Data Analysis with Pandas and Python
19h 5m 40s
English
Paid
April 30, 2024
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!
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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"!
Over the course of more than 19 hours, I'll take you 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. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.
Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!
Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!
Requirements:
- Basic / intermediate experience with Microsoft Excel or another spreadsheet software (common functions, vlookups, Pivot Tables etc)
- Basic experience with the Python programming language
- Strong knowledge of data types (strings, integers, floating points, booleans) etc
Who this course is for:
- Data analysts and business analysts
- Excel users looking to learn a more powerful software for data analysis
What you'll learn:
- Perform a multitude of data operations in Python's popular "pandas" library including grouping, pivoting, joining and more!
- Learn hundreds of methods and attributes across numerous pandas objects
- Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
- Resolve common issues in broken or incomplete data sets
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| # | Title | Duration |
|---|---|---|
| 1 | Introduction to the Course | 12:15 |
| 2 | About Me | 00:57 |
| 3 | MacOS - Download the Anaconda Distribution | 03:56 |
| 4 | MacOS - Install Anaconda Distribution | 10:39 |
| 5 | MacOS - Access the Terminal | 08:35 |
| 6 | MacOS - Create Conda Environment and Install Pandas | 13:08 |
| 7 | MacOS - Unpack Course Materials + The Start and Shutdown Process | 12:33 |
| 8 | Windows - Download the Anaconda Distribution | 03:48 |
| 9 | Windows - Install Anaconda Distribution | 05:17 |
| 10 | Windows - Access the Command Prompt and Update Anaconda Libraries | 10:12 |
| 11 | Windows - Unpack Course Materials + The Startdown and Shutdown Process | 08:50 |
| 12 | Intro to the Jupyter Notebook Interface | 05:15 |
| 13 | Cell Types and Cell Modes | 07:04 |
| 14 | Code Cell Execution | 04:48 |
| 15 | Popular Keyboard Shortcuts | 03:07 |
| 16 | Import Libraries into Jupyter Notebook | 07:10 |
| 17 | Python Crash Course, Part 1 - Data Types and Variables | 07:06 |
| 18 | Python Crash Course, Part 2 - Lists | 05:07 |
| 19 | Python Crash Course, Part 3 - Dictionaries | 04:20 |
| 20 | Python Crash Course, Part 4 - Operators | 04:31 |
| 21 | Python Crash Course, Part 5 - Functions | 06:03 |
| 22 | Create Jupyter Notebook for the Series Module | 02:13 |
| 23 | Create A Series Object from a Python List | 10:33 |
| 24 | Create A Series Object from a Python Dictionary | 03:07 |
| 25 | Intro to Attributes | 07:18 |
| 26 | Intro to Methods | 04:43 |
| 27 | Parameters and Arguments | 10:11 |
| 28 | Import Series with the .read_csv() Method | 10:24 |
| 29 | The .head() and .tail() Methods | 03:43 |
| 30 | Python Built-In Functions | 05:21 |
| 31 | More Series Attributes | 06:14 |
| 32 | The .sort_values() Method | 06:05 |
| 33 | The inplace Parameter | 05:08 |
| 34 | The .sort_index() Method | 04:39 |
| 35 | Python's in Keyword | 04:01 |
| 36 | Extract Series Values by Index Position | 04:16 |
| 37 | Extract Series Values by Index Label | 07:23 |
| 38 | The .get() Method on a Series | 05:04 |
| 39 | Math Methods on Series Objects | 05:40 |
| 40 | The .idxmax() and .idxmin() Methods | 03:11 |
| 41 | The .value_counts() Method | 03:40 |
