Data Analysis with Pandas and Python

Table of contents

1. Chapter 1 : Installation and Setup
   1. Introduction to Data Analysis with Pandas and Python
   2. MacOS - Download the Anaconda Distribution, Our Python Development Environment
   3. MacOS - Install Anaconda Distribution
   4. MacOS - Access the Terminal Application
   5. MacOS - Create Conda Environment and Install Pandas and Jupyter Notebook
   6. MacOS - Unpack Course Materials + The Start and Shutdown Process
   7. Windows - Download the Anaconda Distribution
   8. Windows - Install Anaconda Distribution
   9. Windows - Create Conda Environment and Install Pandas and Jupyter Notebook
   10. Windows - Unpack Course Materials + The Start and Shutdown Process
   11. Introduction to the Jupyter Notebook Interface
   12. Cell Types and Cell Modes in Jupyter Notebook
   13. Code Cell Execution in Jupyter Notebook
   14. Popular Keyboard Shortcuts in Jupyter Notebook
   15. Import Libraries into Jupyter Notebook
2. Chapter 2 : Bonus: Python Crash Course
   1. Introduction to the Python Crash Course
   2. Comments
   3. Basic Data Types
   4. Operators
   5. Variables
   6. Built-In Functions
   7. Custom Functions
   8. String Methods
   9. Lists
   10. Index Positions and Slicing
   11. Dictionaries
3. Chapter 3 : Series
   1. Create Jupyter Notebook for the Series Module
   2. Create a Series Object from a Python List
   3. Create a Series Object from a Python Dictionary
   4. Introduction to Attributes on a Series Object
   5. Introduction to Methods on a Series Object
   6. Parameters and Arguments
   7. Create Series from a Dataset with the pd.read_csv Method
   8. Use the Head and Tail Methods to Return Rows from the Beginning and End of a Dataset
   9. Passing Pandas Objects to Python Built-In Functions
   10. Accessing More Series Attributes
   11. Use the sort_values Method to Sort a Series in Ascending or Descending Order
   12. Use the inplace Parameter to Permanently Mutate a Pandas Data Structure
   13. Use the sort_index Method to Sort the Index of a Pandas Series Object
   14. Use Python's in Keyword to Check for Inclusion in Series Values or Index
   15. Extract Series Values by Index Position
   16. Extract Series Values by Index Label
   17. Use the get Method to Retrieve a Value for an Index label in a Series
   18. Math Methods on Series Objects
   19. Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value
   20. Use the value_counts Method to See Counts of Unique Values within a Series
   21. Use the apply Method to Invoke a Function on Every Series Values
   22. The Series#map Method
4. Chapter 4 : DataFrames I: Introduction
   1. Introduction to DataFrames I Module
   2. Shared Methods and Attributes Between Series and DataFrames
   3. Differences Between Shared Methods
   4. Select One Column from a DataFrame
   5. Select Two or More Columns from a DataFrame
   6. Add a New Column to DataFrame
   7. Broadcasting Operations on DataFrames
   8. A Review of the value_counts Method
   9. Drop DataFrame Rows with Null Values with the dropna Method
   10. Fill in Null DataFrame Values with the fillna Method
   11. Convert DataFrame Column Types with the astype Method
   12. Sort a DataFrame with the sort_values Method, Part I
   13. Sort a DataFrame with the sort_values Method, Part II
   14. Sort DataFrame Index with the sort_index() Method
   15. 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 DataFrame with More than One Condition (AND - )
   4. Filter DataFrame with More than One Condition (OR - |)
   5. Check for Inclusion with the isin() Method
   6. Check for Null and Present DataFrame Values with the isnull and notnull Methods
   7. Check for Inclusion Within a Range of Values with the between Method
   8. Check for Duplicate DataFrame Rows with the duplicated Method
   9. Delete Duplicate DataFrame Rows with the drop_duplicates Method
   10. Identify and Count Unique Values with the unique and nunique Methods
6. Chapter 6 : DataFrames III: Data Extraction
   1. Introduction to the DataFrames III Module + Import Dataset
   2. Use the set_index and reset_index Methods to Define a new DataFrame Index
   3. Retrieve Rows by Index Label with loc Accessor
   4. Retrieve Rows by Index Position with iloc Accessor
   5. Passing Second Arguments to the loc and iloc Accessors
   6. Set New Value for a Specific Cell or Cells in a Row
   7. Set 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. Use the nsmallest / nlargest Methods to Get Rows with Smallest / Largest Values.
