Pandas for Data Processing

Hands-on with Pandas
Pandas for Data Processing
File Size :
4.38 GB
Total length :
9h 36m



Shrirang Korde


Last update




Pandas for Data Processing

What you’ll learn

The students will learn what is DataScience, how to do data processing using Pandas (with Python)

Pandas for Data Processing


Python language


Pandas is an open source Python package that is used for data science/data analysis and machine learning tasks. It is built using Numpy and provides support for multi-dimensional arrays, dataframes etc. Pandas makes it simple to do many of the time consuming, repetitive tasks associated with working with data. Pandas happens to play important role in Data Science / Data Analysis.Data Science is an essential part of many industries, given the massive amounts of data that are produced, and is one of the most topic. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.  The data used for analysis can come from many different sources and presented in various formats. The following topics are covered using pandas:What is Data ScienceHow to get (real) stock data , how to plot data in JupyterSQL and MySQL interaction using Pandasread csv file and do various operations. Stocks data is considered as examplewrite csv file and associated operationsHandling missing data, Data filtering / Wrangling using PandasGroup by support in PandasConcat support in PandasMerge of Dataframes Pivot Table support in PandasStack / Unstack support in PandasReshape with PandasCross Tab with Pandasexcel supportTime series Support for calendar ( Business Days, Holidays)Time series Support for resampling, indexing time series dataHands-on / Practical with various datasets like: stock /share data, weather data etc


Section 1: Pandas – Basics

Lecture 1 Intro & Course Contents

Lecture 2 Updates to Contents

Lecture 3 Introduction to Data Science

Lecture 4 Hands-on with Jupyter

Lecture 5 Using MySQL- introduction

Lecture 6 MySQL and Pandas (with Practical)

Lecture 7 csv File read operation with Pandas (with Practical)

Lecture 8 csv File write operation with Pandas

Lecture 9 Handling missing data and data wrangling/filtering (part 1, Practical)

Lecture 10 Handling missing data and data wrangling/filtering (part 2, Practical)

Lecture 11 GroupBy (with Practical)

Lecture 12 Concatenation of Dataframes (with Practical)

Section 2: Pandas – Advanced

Lecture 13 Merging of Dataframes (with Practical)

Lecture 14 Pivot Table Support (with Practical)

Lecture 15 Excel Support (xlsx files)

Lecture 16 Stack / Unstack Support

Lecture 17 Stack / Unstack (with Practical)

Lecture 18 Cross Tab (with Practical)

Lecture 19 Reshaping (with Practical)

Lecture 20 TimeSeries – Calendar , Holidays (with Practical , Stock data)

Lecture 21 TimeSeries – Resample (with Practical, Stock data)

Section 3: Python & NumPy

Lecture 22 python contents

Lecture 23 Development Environment and Installation

Lecture 24 Variables and Numbers in Python (with Practical)

Lecture 25 Strings in Python (with Practical)

Lecture 26 Lists in Python (with Practical)

Lecture 27 Conditional Execution (with Practical)

Lecture 28 Loops (with Practical)

Lecture 29 Functions (with Practical)

Lecture 30 Dictionaries in Python (with Practical)

Lecture 31 Tuples in Python (with Practical)

Lecture 32 Exceptions and it’s Handling

Lecture 33 Exceptions and it’s Handling (with Practical)

Lecture 34 Iterators (with Strings, List, Dictionary, Tuple)

Lecture 35 Iterators Practical (with Strings, List, Dictionary, Tuple)

Lecture 36 File Support (with Practical) – part 1

Lecture 37 File Support (with Practical) – part 2

Lecture 38 JSON support (with Practical)

Section 4: Numpy

Lecture 39 NumPy with Practical (part 1)

Lecture 40 NumPy with Practical (part 2)

Beginner Python developers, Data Science students, Students who have some exposure to data analytics,This course is meant typically for those who want to learn Data Science and who have some exposure to python programming, like freshers.

Course Information:

Udemy | English | 9h 36m | 4.38 GB
Created by: Shrirang Korde

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