Introduction to pandas [track]

Introduction to pandas [track]

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1. Basics

In the first section of the course you will learn two basic objects provided by pandas - Series and DataFrame. You will learn how to create them and change their indexes or column names.

What is pandas?

Indexes and Values

Indexes and Columns

2. Reading and Exploring Data

Often the necessary data are stored in some files. You need to be able to load these data and gain the first insights.

Reading CSV Files

Reading XLS/XLSX Files

Exploring Data [1/3]

Exploring Data [2/3]

Exploring Data [3/3]

3. Accessing DataFrame Values

Once you have loaded the data as Series/DataFrame, you need to be able to get certain data based on needs. You will learn how to access rows, columns, and filter observations by conditions.

Accessing Specific Columns

Accessing Specific Rows

Accessing Rows and Columns

Accessing using .loc[] [1/3]

Accessing using .loc[] [2/3]

Accessing using .loc[] [3/3]

4. Aggregate Functions

In the last section of the course, you will get to know how to calculate some aggregate statistics of your data, such as mean, median, sum, product. Also you will learn how to count values and get only unique observations.

Number of Observations

Unique Observations

Counting Values

Sum and Product

Mean and Median

Minimum and Maximum

Course Content

Introduction to pandas [track]

Introduction to pandas [track]

Accessing using .loc[] [2/3]

Not only you can get certain rows, but you can also get specific columns. To set column/columns you want to receive, pass it as the second parameter between square brackets of .loc[].

If you want to indicate only one column, just place its name. If you want to indicate several columns, you need to pass list of their names.

Note that the first parameter must be set, even if you don't want to indicate specific rows (use : instead).


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# Importing library
import pandas as pd

# Reading csv file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv')
# Get the first five rows (indexes 0-4) and column 'fuelType'
print(df.loc[:4, 'fuelType'])
# Get the 9th - 11th rows (indexes 8, 9, 10) and columns 'model', 'year', and 'price'
print(df.loc[8:10, ['model', 'year', 'price']])

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Section 3. Chapter 7

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