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Working in a hands-on learning environment, led by our Analysis with pandas expert instructor, students will learn about and explore: Learn to set up data analysis pipelines with pandas and Jupyter notebooks. Effective techniques for data selection, manipulation, and visualization. Introduction to Matplotlib for interactive data visualization using charts and plots.

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Unlimited Duration

Last Updated

July 29, 2021

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The pandas is a Python library that lets you manipulate, transform, and analyze data. It is a popular framework for exploratory data visualization and analyzing datasets and data pipelines based on their properties. This course will be your practical guide to exploring datasets using pandas. You will start by setting up Python, pandas, and Jupyter Notebooks. You will learn how to use Jupyter Notebooks to run Python code. We then show you how to get data into pandas and do some exploratory analysis, before learning how to manipulate and reshape data using pandas methods. You will also learn how to deal with missing data from your datasets, how to draw charts and plots using pandas and Matplotlib, and how to create some effective visualizations for your audience. Finally, you will wrapup your newly gained pandas knowledge by learning how to import data out of pandas into some popular file formats. By the end of this course, you will have a better understanding of exploratory analysis and how to build exploratory data pipelines with Python.

Course Curriculum

    • Working with Different Kinds of Datasets 00:00:00
    • Using advanced options while reading data from CSV files 00:00:00
    • Reading data from Excel files 00:00:00
    • Handling missing data while reading 00:00:00
    • Reading data from other popular formats 00:00:00
    • Data Selection 00:00:00
    • Introduction to datasets 00:00:00
    • Selecting data from the dataset 00:00:00
    • Sorting a pandas DataFrame 00:00:00
    • Filtering rows of a pandas DataFrame 00:00:00
    • Applying multiple filter criteria to a pandas DataFrame 00:00:00
    • Using the axis parameter in pandas 00:00:00
    • Using string methods in pandas 00:00:00
    • Changing the datatype of a pandas series 00:00:00
    • Manipulating, Transforming, and Reshaping Data 00:00:00
    • Modifying a pandas DataFrame using the inplace parameter 00:00:00
    • Using the groupby method 00:00:00
    • Handling missing values in pandas 00:00:00
    • Indexing in pandas DataFrames 00:00:00
    • Renaming columns in a pandas DataFrame 00:00:00
    • Removing columns from a pandas DataFrame 00:00:00
    • Working with date and time series data 00:00:00
    • Handling Setting With Copy Warning 00:00:00
    • Applying a function to a pandas series or DataFrame 00:00:00
    • Merging and concatenating multiple DataFrames into one 00:00:00
    • Visualizing Data Like a Pro 00:00:00
    • Controlling plot aesthetics 00:00:00
    • Choosing the colors for plots 00:00:00
    • Plotting categorical data 00:00:00
    • Plotting with Data-Aware Grids 00:00:00

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