Python and Pandas for Data Manipulation Online Training

This intermediate Python and Pandas for Data Manipulation training prepares data practitioners to manipulate and analyze data coming in from multiple sources, big and small, with Pandas.

If it’s your first time stumbling across the name of the Python library that’s used in econometrics for multidimensional structured data set conversion, you should know that Pandas is a deceptively cute name for a really, really powerful data analysis and manipulation tool. If you’re using Python to analyze data, Pandas is arguably the only tool for data munging – transforming raw data into a different format so that it’s more useful.

After this Pandas course, you’ll be selecting, filtering, sorting, cleaning and combining your data quickly and easily.

For anyone who leads an IT team, this open source training can be used to onboard new data practitioners, curated into individual or team training plans, or as an open source reference resource.

Python and Pandas for Data Manipulation: What You Need to Know

This Python and Pandas for Data Manipulation training has videos that cover topics such as:

Extracting meaning from numbers
Managing large data sources and extracting the right data from them
Importing, cleaning, and calculating statistics
Visualizing data and making smarter decisions

Who Should Take Python and Pandas for Data Manipulation Training?

This Python and Pandas for Data Manipulation training is considered associate-level open source training, which means it was designed for data analysts. This Python skills course is valuable for new IT professionals with at least a year of experience with data science and experienced data practitioners looking to validate their data skills.

New or aspiring data practitioners. There’s almost no time in your data science or data analysis career that’s too early to take Pandas training. Of course, it’s important to learn the fundamentals first without skipping ahead to advanced tools. But as this Pandas course shows, data manipulation and importing isn’t just easier – in some cases, it’s only possible – with Pandas.

Experienced data practitioners. If you’ve been working with data for several years already, you’ve probably seen some of the things Pandas can do. Maybe you didn’t even realize it when you saw them, but Pandas and DataFrames make it possible to extract, filter and transform real-world data at an otherworldly level. This course shows you how to use Pandas and advance your data career.

