Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • The Pandas Workshop
  • Toc
  • feedback
The Pandas Workshop

The Pandas Workshop

By : Blaine Bateman, Saikat Basak , Thomas Joseph, William So
4.8 (16)
close
The Pandas Workshop

The Pandas Workshop

4.8 (16)
By: Blaine Bateman, Saikat Basak , Thomas Joseph, William So

Overview of this book

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Table of Contents (21 chapters)
close
1
Part 1 – Introduction to pandas
6
Part 2 – Working with Data
11
Part 3 – Data Modeling
15
Part 4 – Additional Use Cases for pandas

Solution 2.1

Perform the following steps to complete the activity:

  1. Import the pandas library:
    import pandas as pd
  2. Read the US_GDP.csv file from the Datasets directory into a DataFrame named GDP_data. The data is stored in two columns, date and GDP, and the date is read in (by default) as the object type. The goal of this activity is to first convert the date column into a timestamp and then set this column as the index. Finally, save the updated dataset to a new file:
    fname = '../Datasets/US_GDP.csv'
    GDP_data = pd.read_csv(fname)

    Note

    Please change the path of the dataset file (highlighted) based on where you have downloaded it in your system. You can download the file from The-Pandas-Workshop/US_GDP.csv at master · PacktWorkshops/The-Pandas-Workshop · GitHub.

  3. Display the head of GDP_data so that you can see the format of the data in the file:
    GDP_data.head()

The output should look as follows:

Figure 15.4 – First five...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete