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Data Analysis with Pandas and Python

Video Description

Analyze data quickly and easily with Python's powerful panda library! All datasets included --- beginners welcome!

About This Video

  • To take your data analysis skills to the next level
  • Introduces you to the popular Pandas library built on top of the Python programming language.
  • Over the course of more than 19 hours, the author takes you on step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library.

In Detail

Welcome to the most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world! Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include: installing, sorting, filtering, grouping, aggregating, de-duplicating, pivoting, munging, deleting, merging, visualizing, and more! Why learn pandas? If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language. Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! I call it "Excel on steroids"!

Table of Contents

  1. Chapter 1 : Installation and Setup
    1. Introduction to the Course 00:12:15
    2. Mac OS - Download the Anaconda Distribution 00:03:28
    3. Mac OS - Install Anaconda Distribution 00:07:04
    4. Mac OS - Access the Terminal 00:01:55
    5. Mac OS - Update Anaconda Libraries 00:11:18
    6. Mac OS - Unpack Course Materials + The Startdown and Shutdown Process 00:10:01
    7. Windows - Download the Anaconda Distribution 00:03:47
    8. Windows - Install Anaconda Distribution 00:05:16
    9. Windows - Access the Command Prompt and Update Anaconda Libraries 00:10:11
    10. Windows - Unpack Course Materials + The Startdown and Shutdown Process 00:08:49
    11. Intro to the Jupyter Notebook Interface 00:05:14
    12. Cell Types and Cell Modes 00:07:03
    13. Code Cell Execution 00:04:47
    14. Popular Keyboard Shortcuts 00:03:06
    15. Import Libraries into Jupyter Notebook 00:07:09
    16. Python Crash Course, Part 1 - Data Types and Variables 00:07:05
    17. Python Crash Course, Part 2 – Lists 00:05:06
    18. Python Crash Course, Part 3 – Dictionaries 00:04:19
    19. Python Crash Course, Part 4 – Operators 00:04:30
    20. Python Crash Course, Part 5 – Functions 00:06:02
  2. Chapter 2 : Series
    1. Create Jupyter Notebook for the Series Module 00:02:12
    2. Create A Series Object from a Python List 00:10:32
    3. Create A Series Object from a Python Dictionary 00:03:06
    4. Intro to Attributes 00:07:17
    5. Intro to Methods 00:04:42
    6. Parameters and Arguments 00:10:10
    7. Import Series with the .read_csv() Method 00:10:23
    8. The .head() and .tail() Methods 00:03:42
    9. Python Built-In Functions 00:05:20
    10. More Series Attributes 00:06:13
    11. The .sort_values() Method 00:06:04
    12. The inplace Parameter 00:05:07
    13. The .sort_index() Method 00:04:38
    14. Python's in Keyword 00:04:00
    15. Extract Series Values by Index Position 00:04:15
    16. Extract Series Values by Index Label 00:07:22
    17. The .get() Method on a Series 00:05:03
    18. Math Methods on Series Objects 00:05:39
    19. The .idxmax() and .idxmin() Methods 00:03:10
    20. The .value_counts() Method 00:03:39
    21. The .apply() Method 00:06:46
    22. The .map() Method 00:06:52
  3. Chapter 3 : DataFrames I
    1. Intro to DataFrames I Module 00:07:24
    2. Shared Methods and Attributes between Series and DataFrames 00:07:37
    3. Differences between Shared Methods 00:06:48
    4. Select One Column from a DataFrame 00:07:57
    5. Select Two or More Columns from a DataFrame 00:05:12
    6. Add New Column to DataFrame 00:08:03
    7. Broadcasting Operations 00:09:07
    8. A Review of the .value_counts() Method 00:03:54
    9. Drop Rows with Null Values 00:06:41
    10. Fill in Null Values with the .fillna() Method 00:04:25
    11. The .astype() Method 00:10:38
    12. Sort a DataFrame with the .sort_values() Method, Part I 00:05:46
    13. Sort a DataFrame with the .sort_values() Method, Part II 00:04:13
    14. Sort DataFrame with the .sort_index() Method 00:02:59
    15. Rank Values with the .rank() Method 00:05:53
  4. Chapter 4 : DataFrames II
    1. This Module's Dataset + Memory Optimization 00:10:45
    2. Filter a DataFrame Based on A Condition 00:12:57
    3. Filter with More than One Condition (AND - &) 00:04:41
    4. Filter with More than One Condition (OR - |) 00:08:35
    5. The .isin() Method 00:06:17
    6. The .isnull() and .notnull() Methods 00:05:07
    7. The .between() Method 00:06:51
    8. The .duplicated() Method 00:09:05
    9. The .drop_duplicates() Method 00:08:16
    10. The .unique() and .nunique() Methods 00:04:22
  5. Chapter 5 : DataFrames III
    1. Intro to the DataFrames III Module + Import Dataset 00:03:23
    2. The .set_index() and .reset_index() Methods 00:05:37
    3. Retrieve Rows by Index Label with .loc[] 00:09:43
    4. Retrieve Rows by Index Label with .loc[] 00:06:07
    5. The Catch-All .ix[] Method 00:08:44
    6. Second Arguments to .loc[], .iloc[], and .ix[] Methods 00:06:21
    7. Set New Values for a Specific Cell or Row 00:04:27
    8. Set Multiple Values in DataFrame 00:09:16
    9. Rename Index Labels or Columns in a DataFrame 00:06:49
    10. Delete Rows or Columns from a DataFrame 00:07:29
