Data Manipulation in Python A Pandas Crash Course

Data Manipulation in Python A Pandas Crash Course. Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.

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.

Data Manipulation in Python A Pandas Crash Course Description


  • Basic knowledge of Python

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What you’ll learn

  • Visualise data using methods from histograms to dimensionality reduction.
  • Create, save and serialise data frames in and out of multiple formats.
  • Clean and format data easily.
  • Detect and intelligently fill missing values.
  • Group, aggregate and summarise your data.
  • Merge data sources into a beautiful whole.
  • Pivot and cross-tabulate data like a pro.
  • Intersplice, summarise and investigate time series data.
  • Seamlessly work with data from different time zones.
  • Learn the common pitfalls and traps that ensnare beginners and how to avoid them.

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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