Data Analysis with Pandas and Python
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1 Year
Access -
Intermediate
Skill Level -
Course Certificate
Certificate -
173
Units -
0
Quizzes -
19 hours, 43 minutes
Duration
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Introduction to the Course 00:12:00 -
Mac OS – Download the Anaconda Distribution 00:03:00
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Mac OS – Install Anaconda Distribution 00:07:00
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Mac OS – Access the Terminal 00:02:00
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Mac OS – Update Anaconda Libraries 00:11:00
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Mac OS – Unpack Course Materials + The Startdown and Shutdown Process 00:10:00
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Windows – Download the Anaconda Distribution 00:04:00
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Windows – Install Anaconda Distribution 00:05:00
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Windows – Access the Command Prompt and Update Anaconda Libraries 00:10:00
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Windows – Unpack Course Materials + The Startdown and Shutdown Process 00:09:00
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Intro to the Jupyter Notebook Interface 00:05:00
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Cell Types and Cell Modes 00:07:00
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Code Cell Execution 00:05:00
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Popular Keyboard Shortcuts 00:03:00
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Import Libraries into Jupyter Notebook 00:07:00
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Python Crash Course, Part 1 – Data Types and Variables 00:07:00
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Python Crash Course, Part 2 – Lists 00:05:00
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Python Crash Course, Part 3 – Dictionaries 00:04:00
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Python Crash Course, Part 4 – Operators 00:05:00
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Python Crash Course, Part 5 – Functions 00:06:00
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Create Jupyter Notebook for the Series Module 00:02:00
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Create A Series Object from a Python List 00:11:00
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Create A Series Object from a Python Dictionary 00:03:00
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Intro to Attributes 00:07:00
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Intro to Methods 00:05:00
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Parameters and Arguments 00:10:00
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Import Series with the read_csv() Method 00:10:00
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The head() and tail() Methods 00:04:00
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Python Built-In Functions 00:05:00
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More Series Attributes 00:06:00
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The sort_values() Method 00:06:00
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The inplace Parameter 00:05:00
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The sort_index() Method 00:05:00
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Python’s in Keyword 00:04:00
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Extract Series Values by Index Position 00:04:00
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Extract Series Values by Index Label 00:07:00
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The get() Method on a Series 00:05:00
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Math Methods on Series Objects 00:06:00
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The idxmax() and idxmin() Methods 00:03:00
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The value_counts() Method 00:04:00
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The apply() Method 00:07:00
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The map() Method 00:07:00
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Intro to DataFrames I Module 00:07:00
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Shared Methods and Attributes between Series and DataFrames 00:08:00
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Differences between Shared Methods 00:07:00
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Select One Column from a DataFrame 00:08:00
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Select Two or More Columns from a DataFrame 00:05:00
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Add New Column to DataFrame 00:08:00
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Broadcasting Operations 00:09:00
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A Review of the value_counts() Method 00:04:00
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Drop Rows with Null Values 00:07:00
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Fill in Null Values with the fillna() Method 00:04:00
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The astype() Method 00:11:00
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Sort a DataFrame with the sort_values() Method, Part I 00:06:00
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Sort a DataFrame with the sort_values() Method, Part II 00:04:00
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Sort DataFrame with the sort_index() Method 00:03:00
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Rank Values with the rank() Method 00:06:00
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This Module’s Dataset + Memory Optimization 00:11:00
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Filter a DataFrame Based on A Condition 00:13:00
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Filter with More than One Condition (AND – &) 00:05:00
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Filter with More than One Condition (OR – ) 00:09:00
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The isin() Method 00:06:00
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The isnull() and notnull() Methods 00:05:00
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The between() Method 00:07:00
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The duplicated() Method 00:09:00
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The drop_duplicates() Method 00:08:00
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The unique() and nunique() Methods 00:04:00
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Intro to the DataFrames III Module + Import Dataset 00:03:00
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The set_index() and reset_index() Methods 00:06:00
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Retrieve Rows by Index Label with loc[] 00:10:00
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Retrieve Rows by Index Position with iloc[] 00:06:00
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The Catch-All ix[] Method 00:09:00
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Second Arguments to loc[], iloc[], and ix[] Methods 00:06:00
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Set New Values for a Specific Cell or Row 00:04:00
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Set Multiple Values in DataFrame 00:09:00
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Rename Index Labels or Columns in a DataFrame 00:07:00
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Delete Rows or Columns from a DataFrame 00:07:00
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Create Random Sample with the sample() Method 00:05:00
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The nsmallest() and nlargest() Methods 00:06:00
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Filtering with the where() Method 00:05:00
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The query() Method 00:09:00
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A Review of the apply() Method on Single Columns 00:06:00
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The apply() Method with Row Values 00:07:00
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The copy() Method 00:07:00
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Intro to the Working with Text Data Module 00:06:00
