Section 1 : Section 1 Installation and Setup
Lecture 1 About Proctor Testing
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 Completed Course Files
Lecture 4 MacOS - Download the Anaconda Distribution, our Python 00:04:20 Duration
Lecture 5 MacOS - Install Anaconda Distribution 00:10:36 Duration
Lecture 6 MacOS - Access the Terminal Application 00:08:30 Duration
Lecture 7 MacOS - Create conda Environment and Install pandas 00:13:04 Duration
Lecture 8 MacOS - Unpack Course Materials + The Start and Shutdown 00:12:27 Duration
Lecture 9 Windows - Download the Anaconda Distribution
Lecture 10 Windows - Install Anaconda Distribution 00:07:49 Duration
Lecture 11 Windows - Create conda Environment and Install pandas 00:18:04 Duration
Lecture 12 Windows - Unpack Course Materials + The Startdown 00:13:19 Duration
Lecture 13 Intro to the Jupyter Notebook Interface 00:09:50 Duration
Lecture 14 Cell Types and Cell Modes in Jupyter Notebook 00:07:34 Duration
Lecture 15 Code Cell Execution in Jupyter Notebook 00:03:13 Duration
Lecture 16 Popular Keyboard Shortcuts in Jupyter Notebook 00:03:38 Duration
Lecture 17 Import Libraries into Jupyter Notebook 00:08:26 Duration
Lecture 18 Troubleshooting Issues with Jupyter Notebook

Section 2 : Section 2 BONUS Python Crash Course
Lecture 1 Intro to the Python Crash Course 00:03:33 Duration
Lecture 2 Comments 00:03:18 Duration
Lecture 3 Basic Data Types 00:11:09 Duration
Lecture 4 Operators 00:15:38 Duration
Lecture 5 Variables 00:07:44 Duration
Lecture 6 Built-in Functions 00:10:27 Duration
Lecture 7 Custom Functions 00:16:30 Duration
Lecture 8 String Methods 00:20:45 Duration
Lecture 9 Lists 00:13:37 Duration
Lecture 10 Index Positions and Slicing 00:15:54 Duration
Lecture 11 Dictionaries 00:15:24 Duration

Section 3 : Section 3 Series
Lecture 1 Create Jupyter Notebook for the Series Module 00:02:15 Duration
Lecture 2 Create A Series Object from a Python List 00:10:58 Duration
Lecture 3 Create A Series Object from a Python Dictionary 00:03:06 Duration
Lecture 4 Coding Exercise SOLUTION Create a Series Object
Lecture 5 Intro to Attributes on a Series Object 00:07:17 Duration
Lecture 6 Intro to Methods on a Series Object 00:04:42 Duration
Lecture 7 Parameters and Arguments 00:10:10 Duration
Lecture 8 Create Series from Dataset with the pd 00:15:00 Duration
Lecture 9 Coding Exercise SOLUTION Import Series with the read_csv
Lecture 10 Use the head and tail Methods to Return Rows 00:03:41 Duration
Lecture 11 Passing pandas Objects to Python Built-In Functions 00:05:16 Duration
Lecture 12 Accessing More Series Attributes 00:06:08 Duration
Lecture 13 Use the sort_values method to sort a Series in ascending
Lecture 14 Use the inplace Parameter to permanently mutate a pandas 00:05:06 Duration
Lecture 15 Use the sort_index Method to Sort the Index of a pandas 00:04:41 Duration
Lecture 16 Coding Exercise SOLUTION The sort_values and sort_index
Lecture 17 Use Python's in Keyword to Check for Inclusion in Series
Lecture 18 Extract Series Values by Index Positiox 00:04:10 Duration
Lecture 19 Extract Series Values by Index Label 00:10:33 Duration
Lecture 20 Coding Exercise SOLUTION Extract Series Values by Index
Lecture 21 Use the get Method to Retrieve a Value for an index label 00:09:39 Duration
Lecture 22 Math Methods on Series Objects 00:05:40 Duration
Lecture 23 Use the idxmax and idxmin Methods to Find Index 00:03:12 Duration
Lecture 24 Use the value_counts Method to See Counts of Unique 00:03:37 Duration
Lecture 25 Use the apply Method to Invoke a Function on Every Series 00:06:44 Duration
Lecture 26 The Series#map Method 00:06:48 Duration

