Learn Data Analysis with Pandas and Python Free Video Course

Data Analysis with Pandas and Python Course Analyze data quickly and easily with Python’s powerful pandas library! All datasets included beginners welcome!

Data Analysis with Pandas and Python

What you will learn:

  • Perform a multitude of data operations in Python’s popular “pandas” library including grouping, pivoting, joining and more!
  • Learn hundreds of methods and attributes across numerous pandas objects
  • Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Resolve common issues in broken or incomplete data sets

Requirements of Data Analysis with Pandas and Python:

  • Basic / intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables etc)
  • Basic experience with the Python programming language
  • Strong knowledge of data types (strings, integers, floating points, booleans) etc

Description of Data Analysis with Pandas and Python:

The educator knows the material, and has point by point clarification on each subject he talks about. Has lucidity as well, and cautions understudies of possible traps. He has a coherent clarification, and it is not difficult to follow him. I energetically suggest this class, and would investigate taking another class from him. – Diana

This is great, and I can’t supplement the teacher enough. Very clear, significant, and high caliber – with accommodating functional tips and exhortation. Would prescribe this to anybody needing to learn pandas. Exercises are all around developed. I’m really astonished at how all around done this is. I don’t give numerous 5 stars, however this has procured it up until now. – Michael

This course is exceptionally exhaustive, clear, and thoroughly examined. This is the best Udemy course I have taken so far. (This is my third course.) The guidance is amazing! – James

Welcome to the most extensive Pandas course accessible on Udemy! An incredible decision for the two novices and specialists hoping to grow their insight on quite possibly the most famous Python libraries on the planet!

Information Analysis with Pandas and Python offers 19+ long periods of top to bottom video instructional exercises on the most impressive information examination toolbox accessible today. Exercises include:

  • introducing
  • arranging
  • separating
  • gathering
  • totaling
  • de-copying
  • rotating
  • munging
  • erasing
  • blending
  • picturing

and then some!

Why learn pandas?

In the event that you’ve invested energy in a bookkeeping page programming like Microsoft Excel, Apple Numbers, or Google Sheets and are anxious to take your information investigation abilities to the following level, this course is for you!

Information Analysis with Pandas and Python acquaints you with the well known Pandas library based on top of the Python programming language.

Pandas is a stalwart apparatus that permits you to do everything without exception with titanic informational indexes – examining, putting together, arranging, separating, turning, totaling, munging, cleaning, ascertaining, and then some!

I call it “Dominate on steroids”!

Throughout over 19 hours, I’ll make you stride by-venture through Pandas, from establishment to perception! We’ll cover many various techniques, traits, highlights, and functionalities stashed inside this marvelous library. We’ll plunge into huge loads of various datasets, short and since a long time ago, broken and flawless, to show the mind blowing flexibility and proficiency of this bundle.

Information Analysis with Pandas and Python is packaged with many datasets for you to utilize. Make a plunge and track with my exercises to perceive that it is so natural to begin with pandas!

Regardless of whether you’re another information investigator or have gone through years (cough too long cough) in Excel, Data Analysis with pandas and Python offers you an unbelievable prologue to perhaps the most impressive information tool stash accessible today!

Who this course is for:

  • Data analysts and business analysts
  • Excel users looking to learn a more powerful software for data analysis

Course content of Data Analysis with Pandas and Python:

Introduction Of Data Analysis with Pandas and Python:

  • Introduction to Data Analysis with Pandas and Python
  • About Me
  • Completed Course Files
  • MacOS – Download the Anaconda Distribution, our Python development environment
  • MacOS – Install Anaconda Distribution
  • MacOS – Access the Terminal Application
  • MacOS – Create conda Environment and Install pandas and Jupyter Notebook
  • MacOS – Unpack Course Materials + The Start and Shutdown Process
  • Windows – Download the Anaconda Distribution
  • Windows – Install Anaconda Distribution
  • Windows – Create conda Environment and Install pandas and Jupyter Notebook
  • Windows – Unpack Course Materials + The Startdown and Shutdown Process
  • Intro to the Jupyter Notebook Interface
  • Cell Types and Cell Modes in Jupyter Notebook
  • Code Cell Execution in Jupyter Notebook
  • Popular Keyboard Shortcuts in Jupyter Notebook
  • Import Libraries into Jupyter Notebook
  • Troubleshooting Issues with Jupyter Notebook


  • Create Jupyter Notebook for the Series Module
  • Create A Series Object from a Python List
  • Create A Series Object from a Python Dictionary
  • Create a Series Object1 question
  • Intro to Attributes on a Series Object
  • Intro to Methods on a Series Object
  • Parameters and Arguments
  • Create Series from Dataset with the pd.read_csv Method
  • Import Series with the read_csv Method1 question
  • Use the head and tail Methods to Return Rows from Beginning and End of Dataset
  • Passing pandas Objects to Python Built-In Functions
  • Accessing More Series Attributes
  • Use the sort_values method to sort a Series in ascending or descending order
  • Use the inplace Parameter to permanently mutate a pandas data structure
  • Use the sort_index Method to Sort the Index of a pandas Series object
  • The sort_values and sort_index Methods1 question
  • Use Python’s in Keyword to Check for Inclusion in Series values or index
  • Extract Series Values by Index Positiox
  • Extract Series Values by Index Label
  • Extract Series Values by Index Position or Index Label1 question
  • Use the get Method to Retrieve a Value for an index label in a Series
  • Math Methods on Series Objects
  • Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value
  • Use the value_counts Method to See Counts of Unique Values within a Series
  • Use the apply Method to Invoke a Function on Every Series Values
  • The Series#map Method
  • A Review of the Series Module7 questions

