pandas Series

The pandas library comes with two types of classes to handle data. The first that we will learn about is the pandas Series. A pandas Series can be visualized as an individual column within a spreadsheet. The column typically has a name and an index for each row associated with that column.

A pandas Series can hold any data type and all values within a Series are associated with a labeled index. The labeled index can be either label or integer-based. An example of a label-based index would be groceries or movies while an integer-based index would be numeric (1, 2, 3, etc..).

Creating a Series

Let’s take a look at the syntax for creating a Series using lists, dictionaries, and tuples.

Using a List

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# import pandas
import pandas as pd

# Create a pandas Series by providing a list
example_list = pd.Series(["apple", "banana", "avocado", "honey dew"])

# Create a pandas Series from a pre-existing list of values
pre_existing_list = ["apple", "banana", "avocado", "honey dew"]

series_from_existing_list = pd.Series(pre_existing_list)

The above code block accomplishes the following:

1. Imports pandas as pd.
2. Creates a pandas Series called example_list by providing a list of values.
3. Creates a pandas Series called series_from_existing_list by using the already existing list pre_existing_list and passing it in as a parameter to the .Series() function.

Using a Dictionary

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# import pandas
import pandas as pd

# Create a pandas Series by providing a dictionary, the i represents the index of the dictionary
example_dictionary = pd.Series({'0': "apple", '1': "banana", '2': "avocado", '3': "honey dew"})

# Create a pandas Series from a pre-existing dictionary, the i represents the index of the dictionary
pre_existing_dictionary = {'0': "apple", '1': "banana", '2': "avocado", '3': "honey dew"}

series_from_existing_dictionary = pd.Series(pre_existing_dictionary)

The above code block accomplishes the following:

1. imports pandas as pd.
2. Creates a pandas Series called example_dictionary by providing a dictionary.
3. Creates a pandas Series called series_from_existing_dictionary by using the already existing dictionary pre_existing_dictionary and passing it in as a parameter to the .Series() function.

Using a Tuple

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# import pandas
import pandas as pd

# Create a pandas Series by providing a tuple
example_tuple = pd.Series(("Interstellar", "Pride and Prejudice", "Inception", "Barbie"))

# Create a pandas Series by providing a pre-existing tuple
pre_existing_tuple = ("Interstellar", "Pride and Prejudice", "Inception", "Barbie")

series_from_existing_tuple = pd.Series(pre_existing_tuple)

The above code block accomplishes the following:

1. imports pandas as pd.
2. Creates a pandas Series called example_tuple by providing a tuple with values.
3. Creates a pandas Series called series_from_existing_tuple by using an already existing tuple pre_existing_tuple and passing it in as a parameter to the .Series() function.

Indexing and Naming

When creating a pandas Series you have the ability to add custom index labels and a name (sometimes also referred to as a label) for the column associated with the Series.

If you do not add custom index labels to a Series and none already exist it will default to a typical index range of 0, 1, 2, 3, 4, 5, etc... In regards to column names, if you do not add a custom name to the column it will default to none.

custom_index_labels = pd.Series(["apple", "banana", "avocado", "honey dew"], index = ["red", "yellow", "green", "green"])

custom_index_labels = pd.Series(["apple", "banana", "avocado", "honey dew"], index = ["red", "yellow", "green", "green"], name = "fruit")

custom_index_labels = pd.Series(["apple", "banana", "avocado", "honey dew"], index = ["red", "yellow", "green", "green"])

custom_index_labels.name = "fruit"

fruit_color = ["red", "yellow", "green", "green"]

custom_fruit_labels = pd.Series(["apple", "banana", "avocado", "honey dew"], index = fruit_color)

Subsetting a Series

pandas allows you to use slicing to subset a Series. You can accomplish this using bracket notation and specifying an index range. Let’s take a look at how we can do this using the dictionary created above as an example.

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# import pandas
import pandas as pd

# Create a pandas Series from a pre-existing dictionary, the i represents the index of the dictionary
fruit_data = {'0': "apple", '1': "banana", '2': "avocado", '3': "honey dew"}

# create a series from the fruit_data
fruit_series = pd.Series(fruit_data)

# Subset the existing series
subset_fruit = fruit_series[:2]

# Print the subset
print(subset_fruit)

# Subset Series to include elements from index 1 to 3
subset_fruit_twice = fruit_series[1:4]
# Print the series
print("Subset elements from index 1 to 3")
print(subset_fruit_twice)

0     apple
1    banana

Subset elements from index 1 to 3
1       banana
2      avocado
3    honey dew

Check Your Understanding