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    The Ultimate Pandas Bootcamp Advanced Python Data Analysis

    BY
    Udemy

    Get acquainted with Pandas library through the boot camp offered by Udemy.

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    Mode

    Online

    Fees

    ₹ 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    The Ultimate Pandas Bootcamp: Advanced Python Data Analysis Course will expose the students to the realm of Pandas and will enrich their understanding of how to use the Pandas library for the process of analyzing, manipulating, and visualizing data. The curriculum of the programme created by Andy Bek, a Software Consultant, will discuss many aspects of Pandas such as data analysis with pandas, computer science, statistics, programming concepts, and the like. The Ultimate Pandas Bootcamp: Advanced Python Data Analysis Online Course is open for enrolment by anyone passionate about Pandas and has a computer. 

    Provided by Udemy, The Ultimate Pandas Bootcamp: Advanced Python Data Analysis Certification will coach the learners on data analysis and will help explore the methods and techniques of Pandas and Python.

    The highlights

    • Online course
    • Downloadable resources
    • Full lifetime access
    • Access on mobile and TV
    • Certificate of completion
    • English videos
    • 30-Day Money-Back Guarantee

    Program offerings

    • 32 hours on-demand video
    • Full lifetime access
    • Access on mobile and tv
    • Certificate of completion
    • 14 articles
    • 87 downloadable resources

    Course and certificate fees

    Fees information
    ₹ 3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    Who it is for

    Python programmer

    What you will learn

    At the end of The Ultimate Pandas Bootcamp: Advanced Python Data Analysis Online Certification, the students will study the Pandas library from the scratch and will get familiarized with Series Methods and Handling, data analysis with Python, DataFrames, Visualizing Data, String Methods In Python, and many more. 

    The syllabus

    Introduction

    • Course Structure
    • Pandas Is Not Single
    • Anaconda
    • Jupyter Notebooks
    • Cloud vs Local
    • Hello, Python
    • NumPy
    • Course Structure

    Series At A Glance

    • Section Intro
    • What Is A Series?
    • Parameters vs Arguments
    • What’s In The Data?
    • The .dtype Attribute
    • BONUS: What Is dtype('o'), Really?
    • Index And RangeIndex
    • Series And Index Names
    • Skill Challenge
    • Solution
    • Another Solution
    • The head() And tail() Methods
    • Extracting By Index Position
    • Accessing Elements By Label
    • BONUS: The add_prefix() And add_suffix() Methods
    • Using Dot Notation
    • Boolean Masks And The .loc Indexer
    • Extracting By Position With .iloc
    • BONUS: Using Callables With .loc And .iloc
    • Selecting With .get()
    • Selection Recap
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Series Methods And Handling

    • Section Intro
    • Reading In Data With read_csv()
    • Series Sizing With .size, .shape, And len()
    • Unique Values And Series Monotonicity
    • The count() Method
    • Accessing And Counting NAs
    • BONUS: Another Approach
    • The Other Side: notnull() And notna()
    • BONUS: Booleans Are Literally Numbers In Python
    • Skill Challenge
    • Solution
    • Dropping And Filling NAs
    • Descriptive Statistics
    • The describe() Method
    • mode() And value_counts()
    • idxmax() And idxmin()
    • Sorting With sort_values()
    • nlargest() And nsmallest()
    • Sorting With sort_index()
    • Skill Challenge
    • Solution
    • Series Arithmetics And fill_value()
    • BONUS: Calculating Variance And Standard Deviation
    • Cumulative Operations
    • Pairwise Differences With diff()
    • Series Iteration
    • Filtering: filter(), where(), And mask()
    • Transforming With update(), apply() And map()
    • Skill Challenge
    • Solution I - Reading Data
    • Solution II - Mean, Median, And Standard Deviation
    • Solution III - Z-scores
    • Section Recap Notebook

