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Data Analysis with Pandas and Python

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Course Curriculum

1. Course introduction and welcome
Introduction to the Course 00:12:00
Mac OS – Download the Anaconda Distribution 00:03:00
Mac OS – Install Anaconda Distribution 00:07:00
Mac OS – Access the Terminal 00:02:00
Mac OS – Update Anaconda Libraries 00:11:00
Mac OS – Unpack Course Materials + The Startdown and Shutdown Process 00:10:00
Windows – Download the Anaconda Distribution 00:04:00
Windows – Install Anaconda Distribution 00:05:00
Windows – Access the Command Prompt and Update Anaconda Libraries 00:10:00
Windows – Unpack Course Materials + The Startdown and Shutdown Process 00:09:00
Intro to the Jupyter Notebook Interface 00:05:00
Cell Types and Cell Modes 00:07:00
Code Cell Execution 00:05:00
Popular Keyboard Shortcuts 00:03:00
Import Libraries into Jupyter Notebook 00:07:00
Python Crash Course, Part 1 – Data Types and Variables 00:07:00
Python Crash Course, Part 2 – Lists 00:05:00
Python Crash Course, Part 3 – Dictionaries 00:04:00
Python Crash Course, Part 4 – Operators 00:05:00
Python Crash Course, Part 5 – Functions 00:06:00
Create Jupyter Notebook for the Series Module 00:02:00
Create A Series Object from a Python List 00:11:00
Create A Series Object from a Python Dictionary 00:03:00
Intro to Attributes 00:07:00
Intro to Methods 00:05:00
Parameters and Arguments 00:10:00
Import Series with the read_csv() Method 00:10:00
The head() and tail() Methods 00:04:00
Python Built-In Functions 00:05:00
More Series Attributes 00:06:00
The sort_values() Method 00:06:00
The inplace Parameter 00:05:00
The sort_index() Method 00:05:00
Python’s in Keyword 00:04:00
Extract Series Values by Index Position 00:04:00
Extract Series Values by Index Label 00:07:00
The get() Method on a Series 00:05:00
Math Methods on Series Objects 00:06:00
The idxmax() and idxmin() Methods 00:03:00
The value_counts() Method 00:04:00
The apply() Method 00:07:00
The map() Method 00:07:00
DataFrames I
Intro to DataFrames I Module 00:07:00
Shared Methods and Attributes between Series and DataFrames 00:08:00
Differences between Shared Methods 00:07:00
Select One Column from a DataFrame 00:08:00
Select Two or More Columns from a DataFrame 00:05:00
Add New Column to DataFrame 00:08:00
Broadcasting Operations 00:09:00
A Review of the value_counts() Method 00:04:00
Drop Rows with Null Values 00:07:00
Fill in Null Values with the fillna() Method 00:04:00
The astype() Method 00:11:00
Sort a DataFrame with the sort_values() Method, Part I 00:06:00
Sort a DataFrame with the sort_values() Method, Part II 00:04:00
Sort DataFrame with the sort_index() Method 00:03:00
Rank Values with the rank() Method 00:06:00
DataFrames II
This Module’s Dataset + Memory Optimization 00:11:00
Filter a DataFrame Based on A Condition 00:13:00
Filter with More than One Condition (AND – &) 00:05:00
Filter with More than One Condition (OR – ) 00:09:00
The isin() Method 00:06:00
The isnull() and notnull() Methods 00:05:00
The between() Method 00:07:00
The duplicated() Method 00:09:00
The drop_duplicates() Method 00:08:00
The unique() and nunique() Methods 00:04:00
DataFrames III
Intro to the DataFrames III Module + Import Dataset 00:03:00
The set_index() and reset_index() Methods 00:06:00
Retrieve Rows by Index Label with loc[] 00:10:00
Retrieve Rows by Index Position with iloc[] 00:06:00
The Catch-All ix[] Method 00:09:00
Second Arguments to loc[], iloc[], and ix[] Methods 00:06:00
Set New Values for a Specific Cell or Row 00:04:00
Set Multiple Values in DataFrame 00:09:00
Rename Index Labels or Columns in a DataFrame 00:07:00
Delete Rows or Columns from a DataFrame 00:07:00
Create Random Sample with the sample() Method 00:05:00
The nsmallest() and nlargest() Methods 00:06:00
Filtering with the where() Method 00:05:00
The query() Method 00:09:00
A Review of the apply() Method on Single Columns 00:06:00
The apply() Method with Row Values 00:07:00
