Data Analysis with Pandas and Python

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
Series
	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
MultiIndex
	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
GroupBy
	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
Panels
	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
Visualization
	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
	Conclusion	00:02:00