    Working With DataFrames

    • Section Intro
    • What Is A DataFrame
    • Creating A DataFrame
    • BONUS - Four More Ways To Build DataFrames
    • The info() Method
    • Reading In Nutrition Data
    • Some Cleanup: Removing The Duplicated Index
    • The sample() Method
    • BONUS - Sampling With Replacement Or Weights
    • BONUS - How Are Random Numbers Generated?
    • DataFrame Axes
    • Changing The Index
    • Extracting From DataFrames By Label
    • DataFrame Extraction by Position
    • Single Value Access With .at And .iat
    • BONUS - The get_loc() Method
    • Skill Challenge
    • Solution
    • More Cleanup: Going Numeric
    • The astype() Method
    • DataFrame replace() + A Glimpse At Regex
    • Part I: Collecting The Units
    • The rename() Method
    • DataFrame dropna()
    • BONUS - dropna() With Subset
    • Part II: Merging Units With Column Names
    • Part III: Removing Units From Values
    • Filtering in 2D
    • DataFrame Sorting
    • Using Series between() With DataFrames
    • BONUS - Min, Max and Idx[MinMax], And Good Foods
    • DataFrame nlargest() And nsmallest()
    • Skill Challenge
    • Solution
    • Another Skill Challenge
    • Solution
    • Section Recap Notebook

    DataFrames In Depth

    • Section Intro
    • Introducing A New Dataset
    • Quick Review: Indexing With Boolean Masks
    • More Approaches To Boolean Masking
    • Binary Operators With Booleans
    • BONUS - XOR and Complement Binary Ops
    • Combining Conditions
    • Conditions As Variables
    • Skill Challenge
    • Solution
    • 2d Indexing
    • Fancy Indexing With lookup()
    • Sorting By Index Or Column
    • Sorting vs. Reordering
    • BONUS - Another Way
    • 15. BONUS - Please Avoid Sorting Like This
    • Skill Challenge
    • Solution
    • Identifying Dupes
    • Removing Duplicates
    • Removing DataFrame Rows
    • BONUS - Removing Columns
    • BONUS - Another Way: pop()
    • BONUS - A Sophisticated Alternative
    • Null Values In DataFrames
    • Dropping And Filling DataFrame NAs
    • BONUS - Methods And Axes With fillna()
    • Skill Challenge
    • Solution
    • Calculating Aggregates With agg()
    • Same-shape Transforms
    • More Flexibility With apply()
    • Element-wise Operations With applymap()
    • Skill Challenge
    • Solution
    • Setting DataFrame Values
    • The SettingWithCopy Warning
    • View vs Copy
    • Adding DataFrame Columns
    • Adding Rows To DataFrames
    • BONUS - How Are DataFrames Stored In Memory
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Working With Multiple DataFrames

    • Section Intro
    • Introducing (Five?) New Datasets
    • Concatenating DataFrames
    • The Duplicated Index Issue
    • Enforcing Unique Indices
    • BONUS - Creating Multiple Indices With concat()
    • Column Axis Concatenation
    • The append() Method: A Special Case Of concat()
    • Concat On Different Columns
    • Skill Challenge
    • Solution
    • The merge() Method
    • The left_on And right_on Params
    • Inner vs Outer Joins
    • Left vs Right Joins
    • One-to-One and One-to-Many Joins
    • Many-to-Many Joins
    • Merging By Index
    • The join() Method
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Going MultiDimensional

    • Section Intro
    • Introducing New Data
    • Inde And RangeInde
    • Index And RangeIndex
    • Creating A MultiIndex
    • MultiIndex From read_csv()
    • Indexing Hierarchical DataFrames
    • Indexing Ranges And Slices
    • BONUS - Use : With pd.IndexSlice!
    • Cross Sections With xs()
    • Skill Challenge
    • Solution
    • The Anatomy Of A MultiIndex Object
    • Adding Another Level
    • Shuffling Levels
    • Removing MultiIndex Levels
    • MultiIndex sort_index()
    • More MultiIndex Methods
    • Reshaping With stack()
    • The Flipside: unstack()
    • BONUS: Creating MultiLevel Columns Manually
    • An Easier Way: transpose()
    • BONUS - What About Panels?
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    GroupBy And Aggregates