The copy() Method 00:07:00
Working with Text Data
Intro to the Working with Text Data Module 00:06:00
Common String Methods – lower, upper, title, and len 00:07:00
The strreplace() Method 00:08:00
Filtering with String Methods 00:07:00
More String Methods – strip, lstrip, and rstrip 00:05:00
String Methods on Index and Columns 00:06:00
Split Strings by Characters with strsplit() Method 00:09:00
More Practice with Splits 00:06:00
The expand and n Parameters of the strsplit() Method 00:07:00
Intro to the MultiIndex Module 00:04:00
Create a MultiIndex with the set_index() Method 00:10:00
The get_level_values() Method 00:08:00
The set_names() Method 00:03:00
The sort_index() Method 00:05:00
Extract Rows from a MultiIndex DataFrame 00:09:00
The transpose() Method and MultiIndex on Column Level 00:06:00
The swaplevel() Method 00:03:00
The stack() Method 00:06:00
The unstack() Method, Part 1 00:04:00
The unstack() Method, Part 2 00:06:00
The unstack() Method, Part 3 00:05:00
The pivot() Method 00:07:00
The pivot_table() Method 00:10:00
The pdmelt() Method 00:06:00
Intro to the Groupby Module 00:08:00
First Operations with groupby Object 00:10:00
Retrieve A Group with the get_group() Method 00:04:00
Methods on the Groupby Object and DataFrame Columns 00:09:00
Grouping by Multiple Columns 00:05:00
The agg() Method 00:06:00
Iterating through Groups 00:09:00
Merging, Joining, and Concatenating
Intro to the Merging, Joining, and Concatenating Module 00:06:00
The pdconcat() Method, Part 1 00:06:00
The pdconcat() Method, Part 2 00:07:00
The append() Method on a DataFrame 00:02:00
Inner Joins, Part 1 00:09:00
Inner Joins, Part 2 00:09:00
Outer Joins 00:12:00
Left Joins 00:09:00
The left_on and right_on Parameters 00:09:00
Merging by Indexes with the left_index and right_index Parameters 00:11:00
The join() Method 00:03:00
The pdmerge() Method 00:03:00
Working with Dates and Times
Intro to the Working with Dates and Times Module 00:04:00
Review of Python’s datetime Module 00:10:00
The pandas Timestamp Object 00:07:00
The pandas DateTimeIndex Object 00:05:00
The pdto_datetime() Method 00:11:00
Create Range of Dates with the pddate_range() Method, Part 1 00:10:00
Create Range of Dates with the pddate_range() Method, Part 2 00:09:00
Create Range of Dates with the pddate_range() Method, Part 3 00:08:00
The dt Accessor 00:07:00
Install pandas-datareader Library 00:03:00
Import Financial Data Set with pandas_datareader Library 00:11:00
Selecting Rows from a DataFrame with a DateTimeIndex 00:08:00
All About Hadoop: The Big Data Lingo Chapter 01:00:00
The truncate() Method 00:03:00
pdDateOffset Objects 00:12:00
More Fun with pdDateOffset Objects 00:14:00
The pandas Timedelta Object 00:09:00
Timedeltas in a Dataset 00:10:00
Intro to the Module + Fetch Panel Dataset from Google Finance 00:07:00
The Axes of a Panel Object 00:08:00
Panel Attributes 00:05:00
Use Bracket Notation to Extract a DataFrame from a Panel 00:04:00
Extracting with the loc, iloc, and ix Methods 00:07:00
Convert Panel to a MultiIndex DataFrame (and Vice Versa) 00:04:00
The major_xs() Method 00:06:00
The minor_xs() Method 00:06:00
Transpose a Panel with the transpose() Method 00:08:00
The swapaxes() Method 00:04:00
Input and Output
Intro to the Input and Output Module 00:02:00
Feed pdread_csv() Method a URL Argument 00:04:00
Quick Object Conversions 00:05:00
Export DataFrame to CSV File with the to_csv() Method 00:06:00
Install xlrd and openpyxl Libraries to Read and Write Excel Files 00:03:00
Import Excel File into pandas 00:10:00
Export Excel File 00:09:00
Intro to Visualization Module 00:04:00
The plot() Method 00:09:00
Modifying Aesthetics with Templates 00:05:00
Bar Graphs 00:06:00
Pie Charts 00:05:00
Histograms 00:06:00
Options and Settings
Introduction to the Options and Settings Module 00:02:00
Changing pandas Options with Attributes and Dot Syntax 00:07:00
Changing pandas Options with Methods 00:06:00
The precision Option 00:03:00
Conclusion 00:02:00

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