Section 4 : Section 4 DataFrames I Introduction
Lecture 1 Intro to DataFrames I Module 00:09:41 Duration
Lecture 2 Shared Methods and Attributes between Series 00:13:53 Duration
Lecture 3 Differences between Shared Methods 00:06:39 Duration
Lecture 4 Select One Column from a DataFrame 00:07:51 Duration
Lecture 5 Coding Exercise SOLUTION Select One Column from
Lecture 6 Select Two or More Columns from a DataFrame 00:05:10 Duration
Lecture 7 Coding Exercise SOLUTION Select Two or More Columns
Lecture 8 Add New Column to DataFrame 00:07:56 Duration
Lecture 9 Broadcasting Operations on DataFrames 00:09:05 Duration
Lecture 10 A Review of the value_counts Method 00:03:49 Duration
Lecture 11 Drop DataFrame Rows with Null Values with the dropna 00:06:36 Duration
Lecture 12 Coding Exercise SOLUTION Delete DataFrame Rows
Lecture 13 Fill in Null DataFrame Values with the fillna Method 00:04:23 Duration
Lecture 14 Convert DataFrame Column Types with the astype Method 00:10:34 Duration
Lecture 15 Sort a DataFrame with the sort_values Method, Part I 00:05:43 Duration
Lecture 16 Sort a DataFrame with the sort_values Method, Part II 00:04:09 Duration
Lecture 17 Coding Exercise SOLUTION The sort_values Method
Lecture 18 Sort DataFrame Indexwith the sort_index Method 00:02:54 Duration
Lecture 19 Rank Series Values with the rank Method 00:05:48 Duration

Section 5 : Section 5 DataFrames II Filtering Data
Lecture 1 This Module's Dataset + Memory Optimization 00:15:45 Duration
Lecture 2 Filter a DataFrame Based on A Condition 00:12:48 Duration
Lecture 3 Filter DataFrame with More than One Condition (AND - &) 00:04:39 Duration
Lecture 4 Filter DataFrame with More than One Condition (OR - ) 00:08:47 Duration
Lecture 5 Check for Inclusion with the isin Method 00:06:15 Duration
Lecture 6 Check for Null and Present DataFrame Values with the isnull 00:05:15 Duration
Lecture 7 Check For Inclusion Within a Range of Values 00:06:50 Duration
Lecture 8 Check for Duplicate DataFrame Rows with the duplicated 00:09:00 Duration
Lecture 9 Delete Duplicate DataFrame Rows with the drop_duplicates 00:08:13 Duration
Lecture 10 Identify and Count Unique Values with the unique 00:04:40 Duration

Section 6 : Section 6 DataFrames III Data Extraction
Lecture 1 Intro to the DataFrames III Module + Import Dataset 00:05:57 Duration
Lecture 2 Use the set_index and reset_index methods to define a new 00:09:25 Duration
Lecture 3 Retrieve Rows by Index Label with loc Accessor 00:17:08 Duration
Lecture 4 Retrieve Rows by Index Position with iloc Accessor 00:07:32 Duration
Lecture 5 Passing second arguments to the loc and iloc Accessors 00:09:10 Duration
Lecture 6 Set New Value for a Specific Cell or Cells In a Row 00:04:42 Duration
Lecture 7 Set Multiple Values in a DataFrame 00:06:13 Duration
Lecture 8 Rename Index Labels or Columns in a DataFrame 00:09:33 Duration
Lecture 9 Delete Rows or Columns from a DataFrame 00:07:40 Duration
Lecture 10 Create Random Sample with the sample Method 00:04:45 Duration
Lecture 11 Use the nsmallest nlargest methods to get rows 00:05:36 Duration
Lecture 12 Filter A DataFrame with the where method 00:05:01 Duration
Lecture 13 Filter A DataFrame with the query method 00:09:06 Duration
Lecture 14 A Review of the apply Method on a pandas Series Object 00:06:00 Duration
Lecture 15 Apply a Function to every DataFrame Row with the apply 00:07:11 Duration
Lecture 16 Create a Copy of a DataFrame with the copy Method 00:07:07 Duration

Section 7 : Section 7 Working with Text Data
Lecture 1 Intro to the Working with Text Data Section 00:06:23 Duration
Lecture 2 Common String Methods - lower, upper, title, and len 00:07:11 Duration
Lecture 3 Use the str 00:09:09 Duration
Lecture 4 Filter a DataFrame's Rows with String Methods 00:08:25 Duration
Lecture 5 More DataFrame String Methods - strip, lstrip, and rstrip 00:05:58 Duration
Lecture 6 Invoke String Methods on DataFrame Index and Columns 00:06:20 Duration
Lecture 7 Split Strings by Characters with the str 00:10:29 Duration
Lecture 8 More Practice with the str 00:08:33 Duration
Lecture 9 Exploring the expand and n Parameters of the str 00:08:44 Duration