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Common String Methods – lower, upper, title, and len 00:07:00
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The strreplace() Method 00:08:00
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Filtering with String Methods 00:07:00
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More String Methods – strip, lstrip, and rstrip 00:05:00
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String Methods on Index and Columns 00:06:00
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Split Strings by Characters with strsplit() Method 00:09:00
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More Practice with Splits 00:06:00
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The expand and n Parameters of the strsplit() Method 00:07:00
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Intro to the MultiIndex Module 00:04:00
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Create a MultiIndex with the set_index() Method 00:10:00
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The get_level_values() Method 00:08:00
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The set_names() Method 00:03:00
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The sort_index() Method 00:05:00
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Extract Rows from a MultiIndex DataFrame 00:09:00
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The transpose() Method and MultiIndex on Column Level 00:06:00
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The swaplevel() Method 00:03:00
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The stack() Method 00:06:00
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The unstack() Method, Part 1 00:04:00
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The unstack() Method, Part 2 00:06:00
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The unstack() Method, Part 3 00:05:00
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The pivot() Method 00:07:00
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The pivot_table() Method 00:10:00
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The pdmelt() Method 00:06:00
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Intro to the Groupby Module 00:08:00
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First Operations with groupby Object 00:10:00
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Retrieve A Group with the get_group() Method 00:04:00
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Methods on the Groupby Object and DataFrame Columns 00:09:00
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Grouping by Multiple Columns 00:05:00
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The agg() Method 00:06:00
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Iterating through Groups 00:09:00
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Intro to the Merging, Joining, and Concatenating Module 00:06:00
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The pdconcat() Method, Part 1 00:06:00
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The pdconcat() Method, Part 2 00:07:00
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The append() Method on a DataFrame 00:02:00
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Inner Joins, Part 1 00:09:00
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Inner Joins, Part 2 00:09:00
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Outer Joins 00:12:00
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Left Joins 00:09:00
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The left_on and right_on Parameters 00:09:00
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Merging by Indexes with the left_index and right_index Parameters 00:11:00
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The join() Method 00:03:00
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The pdmerge() Method 00:03:00
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Intro to the Working with Dates and Times Module 00:04:00
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Review of Python’s datetime Module 00:10:00
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The pandas Timestamp Object 00:07:00
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The pandas DateTimeIndex Object 00:05:00
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The pdto_datetime() Method 00:11:00
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Create Range of Dates with the pddate_range() Method, Part 1 00:10:00
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Create Range of Dates with the pddate_range() Method, Part 2 00:09:00
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Create Range of Dates with the pddate_range() Method, Part 3 00:08:00
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The dt Accessor 00:07:00
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Install pandas-datareader Library 00:03:00
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Import Financial Data Set with pandas_datareader Library 00:11:00
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Selecting Rows from a DataFrame with a DateTimeIndex 00:08:00
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All About Hadoop: The Big Data Lingo Chapter 01:00:00
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The truncate() Method 00:03:00
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pdDateOffset Objects 00:12:00
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More Fun with pdDateOffset Objects 00:14:00
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The pandas Timedelta Object 00:09:00
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Timedeltas in a Dataset 00:10:00
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Intro to the Module + Fetch Panel Dataset from Google Finance 00:07:00
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The Axes of a Panel Object 00:08:00
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Panel Attributes 00:05:00
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Use Bracket Notation to Extract a DataFrame from a Panel 00:04:00
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Extracting with the loc, iloc, and ix Methods 00:07:00
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Convert Panel to a MultiIndex DataFrame (and Vice Versa) 00:04:00
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The major_xs() Method 00:06:00
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The minor_xs() Method 00:06:00
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Transpose a Panel with the transpose() Method 00:08:00
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The swapaxes() Method 00:04:00
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Intro to the Input and Output Module 00:02:00
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Feed pdread_csv() Method a URL Argument 00:04:00
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Quick Object Conversions 00:05:00
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Export DataFrame to CSV File with the to_csv() Method 00:06:00
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Install xlrd and openpyxl Libraries to Read and Write Excel Files 00:03:00
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Import Excel File into pandas 00:10:00
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Export Excel File 00:09:00
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Intro to Visualization Module 00:04:00
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The plot() Method 00:09:00
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Modifying Aesthetics with Templates 00:05:00
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Bar Graphs 00:06:00
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Pie Charts 00:05:00
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Histograms 00:06:00
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Introduction to the Options and Settings Module 00:02:00
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Changing pandas Options with Attributes and Dot Syntax 00:07:00
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Changing pandas Options with Methods 00:06:00
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The precision Option 00:03:00
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Conclusion 00:02:00
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