Data Manipulation in Python: A Pandas Crash Course
Asim Noaman Lodhi
1:50:48
Description
Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.
What You'll Learn?
- Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.
- Data Visualization with Python
- Create, save and serialise data frames in and out of multiple formats.
- Detect and intelligently fill missing values.
- Merge data sources into a beautiful whole.
- Seamlessly work with data from different time zones.
- Learn the common pitfalls and traps that ensnare beginners and how to avoid them.
Who is this for?
What You Need to Know?
More details
Descriptionn the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.
If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.
Own your data, don’t let your data own you!
When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.
Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.
This course prepares you to do just that!
With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, sorting, blending, and data cleaning approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.
If you want to learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge and aggregate your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.
Here’s what you can expect when you enrolled with your instructor, Ph.D. Samuel Hinton:
Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible.
Achieve better results by spending more time problem-solving and less time data-wrangling.
Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible.
Utilize the latest version of Python and the industry-standard Pandas library.
Performing data analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on:
Who this course is for:
- Python students that want to learn how to manipulate data professionally. Aspiring data analysts and scientists looking to upgrade their skillset. People who would prefer to spend more time solving interesting problems than formatting data. Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade.
n the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.
If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.
Own your data, don’t let your data own you!
When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.
Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.
This course prepares you to do just that!
With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, sorting, blending, and data cleaning approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.
If you want to learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge and aggregate your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.
Here’s what you can expect when you enrolled with your instructor, Ph.D. Samuel Hinton:
Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible.
Achieve better results by spending more time problem-solving and less time data-wrangling.
Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible.
Utilize the latest version of Python and the industry-standard Pandas library.
Performing data analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on:
Who this course is for:
- Python students that want to learn how to manipulate data professionally. Aspiring data analysts and scientists looking to upgrade their skillset. People who would prefer to spend more time solving interesting problems than formatting data. Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade.
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Asim Noaman Lodhi
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 23
- duration 1:50:48
- Release Date 2023/12/16