| 42 | The .apply() Method | 06:47 |
| 43 | The .map() Method | 06:53 |
| 44 | Intro to DataFrames I Module | 07:25 |
| 45 | Shared Methods and Attributes between Series and DataFrames | 07:38 |
| 46 | Differences between Shared Methods | 06:49 |
| 47 | Select One Column from a DataFrame | 07:58 |
| 48 | Select Two or More Columns from a DataFrame | 05:13 |
| 49 | Add New Column to DataFrame | 08:04 |
| 50 | Broadcasting Operations | 09:07 |
| 51 | A Review of the .value_counts() Method | 03:55 |
| 52 | Drop Rows with Null Values | 06:42 |
| 53 | Fill in Null Values with the .fillna() Method | 04:25 |
| 54 | The .astype() Method | 10:39 |
| 55 | Sort a DataFrame with the .sort_values() Method, Part I | 05:47 |
| 56 | Sort a DataFrame with the .sort_values() Method, Part II | 04:14 |
| 57 | Sort DataFrame with the .sort_index() Method | 03:00 |
| 58 | Rank Values with the .rank() Method | 05:54 |
| 59 | This Module's Dataset + Memory Optimization | 10:46 |
| 60 | Filter a DataFrame Based on A Condition | 12:58 |
| 61 | Filter with More than One Condition (AND - &) | 04:42 |
| 62 | Filter with More than One Condition (OR - |) | 08:36 |
| 63 | The .isin() Method | 06:18 |
| 64 | The .isnull() and .notnull() Methods | 05:08 |
| 65 | The .between() Method | 06:52 |
| 66 | The .duplicated() Method | 09:06 |
| 67 | The .drop_duplicates() Method | 08:17 |
| 68 | The .unique() and .nunique() Methods | 04:23 |
| 69 | Intro to the DataFrames III Module + Import Dataset | 03:24 |
| 70 | The .set_index() and .reset_index() Methods | 05:38 |
| 71 | Retrieve Rows by Index Label with .loc[] | 09:43 |
| 72 | Retrieve Rows by Index Position with .iloc[] | 06:08 |
| 73 | The Catch-All .ix[] Method | 08:45 |
| 74 | Second Arguments to .loc[], .iloc[], and .ix[] Methods | 06:22 |
| 75 | Set New Values for a Specific Cell or Row | 04:28 |
| 76 | Set Multiple Values in DataFrame | 09:17 |
| 77 | Rename Index Labels or Columns in a DataFrame | 06:50 |
| 78 | Delete Rows or Columns from a DataFrame | 07:30 |
| 79 | Create Random Sample with the .sample() Method | 04:44 |
| 80 | The .nsmallest() and .nlargest() Methods | 05:37 |
| 81 | Filtering with the .where() Method | 05:04 |
| 82 | The .query() Method | 09:07 |
| 83 | A Review of the .apply() Method on Single Columns | 05:54 |
| 84 | The .apply() Method with Row Values | 06:50 |
| 85 | The .copy() Method | 07:06 |
| 86 | Intro to the Working with Text Data Module | 05:56 |
| 87 | Common String Methods - lower, upper, title, and len | 07:15 |
| 88 | The .str.replace() Method | 08:08 |
| 89 | Filtering with String Methods | 06:44 |
| 90 | More String Methods - strip, lstrip, and rstrip | 04:31 |
| 91 | String Methods on Index and Columns | 05:31 |
| 92 | Split Strings by Characters with .str.split() Method | 08:41 |
| 93 | More Practice with Splits | 06:02 |
| 94 | The expand and n Parameters of the .str.split() Method | 07:01 |
| 95 | Intro to the MultiIndex Module | 04:27 |
| 96 | Create a MultiIndex with the set_index() Method | 09:51 |
| 97 | The .get_level_values() Method | 07:52 |
| 98 | The .set_names() Method | 03:09 |
| 99 | The sort_index() Method | 04:57 |
| 100 | Extract Rows from a MultiIndex DataFrame | 08:33 |
| 101 | The .transpose() Method and MultiIndex on Column Level | 05:49 |
| 102 | The .swaplevel() Method | 02:35 |
| 103 | The .stack() Method | 06:01 |
| 104 | The .unstack() Method, Part 1 | 03:39 |
| 105 | The .unstack() Method, Part 2 | 06:10 |
| 106 | The .unstack() Method, Part 3 | 05:10 |
| 107 | The .pivot() Method | 06:35 |
