A row is, as someone else stated, like a “datapoint.” It is one record with various values for the included fields (columns).

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8

You can call it a “record”.

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9

I do not understand why the column title is the index of the series. Do you mean it is the index of the dataframe series is located in ?

10

Can I select multiple columns of DF?