Mô tả

This is a hands-on, project-based course designed to help you master two of the most popular Python packages for data analysis: NumPy and Pandas.


We'll start with a NumPy primer to introduce arrays and array properties, practice common operations like indexing, slicing, filtering and sorting, and explore important concepts like vectorization and broadcasting.


From there we'll dive into Pandas, and focus on the essential tools and methods to explore, analyze, aggregate and transform series and dataframes. You'll practice plotting dataframes with charts and graphs, manipulating time-series data, importing and exporting various file types, and combining dataframes using common join methods.


Throughout the course you'll play the role of Data Analyst for Maven Mega Mart, a large, multinational corporation that operates a chain of retail and grocery stores. Using the Python skills you learn throughout the course, you'll work with members of the Maven Mega Mart team to analyze products, pricing, transactions, and more.


COURSE OUTLINE:


  • Intro to NumPy & Pandas

    • Introduce NumPy and Pandas, two critical Python libraries that help structure data in arrays & DataFrames and contain built-in functions for data analysis


  • Pandas Series

    • Introduce Pandas Series, the Python equivalent of a column of data, and cover their basic properties, creation, manipulation, and useful functions for analysis


  • Intro to DataFrames

    • Work with Pandas DataFrames, the Python equivalent of an Excel or SQL table, and use them to store, manipulate, and analyze data efficiently


  • Manipulating DataFrames

    • Aggregate & reshape data in DataFrames by grouping columns, performing aggregation calculations, and pivoting & unpivoting data


  • Basic Data Visualization

    • Learn the basics of data visualization in Pandas, and use the plot method to create & customize line charts, bar charts, scatterplots, and histograms


  • MID-COURSE PROJECT

    • Put your skills to the test with a brand new dataset, and use your Python skills to analyze and evaluate a new retailer as a potential acquisition target for Maven MegaMart


  • Analyzing Dates & Times

    • Learn how to work with the datetime data type in Pandas to extract date components, group by dates, and perform time intelligence calculations like moving averages


  • Importing & Exporting Data

    • Read in data from flat files and apply processing steps during import, create DataFrames by querying SQL tables, and write data back out to its source


  • Joining DataFrames

    • Combine multiple DataFrames by joining data from related fields to add new columns, and appending data with the same fields to add new rows


  • FINAL COURSE PROJECT

    • Put the finishing touches on your project by joining a new table, performing time series analysis, optimizing your workflow, and writing out your results


Join today and get immediate, lifetime access to the following:


  • 13+ hours of high-quality video

  • Python & Pandas PDF ebook (350+ pages)

  • Downloadable project files & solutions

  • Expert support and Q&A forum

  • 30-day Udemy satisfaction guarantee


If you're a data scientist, BI analyst or data engineer looking to add Pandas to your Python skill set, this course is for you.


Happy learning!

-Chris Bruehl (Python Expert & Lead Python Instructor, Maven Analytics)

__________

Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!


See why our courses are among the TOP-RATED on Udemy:


"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C.


"This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M.


"Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.

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Nội dung khoá học

12 sections

Getting Started

6 lectures
Course Structure & