pandas Foundations
Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
- 4 hours
- 15 Videos
- 62 Exercises
- 95,215 Participants
- 5,150 XP
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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.
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1
Data ingestion & inspection
FreeIn 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.
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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.
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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.
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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
Data ingestion & inspection
FreeIn 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.
-
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.
-
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.
-
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!
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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.
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Collaborators
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Yashas Roy
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Hugo Bowne-Anderson
Prerequisites
Datasets
- 1981-2010 NOAA Austin Climate Normals
- July 2015 Austin airport departures (Southwest Airlines)
- Automobile miles per gallon
- Life expectancy at birth (Gapminder)
- Stock data (messy)
- Percentage of bachelor's degrees awarded to women in the USA
- Tips
- Titanic
- 2010 Austin weather
- World Bank World Development Indicators
- World population