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Course Description
Discover Data Manipulation with pandas
With 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 Concepts
You’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 DataFrames
By 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.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Identify methods to import, inspect, and subset pandas DataFrames using functions like .head(), .info(), and .loc[].
- Differentiate between aggregation techniques using .groupby() and pivot tables for grouped statistics.
- Recognize how to modify DataFrames by adding new columns, setting indexes, and handling missing values.
- Define common visualization types in pandas, including bar, line, scatter, and histogram plots.
- Assess when to use Boolean masking, sorting, and slicing techniques for efficient data selection.
Prerequisites
Curriculum
Course outline
1
Data Aggregation
Summary Values
You will learn to summarize data by calculating key statistics like totals, averages, and counts, enabling you to extract meaningful insights from raw data.
One Grouping Column
You will learn to break down summary statistics by categories, enabling you to compare metrics across different groups and discover patterns in your data.
Multiple Grouping Columns
You will learn to analyze data across multiple dimensions at once, enabling you to discover nuanced patterns by breaking down summaries along several categories simultaneously.
2
Data Transformation
Basic Transformations
You will learn to create new columns by combining and calculating values from existing data, enabling you to derive ratios and other metrics not available in the original dataset.
Complex Transformations
You will learn to handle multi-step calculations and compute percentages of totals, enabling you to build complex metrics that depend on intermediate results.
3
Data Filtering
Basic Filtering
You will learn to extract specific rows from your data based on conditions, enabling you to focus your analysis on relevant subsets and handle missing values and text patterns.
Multiple Conditions
You will learn to filter data using multiple criteria at once, enabling you to extract precisely the rows you need by combining conditions with AND and OR logic.
Complex Filtering
You will learn to simplify complex filtering by breaking conditions into separate columns, and to extract the opposite of a filter result, making your analysis more transparent and verifiable.
4
Conditional Operations
Conditional Transformation
You will learn to apply different calculations based on specific conditions, enabling you to standardize values, classify data into categories, and handle varied scenarios within your data.
Conditional Aggregation
You will learn to calculate summaries that include only values meeting specific criteria, enabling you to compute nuanced metrics like "average of delayed flights only" within each group.
Data Manipulation with pandas
Course
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FAQs
Is this course suitable for beginners?
Yes! This course is ideal for beginners who want to learn how to manipulate DataFrames.
What is meant by data manipulation in Python?
When we talk about data manipulation, we’re referring to the process of organizing data to make it easier to analyze. In Python, pandas is an open-source library used for data analysis and manipulation.
Is Python good for data manipulation?
Yes, Python is a great language for every stage of data analysis, including data manipulation. There are many Python libraries that can help users organize and visualize their data. Specifically, the pandas library is good for data manipulation.
What is the difference between NumPy and pandas?
pandas can be used to analyze and manipulate all kinds of data, whereas NumPy is primarily used for numerical calculations.
Will I receive a certificate at the end of the course?
Yes, you will receive a certificate of completion once you’ve finished the course, and it will automatically appear on your profile.
What kind of datasets are used in this course?
In this course, you’ll get hands-on experience manipulating real-world datasets, such as Walmart sales figures and global temperature time series.
How long does it take to complete this course?
The course should take about four hours to complete.
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