Data Manipulation with Pandas
- Offered byDataCamp
Data Manipulation with Pandas at DataCamp Overview
Duration | 4 hours |
Mode of learning | Online |
Difficulty level | Beginner |
Credential | Certificate |
Data Manipulation with Pandas at DataCamp Highlights
- 15 Videos
- 56 Exercises
- Data Analyst with Python Track
- Data Manipulation with Python Track
- Data Scientist with Python Track
- Python Programmer Track
Data Manipulation with Pandas at DataCamp Course details
- Discover Data Manipulation with pandas
- Work with pandas Data to Explore Core Data Science Concepts
- Learn to Manipulate DataFrames
- With this course, Learner willll learn why pandas is the world's most popular Python library, used for everything from data manipulation to data analysis
- Learner will explore how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis
- Learner will explore all the core data science concepts
- Learner will learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python
- Learner will start by mastering the pandas basics, including how to inspect DataFrames and perform some fundamental manipulations
- Learner will also learn about aggregating DataFrames, before moving on to slicing and indexing
- Learner will wrap up the course by learning how to visualize the contents of your DataFrames, working with a dataset that contains weekly US avocado sales
- Learner will understand how to use this Python library for data manipulation
- Learner will have an understanding of DataFrames and how to use them, as well as be able to visualize data in Python
Data Manipulation with Pandas at DataCamp Curriculum
Transforming DataFrames
Introducing DataFrames
Inspecting a DataFrame
Parts of a DataFrame
Sorting and subsetting
Sorting rows
Subsetting columns
Subsetting rows
Subsetting rows by categorical variables
New columns
Adding new columns
Combo-attack!
Aggregating DataFrames
Summary statistics
Mean and median
Summarizing dates
Efficient summaries
Cumulative statistics
Counting
Dropping duplicates
Counting categorical variables
Grouped summary statistics
What percent of sales occurred at each store type?
Calculations with .groupby()
Multiple grouped summaries
Pivot tables
Pivoting on one variable
Fill in missing values and sum values with pivot tables
Slicing and Indexing DataFrames
Explicit indexes
Setting and removing indexes
Subsetting with .loc[]
Setting multi-level indexes
Sorting by index values
Slicing and subsetting with .loc and .iloc
Slicing index values
Slicing in both directions
Slicing time series
Subsetting by row/column number
Working with pivot tables
Pivot temperature by city and year
Subsetting pivot tables
Calculating on a pivot table
Creating and Visualizing DataFrames
Visualizing your data
Which avocado size is most popular?
Changes in sales over time
Avocado supply and demand
Price of conventional vs. organic avocados
Missing values
Finding missing values
Removing missing values
Replacing missing values
Creating DataFrames
List of dictionaries
Dictionary of lists
Reading and writing CSVs
CSV to DataFrame
DataFrame to CSV
Wrap-up
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Student Forum
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