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Complete Guide To Numpy And Pandas




Pandas IT Institute


All Job Seekers


Online and Offline Classes


Week Days and Week Ends

Duration :

30 to 45 days

Pandas Objectives

•Learn how to code in Pandas.
•Explore different tools used for the Pandas.
•How to write clean production-ready code using Pandas.
•Learn about each and every major Pandas component.
•Learn and Master Pandas with this time saving course
•The best way to learn modern Pandas step-by-step from scratch.
•Learn How to code in Pandas in simple and easy way.
•Learn Pandas. Become Developer in Test and Kick-start your Career in IT.
•Learn how to code in Pandas This Pandas Course is set up for complete beginners!

complete guide to numpy and pandas Course Features

•Advanced Topics covered with examples
•Exercises and handouts after every session
•Accessibility of adequate training resources
•Our Trainers with 15+ years of teaching Experience
•Highly Experienced Trainer with 10+ Years in MNC Company
•Hands On Experience – will be provided during the course to practice
•Our trainers have experience in training End Users & Students & Corporate employees.
•The course is all about familiarizing the trainees with simpler and smarter ways to develop the skills required for Implementation.

Who are eligible for Pandas

•Cloud Computing, Cyber Security, Iot, Big Data, Business Analytics, Data Science, Python, Node.js, React.js, Hadoop, Aws, Qa
•Java Developer, .net sqlserver, oracle, mainframe cobol cics db2 jcl, banking financial services, telecom, ccna ccnp networking mcse, W2
•Javascript, CSS, UI Development, Html5, JSON, MySQL, Spring Boot, Design Patterns, NoSQL, Algorithms, Ui Developer
•php, wordpress, drupal, Iphone Developer, Android, Java, Team Management, Android Developer, Mobile Application Development
•Web Apps, ios/android/windows, Ux Designers, web/mobile developer, html5/css3/javascript/mobile code, testing, automation, manual, mobile, web, ui


•Learning Pandas
•The Course Overview
•Installing and Setting Up Python
•Installing Pandas and Other Dependent Python Modules
•Setting Up and Using Jupyter Notebooks
•Importing Data CSV into Pandas
•Exploring the Imported Dataset
•Manipulating and Reshaping the Dataset
•Handling Missing Data in Pandas
•Analyzing the Imported Dataset
•Using Pandas and Matplotlib to Draw Plots and Charts
•Drawing Bar Charts
•Making Histograms
•Drawing Box Plots
•Drawing Some Other Kinds of Plots with Matplotlib
•Exporting Transformed and Processed Data Out of Pandas
•Exporting to Some Popular File Formats
•Exporting to SQLBased Databases
•Unpacking NumPy and Pandas
•Installing Anaconda
•Exploring Jupyter Notebooks
•Exploring Alternatives to Jupyter
•Package Management with conda
•Setting Up a Database
•Running through NumPy Data Types
•Creating NumPy Arrays
•Slicing Arrays in NumPy
•Arithmetic and Linear Algebra with Arrays
•Employing Array Methods and Functions
•Pandas Are Fun What Is Pandas
•Exploring Series and DataFrame Objects
•Subsetting Your Data
•Arithmetic Function Application Mapping with Pandas
•Handling Missing Data in a Pandas DataFrame
•Managing Your Data by Sorting and Ranking
•Hierarchical Indexing
•Plotting with Pandas
•Modeling and Visualization of Data in Pandas
•Building Financial Model by Calculating and Comparing Rates of Return
•Calculating Securitys Rate of Return
•Calculating the Rate of Return of a Portfolio of Securities
•Calculating the Rate of Return of Indices
•Working with Panel Objects and Attributes
•Working with Extraction of Data Frames from Panels
•Convert Panels to Multiindex Data Frame Transpose Panel
•Export Data Frames to CSV Files with the tocsv Method
•Import Excel Files into Pandas and Export Excel Files
•Using Date and Time Functions for Pandas
•Visual Exploratory Data Analysis
•Pandas Line Plot
•Pandas Scatter Plot
•Pandas Box Plot
•Pandas Histogram Plot
•Pandas Pie Plot
•Pandas Area Plot
•Pandas Heatmap
•Pandas Bar Plot
•Mastering Python Data Analysis with Pandas
•Reading and Writing Data in Text Format
•XML and HTML Web Scrapping
•Interacting with Databases
•Binary Data Formats Excel and HDF
•Data Wrangling Munging and Pandas Data Structures
•Combining and Merging Data Sets
•Reshaping Pivoting and Advanced Indexing Data Sets
•Data Transformation on Data Sets
•String Manipulations on Data Sets
•Working with Missing Data Sets
•Data Aggregation on Data Sets
•GroupWise Operations on Data Sets
•Statistical Functions Example
•Windows Functions Example
•Applying Multiple and Different Functions to Dataframe Columns
•Exponentially Weighted Windows
•Importing Data (CSV) into Pandas
•Exporting to SQL-Based Databases
•Pandas Are Fun! What Is Pandas?
•Arithmetic, Function Application, Mapping with Pandas
•Calculating Security’s Rate of Return
•Convert Panels to Multi-index Data Frame, Transpose Panel
•Export Data Frames to CSV Files with the .to_csv() Method
•Import Excel Files into Pandas, and Export Excel Files
•Binary Data Formats (Excel and HDF5)
•Data Wrangling/ Munging and Pandas Data Structures
•Reshaping, Pivoting, and Advanced Indexing Data Sets
•Group-Wise Operations on Data Sets