Last updated 6/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.53 GB | Duration: 8h 11m

Get insights and solutions to common data problems while working on real-world datasets using Pandas library

What you'll learn

Use Pandas to make predictions using Machine Learning and scikit-learn

Prepare real-world messy datasets for machine learning

Master analyzing and visualizing different kinds of data using Pandas to gain real-world insights

Manipulate quantitative financial data and model -series data, perform algorithmic trading, derive results on fixed and moving windows, and more

Explore the most crucial and common operations that you will perform during data analysis to build customized functions to apply to your groups.

Restructure and tidy data to make data analysis and visualization easier

Perform algorithmic trading, derive results on fixed and moving windows, and more.

Get the hang of taking out transformed data out of Pandas data frames and into the formats your application expects.

Requirements

Prior programming experience in Python will be helpful to get the most out of this course.

Basic understanding of Pandas will be helpful.

Fundamental knowledge of Python. It is assumed that you are familiar with all the common built-in data containers in Python, such as lists, sets, dictionaries, and tuples.

Description

Are you looking for a gigantic boost in your productivity? Are you searching for some interesting and fun tricks to solve your data problems? If so, then this course is indeed a perfect choice for you. This course provides you with unique, idiomatic, and amazing solutions for both fundamental and advanced data manipulation tasks with Pandas.

Pandas is a popular Open Source Python package that provides fast, high performance data structures for perfog efficient data manipulation and analysis. It has quickly emerged as a popular choice of tool for analysts to solve real-world analytical problems. The Pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features.

This comprehensive 3-in-1 course is a step-by-step, a highly practical course showing you the whys and how's of applying Pandas for your data analysis tasks. Solve most complex scientific computing problems with ease using the power of Pandas. Manipulate, analyze and visualize your data using the popular Pandas library. Enhance your data exploration and machine learning skills by gaining surprising insights from Pandas and using expert tips and tricks.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learning Pandas, covers powerful Data Analysis with Python Library in an engaging and exciting way. Analyze and model your data, and organize the results of your analysis in the form of plots or other visualization means. Throughout the course, you’ll implement simple yet highly effective examples and use-cases which are relevant in the real-world scenario, as you build on your understanding of Pandas. By the end of this course, you’ll have a firm understanding of the basics of Pandas. You’ll be ready to start using Pandas for different data science tasks with confidence.

The second course, Data Analysis and Exploration with Pandas, covers idiomatic solutions to common data problems while working on real-world datasets to get surprising insights from the Pandas library. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced solutions combine several different features across the Pandas library to generate results.

The third course, Advanced Techniques for Exploring Data Sets with Pandas, covers popular datasets in R, while mastering advanced techniques used for them. Manipulate and reshape data using Pandas methods. You’ll 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 cool visualizations for your audience. Finally, you will wrap-up your newly gained Pandas knowledge by learning how to get data out of Pandas into some popular file formats.

By the end of the course, you’ll get insights and solutions to common data problems while working on real-world datasets using Pandas library.About the Authors

Harish Garg is a Data Scientist and a Lead Software Developer with 17 years' software industry experience. He worked for McAfeeIntel for 11+ years before starting his own software consultancy. He is an expert in creating data visualizations using R, Python, and web-based visualization libraries.

Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his exploring data. Some of his projects included using targeted sennt analysis to discover the root cause of part failures from eeer text, developing customized client/server dash boarding applications, and real- web services to avoid mispricing sales items. Ted received his Master's degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about Pandas on Stack Overflow.

