In 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 exploratory analysis 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.
Exploratory 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, and sorting data 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, and merge 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 in the course:
Learn how to Work with Excel data, CSV datasets.
Learn how to Handling missing data.
Learn how to read and work with JSON format, HTML files, PICKLE dataset, and SQL-based database.
Learn how to select data from the dataset.
Learn how to sort a pandas DataFrame and filtering rows of a pandas DataFrame.
Learn how to apply multiple filter criteria to a pandas DataFrame.
Learn how to using string methods in pandas.
Learn how to change the datatype of a pandas series.
Learn how to modifying a pandas DataFrame.
Learn how to indexing and renaming columns, and removing columns in and from pandas DataFrame.
Learn how to working with date and time series data.
Learn how to applying a function to a pandas series or DataFrame.
Learn how to merging and concatenating multiple DataFrames into one.
Learn how to control plot aesthetics.
Learn how to choose the colours for plots.
Learn how to plot categorical data.
Learn how to plot with Data-Aware Grids.
Performing exploratory 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:
1. Pandas has a steep learning curve: As you dive deeper into the Pandas library, the learning slope becomes steeper and steeper. This course guides beginners and intermediate users smoothly into every aspect of Pandas.
2. Inadequate documentation: Without proper documentation, it’s difficult to learn a new library. When it comes to advanced functions, Pandas documentation is rarely helpful. This course helps you grasp advanced Pandas techniques easily and saves you time in searching for help.
After this course, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of Exploratory analysis.
Here’s a closer look at the curriculum:
Loading and creating Pandas DataFrames
Displaying your data with basic plots, and 1D, 2D and multidimensional visualizations.
Working with Different Kinds of Datasets
Manipulating, Transforming, and Reshaping Data.
Visualizing Data Like a Pro
Merging Pandas DataFrames
Lastly, this course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice with Pandas too.