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Pandas Data Analysis With Pandas 3-In-1

TOP 110


Alpha and Omega
Jan 21, 2021
Reaction score
3 years of service

Last updated 6/2018
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: 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 time-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.
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.
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 performing 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 McAfee\Intel 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 time exploring data. Some of his projects included using targeted sentiment analysis to discover the root cause of part failures from engineer text, developing customized client/server dash boarding applications, and real-time 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.
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 Determining College Campus Diversity
Lecture 38 Developing a Data Analysis Routine
Lecture 39 Reducing Memory by Changing 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 Determining 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 MultiIndex 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 Transforming 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-Time 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 Changing 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 Times Data
Lecture 118 Handling SettingWithCopyWarning
Lecture 119 Applying a Function to a Pandas Series or DataFrame
Lecture 120 Merging 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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