DataFrames I: Introduction:

  • Intro to DataFrames I Module
  • Shared Methods and Attributes between Series and DataFrames
  • Differences between Shared Methods
  • Select One Column from a DataFrame
  • Select One Column from a DataFrame1 question
  • Select Two or More Columns from a DataFrame
  • Select Two or More Columns from a DataFrame1 question
  • Add New Column to DataFrame
  • Broadcasting Operations on DataFrames
  • A Review of the value_counts Method
  • Drop DataFrame Rows with Null Values with the dropna Method
  • Delete DataFrame Rows with Missing Values1 question
  • Fill in Null DataFrame Values with the fillna Method
  • Convert DataFrame Column Types with the astype Method
  • Sort a DataFrame with the sort_values Method, Part I
  • Sort a DataFrame with the sort_values Method, Part II
  • The sort_values Method on a DataFrame1 question
  • Sort DataFrame Indexwith the sort_index Method
  • Rank Series Values with the rank Method

DataFrames II: Filtering Data:

  • This Module’s Dataset + Memory Optimization
  • Filter a DataFrame Based on A Condition
  • Filter DataFrame with More than One Condition (AND – &)
  • Filter DataFrame with More than One Condition (OR – |)
  • Check for Inclusion with the isin Method
  • Check for Null and Present DataFrame Values with the isnull and notnull Methods
  • Check For Inclusion Within a Range of Values with the between Method
  • Check for Duplicate DataFrame Rows with the duplicated Method
  • Delete Duplicate DataFrame Rows with the drop_duplicates Method
  • Identify and Count Unique Values with the unique and nunique Methods

DataFrames III: Data Extraction:

  • Intro to the DataFrames III Module + Import Dataset
  • Use the set_index and reset_index methods to define a new DataFrame index
  • Retrieve Rows by Index Label with loc Accessor
  • Retrieve Rows by Index Position with iloc Accessor
  • Passing second arguments to the loc and iloc Accessors
  • Set New Value for a Specific Cell or Cells In a Row
  • Set Multiple Values in a DataFrame
  • Rename Index Labels or Columns in a DataFrame
  • Delete Rows or Columns from a DataFrame
  • Create Random Sample with the sample Method
  • Use the nsmallest / nlargest methods to get rows with smallest / largest values.
  • Filter A DataFrame with the where method
  • Filter A DataFrame with the query method
  • A Review of the apply Method on a pandas Series Object
  • Apply a Function to every DataFrame Row with the apply Method
  • Create a Copy of a DataFrame with the copy Method

Working with Text Data:

  • Intro to the Working with Text Data Section
  • Common String Methods – lower, upper, title, and len
  • Use the str.replace method to replace all occurrences of character with another
  • Filter a DataFrame’s Rows with String Methods
  • More DataFrame String Methods – strip, lstrip, and rstrip
  • Invoke String Methods on DataFrame Index and Columns
  • Split Strings by Characters with the str.split Method
  • More Practice with the str.split method on a Series
  • Exploring the expand and n Parameters of the str.split Method


  • Intro to the MultiIndex Module
  • Create a MultiIndex on a DataFrame with the set_index Method
  • Extract Index Level Values with the get_level_values Method
  • Change Index Level Name with the set_names Method
  • The sort_index Method on a MultiIndex DataFrame
  • Extract Rows from a MultiIndex DataFrame
  • The transpose Method on a MultiIndex DataFrame
  • The .swaplevel() Method
  • The .stack() Method
  • The .unstack() Method, Part 1
  • The .unstack() Method, Part 2
  • The .unstack() Method, Part 3
  • The pivot Method
  • Use the pivot_table method to create an aggregate summary of a DataFrame
  • Use the pd.melt method to create a narrow dataset from a wide one

The GroupBy Objec:

  • Intro to the Groupby Module
  • First Operations with groupby Object
  • Retrieve a group from a GroupBy object with the get_group Method
  • Methods on the Groupby Object and DataFrame Columns
  • Grouping by Multiple Columns
  • The .agg() Method
  • Iterating through Groups

Merging, Joining, and Concatenating DataFrames:

  • Intro to the Merging, Joining, and Concatenating Section
  • The pd.concat Method, Part 1
  • The pd.concat Method, Part 2
  • Inner Joins, Part 1
  • Inner Joins, Part 2
  • Outer Joins
  • Left Joins
  • The left_on and right_on Parameters
  • Merging by Indexes with the left_index and right_index Parameters
  • The .join() Method
  • The pd.merge() Method

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