| 108 | The .pivot_table() Method | 10:17 |
| 109 | The pd.melt() Method | 06:00 |
| 110 | Intro to the Groupby Module | 07:43 |
| 111 | First Operations with groupby Object | 09:34 |
| 112 | Retrieve A Group with the .get_group() Method | 03:48 |
| 113 | Methods on the Groupby Object and DataFrame Columns | 08:42 |
| 114 | Grouping by Multiple Columns | 04:36 |
| 115 | The .agg() Method | 06:12 |
| 116 | Iterating through Groups | 09:05 |
| 117 | Intro to the Merging, Joining, and Concatenating Module | 05:48 |
| 118 | The pd.concat() Method, Part 1 | 05:40 |
| 119 | The pd.concat() Method, Part 2 | 06:36 |
| 120 | The .append() Method on a DataFrame | 02:04 |
| 121 | Inner Joins, Part 1 | 09:19 |
| 122 | Inner Joins, Part 2 | 09:01 |
| 123 | Outer Joins | 12:24 |
| 124 | Left Joins | 09:20 |
| 125 | The left_on and right_on Parameters | 08:55 |
| 126 | Merging by Indexes with the left_index and right_index Parameters | 11:03 |
| 127 | The .join() Method | 03:16 |
| 128 | The pd.merge() Method | 03:07 |
| 129 | Intro to the Working with Dates and Times Module | 03:45 |
| 130 | Review of Python's datetime Module | 09:32 |
| 131 | The pandas Timestamp Object | 07:16 |
| 132 | The pandas DateTimeIndex Object | 05:24 |
| 133 | The pd.to_datetime() Method | 11:12 |
| 134 | Create Range of Dates with the pd.date_range() Method, Part 1 | 10:23 |
| 135 | Create Range of Dates with the pd.date_range() Method, Part 2 | 09:05 |
| 136 | Create Range of Dates with the pd.date_range() Method, Part 3 | 07:51 |
| 137 | The .dt Accessor | 07:30 |
| 138 | Install pandas-datareader Library | 02:31 |
| 139 | Import Financial Data Set with pandas_datareader Library | 10:43 |
| 140 | Selecting Rows from a DataFrame with a DateTimeIndex | 08:02 |
| 141 | Timestamp Object Attributes | 07:28 |
| 142 | The .truncate() Method | 03:00 |
| 143 | pd.DateOffset Objects | 12:01 |
| 144 | More Fun with pd.DateOffset Objects | 14:07 |
| 145 | The pandas Timedelta Object | 08:40 |
| 146 | Timedeltas in a Dataset | 09:31 |
| 147 | Intro to the Module + Fetch Panel Dataset from Google Finance | 07:18 |
| 148 | The Axes of a Panel Object | 07:43 |
| 149 | Panel Attributes | 05:05 |
| 150 | Use Bracket Notation to Extract a DataFrame from a Panel | 04:00 |
| 151 | Extracting with the .loc, .iloc, and .ix Methods | 06:58 |
| 152 | Convert Panel to a MultiIndex DataFrame (and Vice Versa) | 04:05 |
| 153 | The .major_xs() Method | 05:47 |
| 154 | The .minor_xs() Method | 06:25 |
| 155 | Transpose a Panel with the .transpose() Method | 07:43 |
| 156 | The .swapaxes() Method | 04:23 |
| 157 | Intro to the Input and Output Module | 01:34 |
| 158 | Feed pd.read_csv() Method a URL Argument | 03:49 |
| 159 | Quick Object Conversions | 05:05 |
| 160 | Export DataFrame to CSV File with the .to_csv() Method | 05:49 |
| 161 | Install xlrd and openpyxl Libraries to Read and Write Excel Files | 02:37 |
| 162 | Import Excel File into pandas | 09:31 |
| 163 | Export Excel File | 08:43 |
| 164 | Intro to Visualization Module | 04:17 |
| 165 | The .plot() Method | 09:14 |
| 166 | Modifying Aesthetics with Templates | 05:21 |
| 167 | Bar Graphs | 06:25 |
| 168 | Pie Charts | 05:08 |
| 169 | Histograms | 06:10 |
| 170 | Introduction to the Options and Settings Module | 01:43 |
| 171 | Changing pandas Options with Attributes and Dot Syntax | 06:57 |
| 172 | Changing pandas Options with Methods | 06:14 |
| 173 | The precision Option | 03:11 |
| 174 | Conclusion | 01:39 |
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