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Discover Data Manipulation with pandasWith this course, you’ll learn why pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. You’ll explore how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis.
With pandas, you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python.
Work with pandas Data to Explore Core Data Science ConceptsYou’ll start by mastering the pandas basics, including how to inspect DataFrames and perform some fundamental manipulations. You’ll also learn about aggregating DataFrames, before moving on to slicing and indexing.
You’ll wrap up the course by learning how to visualize the contents of your DataFrames, working with a dataset that contains weekly US avocado sales.
Learn to Manipulate DataFramesBy completing this pandas course, you’ll understand how to use this Python library for data manipulation. You’ll have an understanding of DataFrames and how to use them, as well as be able to visualize your data in Python.
Let’s master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns.
In this chapter, you’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables.Summary statistics50 xpMean and median100 xpSummarizing dates100 xpEfficient summaries100 xpCumulative statistics100 xpCounting50 xpDropping duplicates100 xpCounting categorical variables100 xpGrouped summary statistics50 xpWhat percent of sales occurred at each store type?100 xpCalculations with .groupby()100 xpMultiple grouped summaries100 xpPivot tables50 xpPivoting on one variable100 xpFill in missing values and sum values with pivot tables100 xp
Slicing and Indexing DataFrames
Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting.Explicit indexes50 xpSetting and removing indexes100 xpSubsetting with .loc100 xpSetting multi-level indexes100 xpSorting by index values100 xpSlicing and subsetting with .loc and .iloc50 xpSlicing index values100 xpSlicing in both directions100 xpSlicing time series100 xpSubsetting by row/column number100 xpWorking with pivot tables50 xpPivot temperature by city and year100 xpSubsetting pivot tables100 xpCalculating on a pivot table100 xp
Creating and Visualizing DataFrames
Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.Visualizing your data50 xpWhich avocado size is most popular?100 xpChanges in sales over time100 xpAvocado supply and demand100 xpPrice of conventional vs. organic avocados100 xpMissing values50 xpFinding missing values100 xpRemoving missing values100 xpReplacing missing values100 xpCreating DataFrames50 xpList of dictionaries100 xpDictionary of lists100 xpReading and writing CSVs50 xpCSV to DataFrame100 xpDataFrame to CSV100 xpWrap-up50 xp
In the following tracksData Analyst Data Manipulation Data Scientist Data Scientist ProfessionalPython Developer
Maggie MatsuiSee More
Curriculum Manager at DataCamp
Maggie is a Curriculum Manager at DataCamp. She holds a Bachelor's degree in Statistics and Computer Science from Brown University, where she spent lots of time teaching math, programming, and statistics as a tutor and teaching assistant. She's passionate about teaching all things data-related and making programming accessible to everyone.
Richie CottonSee More
Data Evangelist at DataCamp
Richie is a Data Evangelist at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.