paid course

pandas Foundations

Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

  • 4 hours
  • 15 Videos
  • 62 Exercises
  • 72,800 Participants
  • 5,150 XP

Course Description

Pandas DataFrames are the most widely used in-memory representation of complex data collections within Python. Whether in finance, scientific fields, or data science, a familiarity with Pandas is essential. This course teaches you to work with real-world data sets containing both string and numeric data, often structured around time series. You will learn powerful analysis, selection, and visualization techniques in this course.

Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

Course Outline

  1. 1

    Data ingestion & inspection

    Free

    In this chapter, you will be introduced to Panda's DataFrames. You will use Pandas to import and inspect a variety of datasets, ranging from population data obtained from The World Bank to monthly stock data obtained via Yahoo! Finance. You will also practice building DataFrames from scratch, and become familiar with Pandas' intrinsic data visualization capabilities.

  2. Time series in pandas

    In this chapter, you will learn how to manipulate and visualize time series data using Pandas. You will become familiar with concepts such as upsampling, downsampling, and interpolation. You will practice using Pandas' method chaining to efficiently filter your data and perform time series analyses. From stock prices to flight timings, time series data are found in a wide variety of domains and being able to effectively work with such data can be an invaluable skill.

  3. Exploratory data analysis

    Having learned how to ingest and inspect your data, you will next explore it visually as well as quantitatively. This process, known as exploratory data analysis (EDA), is a crucial component of any data science project. Pandas has powerful methods that help with statistical and visual EDA. In this chapter, you will learn how and when to apply these techniques.

  4. Case Study - Sunlight in Austin

    Working with real-world weather and climate data, in this chapter you will bring together and apply all of the skills you have acquired in this course. You will use Pandas to manipulate the data into a form usable for analysis, and then systematically explore it using the techniques you learned in the prior chapters. Enjoy!

  1. 1

    Data ingestion & inspection

    Free

    In this chapter, you will be introduced to Panda's DataFrames. You will use Pandas to import and inspect a variety of datasets, ranging from population data obtained from The World Bank to monthly stock data obtained via Yahoo! Finance. You will also practice building DataFrames from scratch, and become familiar with Pandas' intrinsic data visualization capabilities.

  2. Exploratory data analysis

    Having learned how to ingest and inspect your data, you will next explore it visually as well as quantitatively. This process, known as exploratory data analysis (EDA), is a crucial component of any data science project. Pandas has powerful methods that help with statistical and visual EDA. In this chapter, you will learn how and when to apply these techniques.

  3. Time series in pandas

    In this chapter, you will learn how to manipulate and visualize time series data using Pandas. You will become familiar with concepts such as upsampling, downsampling, and interpolation. You will practice using Pandas' method chaining to efficiently filter your data and perform time series analyses. From stock prices to flight timings, time series data are found in a wide variety of domains and being able to effectively work with such data can be an invaluable skill.

  4. Case Study - Sunlight in Austin

    Working with real-world weather and climate data, in this chapter you will bring together and apply all of the skills you have acquired in this course. You will use Pandas to manipulate the data into a form usable for analysis, and then systematically explore it using the techniques you learned in the prior chapters. Enjoy!

Team Anaconda
Team Anaconda

Data Science Training

This course was created in collaboration with Anaconda. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python data science and machine learning. It's the industry standard for developing, testing, and training on a single machine.

See More

Course Instructor

Team Anaconda
Team Anaconda

Data Science Training

This course was created in collaboration with Anaconda. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python data science and machine learning. It's the industry standard for developing, testing, and training on a single machine.

See More
Collaborator(s)
  • Yashas Roy

    Yashas Roy

  • Hugo Bowne-Anderson

    Hugo Bowne-Anderson

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