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Course Skill Level:


Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Python developers

Who should attend & recommended skills

  • This course is geared for developers who want to explore Python frameworks like pandas, Jupyter notebooks, and Matplotlib to build data pipelines and data visualization.
  • Skill-level: Foundation-level Analysis with pandas skills for Intermediate skilled team members.
  • This is not a basic class.
  • Python: Basic (1-2 years’ experience).

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Analysis with pandas expert instructor, students will learn about and explore:
  • Setting 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
  • Learning how to read different kinds of data into pandas DataFrames for data analysis
  • Manipulating, transforming, and applying formulas to data imported into pandas DataFrames
  • Using pandas to analyze and visualize different kinds of data to gain real-world insights
  • Extracting transformed data form pandas DataFrames and convert it into the formats your application expects
  • Manipulating model time-series data, perform algorithmic trading, derive results on fixed and moving windows, and more

Course breakdown / modules

  • Using advanced options while reading data from CSV files
  • Reading data from Excel files
  • Handling missing data while reading
  • Reading data from other popular formats

  • Introduction to datasets
  • Selecting data from the dataset
  • Sorting a pandas DataFrame
  • Filtering rows of a pandas DataFrame
  • Applying multiple filter criteria to a pandas DataFrame
  • Using the axis parameter in pandas
  • Using string methods in pandas
  • Changing the datatype of a pandas series

  • Modifying a pandas DataFrame using the in place parameter
  • Using the groupby method
  • Handling missing values in pandas
  • Indexing in pandas DataFrames
  • Renaming columns in a pandas DataFrame
  • Removing columns from a pandas DataFrame
  • Working with date and time series data
  • Handling SettingWithCopyWarning
  • Applying a function to a pandas series or DataFrame
  • Merging and concatenating multiple DataFrames into one

  • Visualizing Data Like a Pro
  • Controlling plot aesthetics
  • Choosing the colors for plots
  • Plotting categorical data
  • Plotting with Data-Aware Grids