Introduction to Pandas in Python

Introduction to Pandas in Python

Learning Objectives

At the end of the lesson you will understand the purpose and functionality of the Pandas library in Python.

What do you already know about the Pandas library in Python?

Overview and Purpose of the Pandas Library

Pandas is a Python library for data manipulation and analysis. 

Pandas library has functions for cleaning, exploring, and manipulating datasets. It has tools for calculating statistical correlations, average, maximum, and minimum values within data.

Importing Pandas and Basic Syntax

To use Pandas library, you have to import it in your code.   

import pandas

You can import with the alias 'pd' to simplify the syntax.

Understanding and Creating Pandas Series

Series - are one-dimensional arrays with index labels.

A Series is like a column in a table.  

import pandas as pd

a = [1, 7, 2]

myvar = pd.Series(a)

print(myvar)

Output:

0       1

1        7

2       2

dtype: int64

Labels in Series

If nothing else is specified, the values are labeled with their index number. First value has index 0, second value has index 1 etc.

Labels can be used to access a specified value.

Labels in Series

import pandas as pd

a = [1, 7, 2]

myvar = pd.Series(a)

print(myvar[1])

Output:

7

Create Labels in Series

With the index argument, you can name your own labels. When you have created labels, you can access  an item by referring to the label.

import pandas as pd

a = [1, 7, 2]

myvar = pd.Series(a, index = ["x", "y", "z"])

print(myvar)  print(myvar["y"])

Output:

x    1

y    7

z    2

dtype: int64

7

Key/Value Objects as Series

You can also use a key/value object, like a dictionary, when creating a Series.

import pandas as pd

calories = {"day1": 420, "day2": 380, "day3": 390}

myvar = pd.Series(calories)

print(myvar)

Output:

day1     420

day2     380

day3     390

dtype: int64

Working with DataFrames in Pandas

DataFrames - two-dimensional tables with rows and columns

import pandas as pd

mydataset = {

  'cars': ["BMW", "Volvo", "Ford"],

  'passings': [3, 7, 2]

}  myvar = pd.DataFrame(mydataset)

print(myvar)

Output

    cars passing

0 BMW 3

1   Volvo 7

2   Ford 2

Reading Data from Files into Pandas DataFrames

Pandas can read data from files, such as CSV and JSON, and load them into DataFrame objects for analysis

Basic Data Operations with Pandas

Performing basic data analysis operations

Lesson Summary

Recap of the main topics and learning objectives

Definition List

Pandas: A Python library used for data analysis, which provides functions for cleaning, exploring, and manipulating data.
Series: A one-dimensional array in Pandas that can hold data of any type, with index labels.
DataFrame: A two-dimensional data structure in Pandas, similar to a table, with rows and columns for organizing data.
loc: An attribute used in Pandas to access rows in a DataFrame by label or a boolean array.
CSV files: Plain text files that use a comma to separate values, which can be read and manipulated using Pandas.
JSON: A text format for data exchange, resembling Python dictionaries, which can be read into Pandas DataFrames.

Write down 3 things you learned in this lesson.

Have students enter three things they learned in this lesson. With this they can indicate their own learning efficiency of this lesson.

Write down 2 things you want to know more about.

Here, students enter two things they would like to know more about. This not only increases involvement, but also gives them more ownership.

Ask 1 question about something you haven't quite understood yet.

The students indicate here (in question form) with which part of the material they still have difficulty. For the teacher, this not only provides insight into the extent to which the students understand/master the material, but also a good starting point for the next lesson.