Data Manipulation In Python: A Pandas Crash Course by Asim Noaman

Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.

What you'll learn
Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.
Data Visualization with Python
Create, save and serialise data frames in and out of multiple formats.
Detect and intelligently fill missing values.
Merge data sources into a beautiful whole.
Seamlessly work with data from different time zones.
Learn the common pitfalls and traps that ensnare beginners and how to avoid them.

Requirements
Basic knowledge of Python

Description
n the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.Own your data, don’t let your data own you!When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.This course prepares you to do just that!With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, sorting, blending, and data cleaning approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.If you want to learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge and aggregate your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.Here’s what you can expect when you enrolled with your instructor, Ph.D. Samuel Hinton:Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible.Achieve better results by spending more time problem-solving and less time data-wrangling.Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible.Utilize the latest version of Python and the industry-standard Pandas library.Performing data analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Python & Jupyter NoteBook Installation

Lecture 3 Introduction to Data Analysis

Lecture 4 Real Time Business Intelligence Problems

Lecture 5 Introduction to Pandas Library

Section 2: Data Manipulation with Pandas

Lecture 6 Importing Libraries in JupyterNote Book

Lecture 7 How to View Dataset

Lecture 8 How to fetch Columns

Lecture 9 How to Perform Descriptive Analysis

Lecture 10 How to Identify Unique Values

Lecture 11 How to Filter the dataset

Lecture 12 How to filter Specific Numbers of Records

Lecture 13 How to Apply Logical Condition

Lecture 14 How to Replace Null Values

Section 3: Data Visualization with Pandas

Lecture 15 How to Create Count plot

Lecture 16 How to Create Histogram

Lecture 17 How to Create Bar Plot

Lecture 18 How to Create Scatter Plot

Lecture 19 How to Create Box Plot

Lecture 20 Pandas Library chearsheet

Lecture 21 What is Data Cleaning

Section 4: Data Analysis with Power Query

Lecture 22 Live Data Analysis with Power Query ( Ms Excel)

Lecture 23 How to Append Multiple Excel Sheets

Python students that want to learn how to manipulate data professionally. Aspiring data analysts and scientists looking to upgrade their skillset. People who would prefer to spend more time solving interesting problems than formatting data. Old hands at programming that want to see what new methods and industry-leading tools are at their fingertips in the new decade.

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