Skill:
Installation and Setup
- 1. Introduction
  3 mins
- 2. Anaconda Installation
  10 mins
- 3. Conda Environments
  7 mins
- 4. Challenge
  1 min
- 5. Challenge Question Answers (optional)
  14 mins
Skill:
Jupyter Notebook
- 1. Introduction
  2 mins
- 2. Brief History
  6 mins
- 3. User Interface
  15 mins
- 4. Data Types Review
  6 mins
- 5. Cell Types
  7 mins
- 6. Shortcuts
  4 mins
- 7. Code Challenge
  1 min
Skill:
Series Introduction
- 1. Introduction
  2 mins
- 2. Create a Series from a list
  8 mins
- 3. Create a Series from a dictionary
  4 mins
- 4. Read CSV files
  12 mins
- 5. Read Excel files
  7 mins
- 6. Head and tail functions
  5 mins
- 7. Series attributes
  4 mins
- 8. Series methods
  4 mins
Skill:
Series Attributes and Methods
- 1. Introduction
  1 min
- 2. Parameter and arguments
  9 mins
- 3. Sorting values
  8 mins
- 4. Series attributes
  8 mins
- 5. Series Methods
  8 mins
- 6. Inplace Mutation
  7 mins
- 7. Sorting Series Indices
  7 mins
- 8. Challenge
  1 min
Skill:
Series Basics
- 1. Introduction
  1 min
- 2. The in keyword
  7 mins
- 3. Extract by position
  7 mins
- 4. Extract by label
  16 mins
- 5. The get() method
  9 mins
- 6. Math methods
  7 mins
- 7. The idxmin() and idxmax() methods
  4 mins
- 8. Unique values
  5 mins
- 9. The apply() method
  6 mins
- 10. Challenge
  1 min
Skill:
DataFrame Introduction
- 1. DataFrame Introduction
  11 mins
- 2. Series shared attributes
  7 mins
- 3. Shared methods
  8 mins
- 4. Extracting columns
  6 mins
- 5. Extracting two or more columns
  5 mins
- 6. Adding columns
  7 mins
- 7. Broadcasting Operations
  7 mins
- 8. DataFrames value_counts( )
  6 mins
- 9. Challenge
  1 min
Skill:
DataFrame Cleaning
- 1. Introduction
  1 min
- 2. Handling null values
  7 mins
- 3. Drop null values
  14 mins
- 4. Impute missing values
  7 mins
- 5. Detect null and not null values
  6 mins
- 6. Challenge
  1 min
Skill:
DataFrame Sorting
- 1. Introduction
  1 min
- 2. Changing data types
  16 mins
- 3. Sorting values
  17 mins
- 4. Sort by indices
  5 mins
- 5. Ranking a Series
  14 mins
- 6. Challenge
  1 min
Skill:
Filtering Data
- 1. Introduction
  2 mins
- 2. Optimization
  14 mins
- 3. Conditional Filtering
  20 mins
- 4. Filtering with AND and OR
  18 mins
- 5. Inclusion method
  8 mins
- 6. Challenge
  1 min
Skill:
Filtering Duplicates
- 1. Introduction
  1 min
- 2. Checking for duplicates
  10 mins
- 3. Drop duplicates
  9 mins
- 4. Unique values
  6 mins
- 5. Inclusion with between()
  11 mins
- 6. Challenge
  1 min
- 7. Solution video
  7 mins
Skill:
Extracting Values
- 1. Introduction
  1 min
- 2. Setting and resetting indices
  14 mins
- 3. Extraction with loc
  14 mins
- 4. Extraction with iloc
  13 mins
- 5. Setting new values
  4 mins
- 6. Set multiple values
  7 mins
- 7. Challenge
  1 min
Skill:
Extraction Methods
- 1. Introduction
  1 min
- 2. The drop method
  9 mins
- 3. Returning smallest and largest values
  10 mins
- 4. The where method
  8 mins
- 5. The query method
  10 mins
- 6. The copy method
  10 mins
- 7. Challenge
  1 min
Skill:
Text Data Basics
- 1. Introduction
  1 min
- 2. Manipulating text data
  14 mins
- 3. String methods
  14 mins
- 4. The replace string method
  15 mins
- 5. Filtering string methods
  11 mins
- 6. Challenge
  1 min
Skill:
Splitting and Stripping Text Data
- 1. Introduction
  2 mins
- 2. Strip strings
  21 mins
- 3. Column and index methods
  7 mins
- 4. Splitting strings
  8 mins
- 5. More splitting
  8 mins
- 6. Challenge
  1 min
Skill:
Grouping Methods
- 1. Introduction
  1 min
- 2. Grouping
  10 mins
- 3. group_by operations
  11 mins
- 4. get_group method
  9 mins
- 5. The group_by methods
  13 mins
- 6. Challenge
  1 min
Skill:
Combining DataFrames
- 1. Introduction
  3 mins
- 2. Combining DataFrames
  8 mins
- 3. Concatenation
  21 mins
- 4. Inner joins
  14 mins
- 5. Outer joins
  11 mins
- 6. Challenge
  1 min
Skill:
Time Series Data
- 1. Introduction
  1 min
- 2. Python Datetime
  9 mins
- 3. Pandas Timestamp
  8 mins
- 4. DatetimeIndex
  6 mins
- 5. The to_datetime method
  11 mins
- 6. Date Ranges Introduction
  8 mins
- 7. Challenge
  1 min
Skill:
Date Ranges
- 1. Introduction
  1 min
- 2. Date ranges part 1
  28 mins
- 3. Date ranges part 2
  15 mins
- 4. Date ranges part 3
  8 mins
- 5. The dt accessor
  12 mins
- 6. Challenge
  1 min
Skill:
DataReader
- 1. Introduction & setup
  9 mins
- 2. Reading cryptocurrency data
  16 mins
- 3. Selecting Datetime rows
  12 mins
- 4. Timestamp attributes & methods
  13 mins
- 5. Challenge
  1 min
Skill:
Visualization
- 1. Introduction
  2 mins
- 2. Matplotlib & PyPlot
  9 mins
- 3. Customizing visualizations
  15 mins
- 4. Creating charts
  13 mins
- 5. Challenge
  1 min

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Introduction

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Course overview

18 HOURS OF TRAINING

20 SKILLS

120 VIDEOS

0 VIRTUAL LABS

0 PRACTICE EXAMS

Jonathan Barrios

Nugget trainer since 2021

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Python and Pandas for Data Manipulation FAQs: Cost, Training, Value

What will you learn in this Python data manipulation and business intelligence training?

This is a thorough and in-depth coding course. You'll learn to code with Python and you'll get a lot of practice writing scripts and programs that sift through huge amounts of data for you. You'll also actually use Pandas in simulated environments and get lots of real-world experience and first-hand practice processing and manipulating big data.

Who should take this Python and Pandas data course?

Data analysts aren't the only people who should take this course – anyone who reads, processes, or analyzes lots of data regularly should be learning Python and know how to use Pandas. Take control of all the data that you use – master manipulating it and putting it to the best use with this course in Python and Pandas.

Is this training in manipulating data with Python and Pandas associated with any certifications?

No, rather than prepare you for an exam or force you to memorize facts on a test, this course is all about using what you learn right away. The virtual simulations and practice environments you use in this course make sure you know how to write Python and use Pandas to manipulate and munge all the data.

What certification should you consider after taking this course in using Python and Pandas?

There aren't very many certifications for Pandas, and the ones that do exist don't have a huge amount of support or recognition. You could use this Python and Pandas training to help you with certifications for Python – like the Python Institute's Certified Entry-level Python Programmer (PCEP) or their Certified Associate in Python Programming (PCAP).

Why should you take this Python data manipulation and business intelligence training?

You should take this Python programming course and Pandas data manipulation training because there's a huge difference between excellence in data and just getting by. Your job moving numbers around and making sense of data will be faster, more enjoyable and produce better results after you learn how to do it with Python and Pandas.

Python for Data Analysis

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Our learners say it best.

Python and Pandas for Data Manipulation FAQs: Cost, Training, Value

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