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Exploratory Analysis with pandas

  • Course Code: Data Science - Exploratory Analysis with pandas
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 1 Days Audience: This course is geared for those who wants to Explore Python frameworks like pandas, Jupyter notebooks, and Matplotlib to build data pipelines and data visualization.

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level Analysis with pandas skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Explore Python frameworks like pandas, Jupyter notebooks, and Matplotlib to build data pipelines and data visualization. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Learn how to read different kinds of data into pandas DataFrames for data analysis 
  • Manipulate, transform, and apply formulas to data imported into pandas DataFrames 
  • Use pandas to analyze and visualize different kinds of data to gain real-world insights 
  • Extract transformed data form pandas DataFrames and convert it into the formats your application expects 
  • Manipulate model time-series data, perform algorithmic trading, derive results on fixed and moving windows, and more 

Audience & Pre-Requisites 

This course is designed for developers wants to Explore Python frameworks like pandas, Jupyter notebooks, and Matplotlib to build data pipelines and data visualization 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Working with Different Kinds of Datasets 
  • Working with Different Kinds of Datasets 
  • 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 
  1. Data Selection 
  • Data Selection 
  • 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 
  1. Manipulating, Transforming, and Reshaping Data 
  • Manipulating, Transforming, and Reshaping Data 
  • Modifying a pandas DataFrame using the inplace 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 
  1. Visualizing Data Like a Pro 
  • Visualizing Data Like a Pro 
  • Controlling plot aesthetics 
  • Choosing the colors for plots 
  • Plotting categorical data 
  • Plotting with Data-Aware Grids 

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