Pandas: Data Analysis with Pandas: 3-in-1
Pandas: Data Analysis with Pandas: 3-in-1, available at $19.99, has an average rating of 3, with 124 lectures, based on 1 reviews, and has 51 subscribers.
You will learn about 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. This course is ideal for individuals who are 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. or Both novice and advanced users, and contain helpful tips, tricks, and caveats wherever necessary. It is particularly useful for 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. or Both novice and advanced users, and contain helpful tips, tricks, and caveats wherever necessary.
Enroll now: Pandas: Data Analysis with Pandas: 3-in-1
Summary
Title: Pandas: Data Analysis with Pandas: 3-in-1
Price: $19.99
Average Rating: 3
Number of Lectures: 124
Number of Published Lectures: 124
Number of Curriculum Items: 124
Number of Published Curriculum Objects: 124
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will 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.
Who Should Attend
- 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.
Target Audiences
- 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.
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 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 Petrouis 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.
Course Curriculum
Chapter 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
Chapter 2: Data Analysis and Exploration with Pandas
Lecture 1: The Course Overview
Lecture 2: Dissecting the Anatomy of a DataFrame
Lecture 3: Accessing the Main DataFrame Components
Lecture 4: Understanding Data Types
Lecture 5: Selecting a Single Column of Data as a Series
Lecture 6: Calling Series Methods
Lecture 7: Working with Operators on a Series
Lecture 8: Chaining Series Methods Together
Lecture 9: Making the Index Meaningful
Lecture 10: Renaming Row and Column Names
Lecture 11: Creating and Deleting Columns
Lecture 12: Selecting Multiple DataFrame Columns
Lecture 13: Selecting Columns with Methods
Lecture 14: Ordering Column Names Sensibly
Lecture 15: Operating on the Entire DataFrame
Lecture 16: Chaining DataFrame Methods Together
Lecture 17: Working with Operators on a DataFrame
Lecture 18: Comparing Missing Values
Lecture 19: Transposing the Direction of a DataFrame Operation
Lecture 20: Determining College Campus Diversity
Lecture 21: Developing a Data Analysis Routine
Lecture 22: Reducing Memory by Changing Data Types
Lecture 23: Selecting the Smallest of the Largest
Lecture 24: Selecting the Largest of Each Group by Sorting
Lecture 25: Replicating nlargest with sort_values
Lecture 26: Selecting Series Data
Lecture 27: Selecting DataFrame Rows
Lecture 28: Selecting DataFrame Rows and Columns Simultaneously
Lecture 29: Selecting Data with Both Integers and Labels
Lecture 30: Speeding Up Scalar Selection
Lecture 31: Slicing Rows Lazily
Lecture 32: Slicing Lexicographically
Lecture 33: Calculating Boolean Statistics
Lecture 34: Calculating Boolean Statistics
Lecture 35: Filtering with Boolean Indexing
Lecture 36: Replicating Boolean Indexing with Index Selection
Lecture 37: Selecting with Unique and Sorted Indexes
Lecture 38: Gaining Perspective on Stock Prices
Lecture 39: Translating SQL WHERE Clauses
Lecture 40: Determining the Normality of Stock Market Returns
Lecture 41: Improving Readability of Boolean Indexing with the Query Method
Lecture 42: Preserving Series with the WHERE Method
Lecture 43: Preserving Series with the WHERE Method
Lecture 44: Preserving Series with the WHERE Method
Lecture 45: Examining the Index Object
Lecture 46: Producing Cartesian Products
Lecture 47: Exploding Indexes
Lecture 48: Filling Values with Unequal Indexes
Lecture 49: Appending Columns from Different DataFrames
Lecture 50: Highlighting the Maximum Value from Each Column
Lecture 51: Replicating idxmax with Method Chaining
Lecture 52: Finding the Most Common Maximum
Lecture 53: Defining an Aggregation
Lecture 54: Grouping and Aggregating with Multiple Columns and Functions
Lecture 55: Removing the MultiIndex After Grouping
Lecture 56: Customizing an Aggregation Function
Lecture 57: Customizing Aggregating Functions with *args and **kwargs
Lecture 58: Examining the groupby Object
Lecture 59: Filtering for States with a Minority Majority
Lecture 60: Transforming through a Weight Loss Bet
Lecture 61: Calculating Weighted Mean SAT Scores Per State with Apply
Lecture 62: Grouping By Continuous Variables
Lecture 63: Counting the Total Number of Flights Between Cities
Lecture 64: Finding the Longest Streak of On-Time Flights
Lecture 65: Tidying Variable Values as Column Names with Stack
Lecture 66: Tidying Variable Values as Column Names with Melt
Lecture 67: Stacking Multiple Groups of Variables Simultaneously
Lecture 68: Inverting Stacked Data
Lecture 69: Unstacking After a groupby Aggregation
Lecture 70: Replicating pivot_table with a groupby Aggregation
Lecture 71: Renaming Axis Levels for Easy Reshaping
Lecture 72: Tidying When Multiple Variables are Stored as Column Names
Lecture 73: Tidying When Multiple Variables are Stored as Column Values
Lecture 74: Tidying When Two or More Values are Stored in the Same Cell
Lecture 75: Tidying When Variables are Stored in Column Names and Values
Lecture 76: Tidying When Multiple Observational Units are Stored in the Same Table
Lecture 77: Appending New Rows to DataFrames
Lecture 78: Concatenating Multiple DataFrames Together
Lecture 79: Comparing President Trump's and Obama's Approval Ratings
Lecture 80: Understanding the Differences Between concat, join, and merge
Lecture 81: Connecting to SQL Databases
Instructors
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Packt Publishing
Tech Knowledge in Motion
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