Data Analysis with Pandas and Python

Table of contents

1. Chapter 1 : Installation and Setup
   1. Introduction to the Course
   2. macOS - Download and Install the Anaconda Distribution
   3. Windows - Download and Install the Anaconda Distribution
   4. Use Anaconda Navigator to Create a New Environment
   5. Unpack Course Materials + The Startdown and Shutdown Process
   6. Intro to the Jupyter Lab Interface
   7. Code Cell Execution
   8. Import Libraries into Jupyter Lab
2. Chapter 2 : Python Crash Course
   1. Comments
   2. Basic Data Types
   3. Operators
   4. Variables
   5. Built-in Functions
   6. Custom Functions
   7. String Methods
   8. Lists
   9. Index Positions and Slicing
   10. Dictionaries
   11. Classes
   12. Navigating Libraries using Jupyter Lab
3. Chapter 3 : Series
   1. Create a Series Object from a List
   2. Create a Series Object from a Dictionary
   3. Intro to Series Methods
   4. Intro to Attributes
   5. Parameters and Arguments
   6. Import Series with the pd.read_csv Function
   7. The head and tail Methods
   8. Passing Series to Python Built-In Functions
   9. Check for Inclusion with Python's in Keyword
   10. The sort_values Method
   11. The sort_index Method
   12. Extract Series Values by Index Position
   13. Extract Series Values by Index Label
   14. The get Method
   15. Overwrite a Series Value
   16. The copy Method
   17. Math Methods on Series Objects
   18. Broadcasting
   19. The value_counts Method
   20. The apply Method
   21. The map Method
4. Chapter 4 : DataFrames I: Introduction
   1. Methods and Attributes between Series and DataFrames
   2. Differences between Shared Methods
   3. Select One Column from a DataFrame
   4. Select Multiple Columns from a DataFrame
   5. Add New Column to DataFrame
   6. A Review of the value_counts Method
   7. Drop DataFrame Rows with Missing Values
   8. Fill in Missing Values with the fillna Method
   9. The astype Method I
   10. The astype Method II
   11. Sort a DataFrame with the sort_values Method I
   12. Sort a DataFrame with the sort_values Method II
   13. Sort DataFrame with the sort_index Method
   14. Rank Series Values with the rank Method
5. Chapter 5 : DataFrames II: Filtering Data
   1. This Module's Dataset + Memory Optimization
   2. Filter a DataFrame Based on a Condition
   3. Filter with More than One Condition (AND - )
   4. Filter with More than One Condition (OR - |)
   5. The isin Method
   6. The isnull and notnull Methods
   7. The between Method
   8. The duplicated Method
   9. The drop_duplicates Method
   10. The unique and nunique Methods
6. Chapter 6 : DataFrames III: Data Extraction
   1. This Module's Dataset
   2. The set_index and reset_index Methods
   3. Retrieve Rows by Index Position with iloc Accessor
   4. Retrieve Rows by Index Label with loc Accessor
   5. Second Arguments to loc and iloc Accessors
   6. Overwrite Value in a DataFrame
   7. Overwrite Multiple Values in a DataFrame
   8. Rename Index Labels or Columns in a DataFrame
   9. Delete Rows or Columns from a DataFrame
   10. Create Random Sample with the sample Method
   11. The nsmallest and nlargest Methods
   12. Filtering with the where Method
   13. The apply Method with DataFrames
7. Chapter 7 : Working with Text Data
   1. This Module's Dataset
   2. Common String Methods
   3. Filtering with String Methods
   4. String Methods on Index and Columns
   5. The split Method
   6. More Practice with Splits
   7. The expand and n Parameters of the split Method
8. Chapter 8 : MultiIndex
   1. Intro to the MultiIndex Module
   2. Create a MultiIndex
   3. Extract Index Level Values
   4. Rename Index Lebels
   5. The sort_index Method on a MultiIndex DataFrame
   6. Extract Rows from a MultiIndex DataFrame
   7. The transpose Method
   8. The stack Method
   9. The unstack Method
   10. The pivot Method
   11. The melt Method
   12. The pivot_table Method
9. Chapter 9 : GroupBy
   1. Intro to the GroupBy Module
   2. The groupby Method
   3. Retrieve A Group with the get_group Method
   4. Methods on the GroupBy Object
   5. Grouping by Multiple Columns
   6. The agg Method
   7. Iterating through Groups
10. Chapter 10 : Merging DataFrames
    1. Intro to the Merging DataFrames Module
    2. The pd.concat Function I
    3. The pd.concat Function II
    4. Left Joins
    5. The left_on and right_on Parameters
    6. Inner Joins I
    7. Inner Joins II
    8. Full-Outer Joins
    9. Merging by Indexes with the left_index and right_index Parameters
    10. The join Method
11. Chapter 11 : Working with Dates and Times
    1. Intro to the Working with Dates and Times Module and Review of Python's datetime
    2. The Timestamp and DatetimeIndex Objects
    3. Create Range of Dates with pd.date_range Function
    4. The dt Attribute
    5. Selecting Rows from a DataFrame with DatetimeIndex
    6. The DateOffset Object
    7. Specialized Date Offsets
    8. Timedeltas
12. Chapter 12 : Input and Output
    1. Intro to the Input and Output Module
    2. Export DataFrame to CSV File
    3. Install openpyxl Library to Read and Write Excel Files
    4. Import Excel File into pandas
    5. Export Excel File from pandas
13. Chapter 13 : Visualization
    1. Install matplotlib Library for Visualization
    2. The plot Method
    3. Modifying Plot Aesthetics with Templates
    4. Bar Charts
    5. Pie Charts
14. Chapter 14 : Options and Settings
    1. Introduction to the Options and Settings Module
    2. Changing Options with Attributes
    3. Changing Options with Functions
    4. The precision Option
15. Chapter 15 : Conclusion
    1. Conclusion