   12. Filter a DataFrame with the where() Method
   13. Filter a DataFrame with the query Method
   14. A Review of the apply Method on a Pandas Series Object
   15. Apply a Function to Every DataFrame Row with the apply Method
   16. Create a Copy of a DataFrame with the copy Method
7. Chapter 7 : Working with Text Data
   1. Introduction to the Working with Text Data Section
   2. Common String Methods - lower, upper, title, and len
   3. Use the str.replace Method to Replace All Occurrences of a Character with Another
   4. Filter a DataFrame's Rows with String Methods
   5. More DataFrame String Methods - strip, lstrip, and rstrip
   6. Invoke String Methods on DataFrame Index and Columns
   7. Split Strings by Characters with the str.split Method
   8. More Practice with the str.split Method on a Series
   9. Exploring the expand and n Parameters of the str.split Method
8. Chapter 8 : MultiIndex
   1. Introduction to the MultiIndex Module
   2. Create a MultiIndex on a DataFrame with the set_index Method
   3. Extract Index Level Values with the get_level_values Method
   4. Change Index Level Name with the set_names Method
   5. The sort_index Method on a MultiIndex DataFrame
   6. Extract Rows from a MultiIndex DataFrame
   7. The transpose Method on a MultiIndex DataFrame
   8. The .swaplevel() Method
   9. The .stack() Method
   10. The .unstack() Method, Part 1
   11. The .unstack() Method, Part 2
   12. The .unstack() Method, Part 3
   13. The pivot Method
   14. Use the pivot_table Method to Create an Aggregate Summary of a DataFrame
   15. Use the pd.melt Method to Create a Narrow Dataset from a Wide One
9. Chapter 9 : The GroupBy Object
   1. Introduction to the GroupBy Module
   2. First Operations with the GroupBy Object
   3. Retrieve a Group from a GroupBy Object with the get_group Method
   4. Methods on the GroupBy Object and DataFrame Columns
   5. Grouping by Multiple Columns
   6. The .agg() Method
   7. Iterating Through Groups
10. Chapter 10 : Merging, Joining, and Concatenating DataFrames
    1. Introduction to the Merging, Joining, and Concatenating Section
    2. The pd.concat Method, Part 1
    3. The pd.concat Method, Part 2
    4. The append Method on a DataFrame
    5. Inner Joins, Part 1
    6. Inner Joins, Part 2
    7. Outer Joins
    8. Left Joins
    9. The left_on and right_on Parameters
    10. Merging by Indexes with the left_index and right_index Parameters
    11. The .join() Method
    12. The pd.merge() Method
11. Chapter 11 : Working with Dates and Times in Datasets
    1. Introduction to the Working with Dates and Times Module
    2. Review of Python's Datetime Module
    3. The Pandas Timestamp Object
    4. The Pandas DateTimeIndex Object
    5. The pd.to_datetime() Method
    6. Create Range of Dates with the pd.date_range() Method, Part 1
    7. Create Range of Dates with the pd.date_range() Method, Part 2
    8. Create Range of Dates with the pd.date_range() Method, Part 3
    9. The .dt Accessor
    10. Install Pandas-datareader Library
    11. Import Financial Dataset with pandas_datareader Library
    12. Selecting Rows from a DataFrame with a DateTimeIndex
    13. Timestamp Object Attributes and Methods
    14. The pd.DateOffset Object
    15. Timeseries Offsets
    16. The Timedelta Object
    17. Timedeltas in a Dataset
12. Chapter 12 : Input and Output in Pandas
    1. Introduction to the Input and Output Section
    2. Pass a URL to the pd.read_csv Method
    3. Quick Object Conversions
    4. Export CSV File with the to_csv Method
    5. Install xlrd and openpyxl Libraries to Read and Write Excel Files
    6. Import Excel File into Pandas with the read_excel Method
    7. Export Excel File with the to_excel Method
13. Chapter 13 : Visualization
    1. Introduction to the Visualization Section
    2. Use the plot Method to Render a Line Chart
    3. Modifying Plot Aesthetics with matplotlib Templates
    4. Creating Bar Graphs to Show Counts
    5. Creating Pie Charts to Represent Proportions
14. Chapter 14 : Options and Settings in Pandas
    1. Introduction to the Options and Settings Module
    2. Changing Pandas Options with Attributes and Dot Syntax
    3. Changing Pandas Options with Methods
    4. The precision Option
15. Chapter 15 : Thank You
    1. Conclusion