    • Section Intro
    • New Data: Game Sales
    • Simple Aggregations Review
    • Conditional Aggregates
    • The Split-Apply-Combine Pattern
    • The groupby() Method
    • The DataFrameGroupBy Object
    • Customizing Index To Group Mappings
    • BONUS - Series groupby()
    • Skill Challenge
    • Solution
    • Iterating Through Groups
    • Handpicking Subgroups
    • MultiIndex Grouping
    • Fine-tuned Aggregates
    • Named Aggregations
    • The filter() Method
    • GroupBy Transformations
    • BONUS - There's Also apply()
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Reshaping With Pivots

    • Section Intro
    • New Data: New York City SAT Scores
    • Pivoting Data
    • Undoing Pivots
    • What About Aggregates?
    • The pivot_table()
    • BONUS: The Problem With Average Percentage
    • Replicating Pivot Tables With GroupBy
    • Adding Margins
    • MultiIndex Pivot Tables
    • Applying Multiple Functions
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Handling Date And Time

    • Section Intro
    • The Python datetime Module
    • Parsing Dates From Text
    • Even Better: dateutil
    • From Datetime To String
    • Performant Datetimes With Numpy
    • The Pandas Timestamp
    • Our Dataset: Brent Prices
    • Date Parsing And DatetimeIndex
    • A Cool Shorcut: read_csv() With parse_dates
    • Indexing Dates
    • Skill Challenge
    • Solution
    • DateTimeIndex Attribute Accessors
    • Creating Date Ranges
    • Shifting Dates With pd.DateOffset
    • BONUS: Timedeltas And Absolute Time
    • Resampling Timeseries
    • Upsampling And Interpolation
    • What About asfreq()?
    • BONUS: Rolling Windows
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Regex And Text Manipulation

    • Section Intro
    • Our Data: Boston Marathon Runners
    • String Methods In Python
    • Vectorized String Operations In Pandas
    • Case Operations
    • Finding Characters And Words
    • Strips And Whitespace
    • String Splitting And Concatenation
    • More Split Parameters
    • Skill Challenge
    • Solution
    • Slicing Substrings
    • Masking With String Methods
    • BONUS: Parsing Indicators With get_dummies()
    • Text Replacement
    • Introduction To Regular Expressions
    • More Regex Concepts
    • How To Approach Regex?
    • Is This A Valid Email?
    • BONUS: What's The Point Of re.compile()?
    • Pandas str contains(), split() And replace() With Regex
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Visualizing Data

    • Section Intro
    • The Art Of Data Visualization
    • The Preliminaries Of matplotlib
    • Line Graphs
    • Bar Charts
    • Pie Plots
    • Histograms
    • Scatter Plots
    • Other Visualization Options
    • BONUS: Data Ink And Chartjunk
    • Skill Challenge
    • Solution
    • Section Recap Notebook

    Data Formats And I/O

    • Section Intro
    • Reading JSON
    • Reading Excel
    • Creating Output: The to_* Family Of Methods
    • BONUS: Introduction To Pickling
    • Pickles In Pandas
    • The Many Other Formats
    • Skill Challenge
    • Solution
    • Section Recap Notebook
    • Reading HTML

    Appendix A - Rapid-Fire Python Fundamentals

    • Section Intro
    • Data Types
    • Variables
    • Arithmetic And Augmented Assignment Operators
    • Ints And Floats
    • Booleans And Comparison Operators
    • Strings
    • Methods
    • Containers I: Lists
    • Lists vs. Strings
    • List Methods And Functions
    • Containers II: Tuples
    • Containers III: Sets
    • Containers IV: Dictionaries
    • Dictionary Keys And Values
    • Membership Operators
    • Controlling Flow: if, else, And elif
    • Truth Value Of Non-booleans
    • For Loops
    • The range() Immutable Sequence
    • While Loops
    • Break And Continue
    • Zipping Iterables
    • List Comprehensions
    • Defining Functions
    • Function Arguments: Positional vs Keyword
    • Lambdas
    • Importing Modules
    • Section Recap Notebook

    Appendix B - Going Local: Installation And Setup

    • Installing Anaconda And Python - Windows
    • Installing Anaconda And Python - Mac
    • Installing Anaconda And Python - Linux

    Instructors

    Andy Bek
    Software Consultant
    Freelancer

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