Series

Series#

Panopto logo Series

In pandas, “a Series is a one-dimensional, array-like object containing a sequence of values…and an associated array of data labels, called its index” (McKinney, 126). At first glance, a Series looks a lot like a Python list.

# import pandas package
import pandas as pd

# import Series and Data Frame components from pandas

# create a Series using pandas
obj = pd.Series([4, 7, -5, 3])

# show obj Series
obj

A few notes on what’s happening in this example:

  • We imported the pandas package (using the pd alias) as well as the specific Series and DataFrame components.

  • We created a Series object containing four integer values.

We could create a list with these values, but for data analysis we needed the functionality pandas provides for working with series. To verify obj is stored as an array-like object, we can use pd.Series(obj).values which should return array([4, 7, -5, 3])

Python Examples#

We can also use Python’s built-in arithmetic functionality for values in a Series object.

# select values in the Series that meet a specific condition
obj2[obj2 > 0]

# returns index-value pairs for values greater than 0

# multiply all values in Series
obj * 2

# returns modified values

Try to perform similar mathematical operations on values stored in a Python dictionary or list and you’ll run into all kinds of data type errors. The Series object uses a similar data structure and opens up a wide range of analysis possibilities.

Application#

Q1: Create your own Series object. Write code the accomplishes the following tasks. Your answer for these items should include a Python program + comments that document process and explain your code.

  • Perform at least two unique arithmetic operations on the Series

  • Test for null values in your series

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