Overview

Section 1: Learning Pandas

Lecture 1 The Course Overview

Lecture 2 Installing and Setting Up Python

Lecture 3 Installing Pandas and Other Dependent Python Modules

Lecture 4 Setting Up and Using Jupyter Notebooks

Lecture 5 Importing Data (CSV) into Pandas

Lecture 6 Exploring the Imported Dataset

Lecture 7 Manipulating and Reshaping the Dataset

Lecture 8 Handling Missing Data in Pandas

Lecture 9 Analyzing the Imported Dataset

Lecture 10 Using Pandas and Matplotlib to Draw Plots and Charts

Lecture 11 Drawing Bar Charts

Lecture 12 Making Histograms

Lecture 13 Drawing Box Plots

Lecture 14 Drawing Some Other Kinds of Plots with Matplotlib

Lecture 15 Exporting Transformed and Processed Data Out of Pandas

Lecture 16 Exporting to Some Popular File Formats

Lecture 17 Exporting to SQL-Based Databases

Section 2: Data Analysis and Exploration with Pandas

Lecture 18 The Course Overview

Lecture 19 Dissecting the Anatomy of a DataFrame

Lecture 20 Accessing the Main DataFrame Components

Lecture 21 Understanding Data Types

Lecture 22 Selecting a Single Column of Data as a Series

Lecture 23 Calling Series Methods

Lecture 24 Working with Operators on a Series

Lecture 25 Chaining Series Methods Together

Lecture 26 Making the Index Meaningful

Lecture 27 Renaming Row and Column Names

Lecture 28 Creating and Deleting Columns

Lecture 29 Selecting Multiple DataFrame Columns

Lecture 30 Selecting Columns with Methods

Lecture 31 Ordering Column Names Sensibly

Lecture 32 Operating on the Entire DataFrame

Lecture 33 Chaining DataFrame Methods Together

Lecture 34 Working with Operators on a DataFrame

Lecture 35 Comparing Missing Values

Lecture 36 Transposing the Direction of a DataFrame Operation

Lecture 37 Deteing College Campus Diversity

Lecture 38 Developing a Data Analysis Routine

Lecture 39 Reducing Memory by Chag Data Types

Lecture 40 Selecting the Smallest of the Largest

Lecture 41 Selecting the Largest of Each Group by Sorting

Lecture 42 Replicating nlargest with sort_values

Lecture 43 Selecting Series Data

Lecture 44 Selecting DataFrame Rows

Lecture 45 Selecting DataFrame Rows and Columns Simultaneously

Lecture 46 Selecting Data with Both Integers and Labels

Lecture 47 Speeding Up Scalar Selection

Lecture 48 Slicing Rows Lazily

Lecture 49 Slicing Lexicographically

Lecture 50 Calculating Boolean Statistics

Lecture 51 Calculating Boolean Statistics

Lecture 52 Filtering with Boolean Indexing

Lecture 53 Replicating Boolean Indexing with Index Selection

Lecture 54 Selecting with Unique and Sorted Indexes

Lecture 55 Gaining Perspective on Stock Prices

Lecture 56 Translating SQL WHERE Clauses

Lecture 57 Deteing the Normality of Stock Market Returns

Lecture 58 Improving Readability of Boolean Indexing with the Query Method

Lecture 59 Preserving Series with the WHERE Method

Lecture 60 Preserving Series with the WHERE Method

Lecture 61 Preserving Series with the WHERE Method

Lecture 62 Examining the Index Object

Lecture 63 Producing Cartesian Products

Lecture 64 Exploding Indexes

Lecture 65 Filling Values with Unequal Indexes

Lecture 66 Appending Columns from Different DataFrames

Lecture 67 Highlighting the Maximum Value from Each Column

Lecture 68 Replicating idxmax with Method Chaining

Lecture 69 Finding the Most Common Maximum

Lecture 70 Defining an Aggregation

Lecture 71 Grouping and Aggregating with Multiple Columns and Functions

Lecture 72 Removing the Muldex After Grouping

Lecture 73 Customizing an Aggregation Function

Lecture 74 Customizing Aggregating Functions with *args and kwargs

Lecture 75 Examining the groupby Object

Lecture 76 Filtering for States with a Minority Majority

Lecture 77 Transfog through a Weight Loss Bet

Lecture 78 Calculating Weighted Mean SAT Scores Per State with Apply

Lecture 79 Grouping By Continuous Variables

Lecture 80 Counting the Total Number of Flights Between Cities

Lecture 81 Finding the Longest Streak of On- Flights

Lecture 82 Tidying Variable Values as Column Names with Stack

Lecture 83 Tidying Variable Values as Column Names with Melt

Lecture 84 Stacking Multiple Groups of Variables Simultaneously

Lecture 85 Inverting Stacked Data

Lecture 86 Unstacking After a groupby Aggregation

Lecture 87 Replicating pivot_table with a groupby Aggregation

Lecture 88 Renaming Axis Levels for Easy Reshaping

Lecture 89 Tidying When Multiple Variables are Stored as Column Names

Lecture 90 Tidying When Multiple Variables are Stored as Column Values

Lecture 91 Tidying When Two or More Values are Stored in the Same Cell

Lecture 92 Tidying When Variables are Stored in Column Names and Values

Lecture 93 Tidying When Multiple Observational Units are Stored in the Same Table

Lecture 94 Appending New Rows to DataFrames

Lecture 95 Concatenating Multiple DataFrames Together

Lecture 96 Comparing President Trump's and Obama's Approval Ratings

Lecture 97 Understanding the Differences Between concat, join, and merge

Lecture 98 Connecting to SQL Databases

Section 3: Advanced Techniques for Exploring Data Sets with Pandas

Lecture 99 The Course Overview

Lecture 100 Using Advanced Options While Reading Data from CSV Files

Lecture 101 Reading Data from Excel Files

Lecture 102 Reading Data from Some Other Popular Formats

Lecture 103 Using Pandas Series Data Structure to Select a Subset of the Data

Lecture 104 Selecting Multiple Rows and Columns from a Pandas DataFrame

Lecture 105 Sorting a Pandas DataFrame or a Series

Lecture 106 Filtering Rows of a Pandas DataFrame by Column Value

Lecture 107 Applying Multiple Filter Criteria to a Pandas DataFrame

Lecture 108 Using the "axis" Parameter in Pandas

Lecture 109 Using String Methods in Pandas

Lecture 110 Chag the Data Type of a Pandas Series

Lecture 111 Modifying a Pandas DataFrame “inplace”

Lecture 112 Using the "groupby" Method

Lecture 113 Handling Missing Values in Pandas

Lecture 114 Indexing in Pandas DataFrames

Lecture 115 Indexing in Pandas DataFrames

Lecture 116 Removing Columns from a Pandas DataFrame

Lecture 117 Working with Dates and s Data

Lecture 118 Handling SettingWithCopyWarning

Lecture 119 Applying a Function to a Pandas Series or DataFrame

Lecture 120 Meg and Concatenating Multiple DataFrames into One

Lecture 121 Controlling Plot Aesthetics

Lecture 122 Choosing the Colors for the Plots

Lecture 123 Plotting Categorical Data

Lecture 124 Plotting with Data Aware Grids

Budding data scientist looking to learn the popular Pandas library, or a Python developer looking to step into the world of data analysis, this video is the ideal resource you need to get started. This course is for data scientists, analysts, and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner.,Both novice and advanced users, and contain helpful tips, tricks, and caveats wherever necessary.

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