Section 8 : Section 8 MultiIndex
Lecture 1 Intro to the MultiIndex Module 00:04:50 Duration
Lecture 2 Create a MultiIndex on a DataFrame with the set_index 00:10:36 Duration
Lecture 3 Extract Index Level Values with the get_level_values Method 00:04:17 Duration
Lecture 4 Change Index Level Name with the set_names Method 00:04:15 Duration
Lecture 5 The sort_index Method on a MultiIndex DataFrame 00:08:20 Duration
Lecture 6 Extract Rows from a MultiIndex DataFrame 00:10:54 Duration
Lecture 7 The transpose Method on a MultiIndex DataFrame 00:08:16 Duration
Lecture 8 The 00:03:30 Duration
Lecture 9 The 00:05:59 Duration
Lecture 10 The 00:03:37 Duration
Lecture 11 The 00:06:14 Duration
Lecture 12 The 00:05:08 Duration
Lecture 13 The pivot Method 00:06:43 Duration
Lecture 14 Use the pivot_table method to create an aggregate 00:06:43 Duration
Lecture 15 Use the pd 00:05:56 Duration

Section 9 : Section 9 The GroupBy Object
Lecture 1 Intro to the Groupby Module 00:07:44 Duration
Lecture 2 First Operations with groupby Object 00:09:30 Duration
Lecture 3 Retrieve a group from a GroupBy object with the get_group 00:03:46 Duration
Lecture 4 Methods on the Groupby Object and DataFrame Columns 00:08:43 Duration
Lecture 5 Grouping by Multiple Columns 00:04:38 Duration
Lecture 6 The 00:06:08 Duration
Lecture 7 Iterating through Groups 00:09:01 Duration

Section 10 : Section 10 Merging, Joining, and Concatenating
Lecture 1 Intro to the Merging, Joining, and Concatenating Section
Lecture 2 The pd 00:05:20 Duration
Lecture 3 The pd 00:07:09 Duration
Lecture 4 Inner Joins, Part 1 00:09:26 Duration
Lecture 5 Inner Joins, Part 2 00:09:05 Duration
Lecture 6 Outer Joins 00:12:43 Duration
Lecture 7 Left Joins 00:09:19 Duration
Lecture 8 The left_on and right_on Parameters 00:09:11 Duration
Lecture 9 Merging by Indexes with the left_index and right_index 00:11:04 Duration
Lecture 10 The 00:03:16 Duration
Lecture 11 The pd 00:03:07 Duration

Section 11 : Section 11 Working with Dates and Times in Datasets
Lecture 1 Intro to the Working with Dates and Times Module 00:04:21 Duration
Lecture 2 Review of Python's datetime Module 00:10:51 Duration
Lecture 3 The pandas Timestamp Object 00:07:50 Duration
Lecture 4 The pandas DateTimeIndex Object 00:05:40 Duration
Lecture 5 The pd 00:12:07 Duration
Lecture 6 Create Range of Dates with the pd 00:10:23 Duration
Lecture 7 Create Range of Dates with the pd 00:10:33 Duration
Lecture 8 Create Range of Dates with the pd 00:07:47 Duration
Lecture 9 The 00:07:29 Duration
Lecture 10 Install pandas-datareader Library 00:03:33 Duration
Lecture 11 Import Financial Data Set with pandas_datareader Library 00:07:56 Duration
Lecture 12 Selecting Rows from a DataFrame with a DateTimeIndex 00:12:28 Duration
Lecture 13 Timestamp Object Attributes and Methods 00:09:38 Duration
Lecture 14 The pd 00:06:50 Duration
Lecture 15 Timeseries Offsets 00:12:46 Duration
Lecture 16 The Timedelta Object 00:08:22 Duration
Lecture 17 Timedeltas in a Dataset 00:09:32 Duration

Section 12 : Section 12 Input and Output in pandas
Lecture 1 Intro to the Input and Output Section 00:01:27 Duration
Lecture 2 Pass a URL to the pd 00:04:25 Duration
Lecture 3 Quick Object Conversions 00:07:04 Duration
Lecture 4 Export CSV File with the to_csv Method 00:05:26 Duration
Lecture 5 Install xlrd and openpyxl Libraries to Read and Write Excel 00:04:11 Duration
Lecture 6 Import Excel File into pandas with the read_excel Method 00:09:44 Duration
Lecture 7 Export Excel File with the to_excel Method 00:07:44 Duration

Section 13 : Section 13 Visualization
Lecture 1 Intro to Visualization Section 00:04:48 Duration
Lecture 2 Use the plot Method to Render a Line Chart 00:07:53 Duration
Lecture 3 Modifying Plot Aesthetics with matplotlib Templates 00:04:45 Duration
Lecture 4 Creating Bar Graphs to Show Counts 00:05:57 Duration
Lecture 5 Creating Pie Charts to Represent Proportions

Section 14 : Section 14 Options and Settings in pandas
Lecture 1 Introduction to the Options and Settings Module 00:01:45 Duration
Lecture 2 Changing pandas Options with Attributes and Dot Syntax 00:07:00 Duration
Lecture 3 Changing pandas Options with Methods 00:06:16 Duration

Section 15 : Section 15 Conclusion
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 Conclusion 00:01:41 Duration