    11. Create Random Sample with the .sample() Method 00:04:43
    12. The .nsmallest() and .nlargest() Methods 00:05:36
    13. Filtering with the .where() Method 00:05:03
    14. The .query() Method 00:09:07
    15. A Review of the .apply() Method on Single Columns 00:05:53
    16. The .apply() Method with Row Values 00:06:49
    17. The .copy() Method 00:07:05
  6. Chapter 6 : Working with Text Data
    1. Intro to the Working with Text Data Module 00:05:55
    2. Common String Methods - lower, upper, title, and len 00:07:14
    3. The .str.replace() Method 00:08:07
    4. Filtering with String Methods 00:06:43
    5. More String Methods - strip, lstrip, and rstrip 00:04:31
    6. String Methods on Index and Columns 00:05:30
    7. Split Strings by Characters with .str.split() Method 00:08:41
    8. More Practice with Splits 00:06:01
    9. The expand and n Parameters of the .str.split() Method 00:07:00
  7. Chapter 7 : MultiIndex
    1. Intro to the MultiIndex Module 00:04:26
    2. Create a MultiIndex with the set_index() Method 00:09:50
    3. The .get_level_values() Method 00:07:52
    4. The .set_names() Method 00:03:08
    5. The sort_index() Method 00:04:56
    6. Extract Rows from a MultiIndex DataFrame 00:08:32
    7. The .transpose() Method and MultiIndex on Column Level 00:05:48
    8. The .swaplevel() Method 00:02:34
    9. The .stack() Method 00:06:01
    10. The .unstack() Method, Part 1 00:03:38
    11. The .unstack() Method, Part 2 00:06:09
    12. The .unstack() Method, Part 3 00:05:09
    13. The .pivot() Method 00:06:34
    14. The .pivot_table() Method 00:10:16
    15. The pd.melt() Method 00:05:59
  8. Chapter 8 : GroupBy
    1. Intro to the Groupby Module 00:07:42
    2. First Operations with groupby Object 00:09:33
    3. Retrieve A Group with the .get_group() Method 00:03:47
    4. Methods on the Groupby Object and DataFrame Columns 00:08:41
    5. Grouping by Multiple Columns 00:04:35
    6. The .agg() Method 00:06:11
    7. Iterating through Groups 00:09:04
  9. Chapter 9 : Merging, Joining, and Concatenating
    1. Intro to the Merging, Joining, and Concatenating Module 00:05:47
    2. The pd.concat() Method, Part 1 00:05:39
    3. The pd.concat() Method, Part 2 00:06:35
    4. The .append() Method on a DataFrame 00:02:03
    5. Inner Joins, Part 1 00:09:18
    6. Inner Joins, Part 2 00:09:01
    7. Outer Joins 00:12:23
    8. Left Joins 00:09:19
    9. The left_on and right_on Parameters 00:08:54
    10. Merging by Indexes with the left_index and right_index Parameters 00:11:02
    11. The .join() Method 00:03:15
    12. The pd.merge() Method 00:03:06
  10. Chapter 10 : Working with Dates and Times
    1. Intro to the Working with Dates and Times Module 00:03:44
    2. Review of Python's datetime Module 00:09:31
    3. The Pandas Timestamp Object 00:07:15
    4. The Pandas DateTimeIndex Object 00:05:23
    5. The pd.to_datetime() Method 00:11:11
    6. Create Range of Dates with the pd.date_range() Method, Part 1 00:10:22
    7. Create Range of Dates with the pd.date_range() Method, Part 2 00:09:04
    8. Create Range of Dates with the pd.date_range() Method, Part 3 00:07:50
    9. The .dt Accessor 00:07:29
    10. Install Pandas-datareader Library 00:02:30
    11. Import Financial Data Set with Pandas_datareader Library 00:10:43
    12. Selecting Rows from a DataFrame with a DateTimeIndex 00:08:01
    13. Timestamp Object Attributes 00:07:27
    14. The .truncate() Method 00:02:59
    15. pd.DateOffset Objects 00:12:00
    16. More Fun with pd.DateOffset Objects 00:14:06
    17. The Pandas Timedelta Object 00:08:40
    18. Timedeltas in a Dataset 00:09:30
  11. Chapter 11 : Panels
    1. Intro to the Module + Fetch Panel Dataset from Google Finance 00:07:17
    2. The Axes of a Panel Object 00:07:42
    3. Panel Attributes 00:05:04
    4. Use Bracket Notation to Extract a DataFrame from a Panel 00:03:59
    5. Extracting with the .loc, .iloc, and .ix Methods 00:06:57
    6. Convert Panel to a MultiIndex DataFrame (and Vice Versa) 00:04:04
    7. The .major_xs() Method 00:05:46
    8. The .minor_xs() Method 00:06:24
    9. Transpose a Panel with the .transpose() Method 00:07:42
    10. The .swapaxes() Method 00:04:22
  12. Chapter 12 : Input and Output
    1. Intro to the Input and Output Module 00:01:33
    2. Feed pd.read_csv() Method a URL Argument 00:03:48
    3. Quick Object Conversions 00:05:04
    4. Export DataFrame to CSV File with the .to_csv() Method 00:05:49
    5. Install xlrd and openpyxl Libraries to Read and Write Excel Files 00:02:36
    6. Import Excel File into Pandas 00:09:30
    7. Export Excel File 00:08:42
  13. Chapter 13 : Visualization
    1. Intro to Visualization Module 00:04:16
    2. The .plot() Method 00:09:13
    3. Modifying Aesthetics with Templates 00:05:20
    4. Bar Graphs 00:06:24
    5. Pie Charts 00:05:07
    6. Histograms 00:06:09
  14. Chapter 14 : Options and Settings
    1. Introduction to the Options and Settings Module 00:01:42
    2. Changing Pandas Options with Attributes and Dot Syntax 00:06:57
    3. Changing Pandas Options with Methods 00:06:14
    4. The precision Option 00:03:10
  15. Chapter 15 : Conclusion
    1. Conclusion 00:01:39