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Data Analysis with Pandas and Python Online Course

Data Analysis with Pandas and Python

A fantastic option for both beginners and seasoned professionals aiming to deepen their understanding of one of the world's most popular Python libraries! Data Analysis with Pandas and Python provides over 19 hours of comprehensive video tutorials on the most powerful data analysis toolkit available today. The lessons cover a wide range of topics, including installing, sorting, filtering, grouping, aggregating, de-duplicating, pivoting, data cleaning, deleting, merging, visualizing, and much more!

Why learn pandas? If you're familiar with spreadsheet tools like Microsoft Excel, Apple Numbers, or Google Sheets and want to elevate your data analysis expertise, this course is perfect for you!

Data Analysis with Pandas and Python introduces you to the widely-used Pandas library, built on top of the Python programming language. Pandas is a versatile and powerful tool that enables you to work seamlessly with massive datasets, handling tasks like analyzing, organizing, sorting, filtering, pivoting, aggregating, cleaning, and much more. It's often described as "Excel on steroids" for its robust functionality and efficiency!

Course Curriculum

Installation and Setup

  • Introduction to the Course
  • Mac OS - Download the Anaconda Distribution
  • Mac OS - Install Anaconda Distribution
  • Mac OS - Access the Terminal
  • Mac OS - Update Anaconda Libraries
  • Mac OS - Unpack Course Materials + The Startdown and Shutdown Process
  • Windows - Download the Anaconda Distribution
  • Windows - Install Anaconda Distribution
  • Windows - Access the Command Prompt and Update Anaconda Libraries
  • Windows - Unpack Course Materials + The Startdown and Shutdown Process
  • Intro to the Jupyter Notebook Interface
  • Cell Types and Cell Modes
  • Code Cell Execution
  • Popular Keyboard Shortcuts
  • Import Libraries into Jupyter Notebook
  • Python Crash Course, Part 1 - Data Types and Variables
  • Python Crash Course, Part 2 – Lists
  • Python Crash Course, Part 3 – Dictionaries
  • Python Crash Course, Part 4 – Operators
  • Python Crash Course, Part 5 – Functions

Series

  • Create Jupyter Notebook for the Series Module
  • Create A Series Object from a Python List
  • Create A Series Object from a Python Dictionary
  • Intro to Attributes
  • Intro to Methods
  • Parameters and Arguments
  • Import Series with the .read_csv() Method
  • The .head() and .tail() Methods
  • Python Built-In Functions
  • More Series Attributes
  • The .sort_values() Method
  • The inplace Parameter
  • The .sort_index() Method
  • Python's in Keyword
  • Extract Series Values by Index Position
  • Extract Series Values by Index Label
  • The .get() Method on a Series
  • Math Methods on Series Objects
  • The .idxmax() and .idxmin() Methods
  • The .value_counts() Method
  • The .apply() Method
  • The .map() Method

DataFrames I

  • Intro to DataFrames I Module
  • Shared Methods and Attributes between Series and DataFrames
  • Differences between Shared Methods
  • Select One Column from a DataFrame
  • Select Two or More Columns from a DataFrame
  • Add New Column to DataFrame
  • Broadcasting Operations
  • A Review of the .value_counts() Method
  • Drop Rows with Null Values
  • Fill in Null Values with the .fillna() Method
  • The .astype() Method
  • Sort a DataFrame with the .sort_values() Method, Part I
  • Sort a DataFrame with the .sort_values() Method, Part II
  • Sort DataFrame with the .sort_index() Method
  • Rank Values with the .rank() Method

DataFrames II

  • This Module's Dataset + Memory Optimization
  • Filter a DataFrame Based on A Condition
  • Filter with More than One Condition (AND - &)
  • Filter with More than One Condition (OR - |)
  • The .isin() Method
  • The .isnull() and .notnull() Methods
  • The .between() Method
  • The .duplicated() Method
  • The .drop_duplicates() Method
  • The .unique() and .nunique() Methods

DataFrames III

  • Intro to the DataFrames III Module + Import Dataset
  • The .set_index() and .reset_index() Methods
  • Retrieve Rows by Index Label with .loc[]
  • Retrieve Rows by Index Label with .loc[]
  • The Catch-All .ix[] Method
  • Second Arguments to .loc[], .iloc[], and .ix[] Methods
  • Set New Values for a Specific Cell or Row
  • Set Multiple Values in DataFrame
  • Rename Index Labels or Columns in a DataFrame
  • Delete Rows or Columns from a DataFrame
  • Create Random Sample with the .sample() Method
  • The .nsmallest() and .nlargest() Methods
  • Filtering with the .where() Method
  • The .query() Method
  • A Review of the .apply() Method on Single Columns
  • The .apply() Method with Row Values
  • The .copy() Method

Working with Text Data

  • Intro to the Working with Text Data Module
  • Common String Methods - lower, upper, title, and len
  • The .str.replace() Method
  • Filtering with String Methods
  • More String Methods - strip, lstrip, and rstrip
  • String Methods on Index and Columns
  • Split Strings by Characters with .str.split() Method
  • More Practice with Splits
  • The expand and n Parameters of the .str.split() Method

MultiIndex

  • Intro to the MultiIndex Module
  • Create a MultiIndex with the set_index() Method
  • The .get_level_values() Method
  • The .set_names() Method
  • The sort_index() Method
  • Extract Rows from a MultiIndex DataFrame
  • The .transpose() Method and MultiIndex on Column Level
  • The .swaplevel() Method
  • The .stack() Method
  • The .unstack() Method, Part 1
  • The .unstack() Method, Part 2
  • The .unstack() Method, Part 3
  • The .pivot() Method
  • The .pivot_table() Method
  • The pd.melt() Method

GroupBy

  • Intro to the Groupby Module
  • First Operations with groupby Object
  • Retrieve A Group 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

  • Intro to the Merging, Joining, and Concatenating Module
  • The pd.concat() Method, Part 1
  • The pd.concat() Method, Part 2
  • The .append() Method on a DataFrame
  • 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

Working with Dates and Times

  • Intro to the Working with Dates and Times Module
  • Review of Python's datetime Module
  • The Pandas Timestamp Object
  • The Pandas DateTimeIndex Object
  • The pd.to_datetime() Method
  • Create Range of Dates with the pd.date_range() Method, Part 1
  • Create Range of Dates with the pd.date_range() Method, Part 2
  • Create Range of Dates with the pd.date_range() Method, Part 3
  • The .dt Accessor
  • Install Pandas-datareader Library
  • Import Financial Data Set with Pandas_datareader Library
  • Selecting Rows from a DataFrame with a DateTimeIndex
  • Timestamp Object Attributes
  • The .truncate() Method
  • pd.DateOffset Objects
  • More Fun with pd.DateOffset Objects
  • The Pandas Timedelta Object
  • Timedeltas in a Dataset

Panels

  • Intro to the Module + Fetch Panel Dataset from Google Finance
  • The Axes of a Panel Object
  • Panel Attributes
  • Use Bracket Notation to Extract a DataFrame from a Panel
  • Extracting with the .loc, .iloc, and .ix Methods
  • Convert Panel to a MultiIndex DataFrame (and Vice Versa)
  • The .major_xs() Method
  • The .minor_xs() Method
  • Transpose a Panel with the .transpose() Method
  • The .swapaxes() Method

Input and Output

  • Intro to the Input and Output Module
  • Feed pd.read_csv() Method a URL Argument
  • Quick Object Conversions
  • Export DataFrame to CSV File with the .to_csv() Method
  • Install xlrd and openpyxl Libraries to Read and Write Excel Files
  • Import Excel File into Pandas
  • Export Excel File

Visualization

  • Intro to Visualization Module
  • The .plot() Method
  • Modifying Aesthetics with Templates
  • Bar Graphs
  • Pie Charts
  • Histograms

Options and Settings

  • Introduction to the Options and Settings Module
  • Changing Pandas Options with Attributes and Dot Syntax
  • Changing Pandas Options with Methods
  • The precision Option

Conclusion

  • Conclusion


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