2020-09-01

DS - Pandas - Series

This is part of a series of articles dedicated to study Data Science using Python. This article tries to explain the basics of using the Series abstraction from the Pandas library.

What is a Series object ?

Following the Pandas documentation, a Series object represents a one-dimensional dnarray with axis labels (including time-series). On the other hand a ndarray, which is an abstraction from the Numpy library, is an array representing a multidimensional, homogeneous array of fixed-sized items.

Long story short, the way you can think of if is like if it was a data structure having 2 columns, the first one contains the indexes, and the second one the actual data, the values we will be providing as content to the Series object, for instance:

Index

Data

0

100

1

200

2

300

Creating a Series object

So lets say we want to provide a series of population for three cities:

create a new Series object
import pandas as pd

city_population_2020 = pd.Series([6.7, 21, 8.5])
city_population_2020

This will show:

0

6.7

1

21

2

8.5

Series indexes

If you don’t explicitly provide indexes for the values by default the indexes will be a series of integer values beginning at 0. It’s important to provide the right indexes because those indexes will enable us to access data later on.

You can provide custom indexes in three ways. Firstly when creating the Series object, using the index parameter:

change at creation
import pandas as pd

city_population_2020 = pd.Series([6.7, 21, 8.5], index=["MAD", "MEXDF", "NYC"])
city_population_2020

Now the Series object will look like:

MAD

6.7

MEXDF

21

NYC

8.5

You can also change the indexes after creating a Series object, setting the index property :

change after creation
import pandas as pd

city_population_2020 = pd.Series([6.7, 21, 8.5])
city_population_2020.index = ["MAD", "MEXDF", "NYC"]
city_population_2020

Finally, you can create a Series object from a Python dictionary, where keys in the dictionary will become the indexes in the Series object:

import pandas as pd

city_population_2020 = pd.Series({"MAD": 6.7, "MEXDF": 21, "NYC": 8.5})
city_population_2020

Accessing data: iloc, loc

As an iterable you can iterate over the Series object like any other array in Python:

iterating over a Series
for value in city_population_2020:
    print("-> {}".format(value))
output
-> 6.7
-> 21
-> 8.5

But if you’d like to access in a more surgical way, you can access specific set of values by index. There’re two functions in the Series objects that can give you a hand with that: iloc and loc functions.

invocations to iloc and loc return another Series object.

The iloc function gets the data by the order is stored in the Series object. For instance:

getting values by index order (iloc)
import pandas as pd

city_population_2020 = pd.Series([6.7, 21, 8.5], index=["MAD", "MEXDF", "NYC"])
city_population_2020.iloc[0]

Will get 6.7 as it is the first value stored in the Series object. But what if wanted to get a value stored under a certain index. Then we could use the loc function. The loc function retrieves a value by the index is stored under. That means it can return None, one value or many values. The following example contains several values stored under the same index:

getting values by index name (loc)
import pandas as pd

people = pd.Series(["Marcos", "Ana", "Jules"], index=["MAD", "MAD", "PAR"])

people.loc["MAD"] # returns Marcos and Ana in another Series object
If you try to get values by a non existent index, you will get an error.

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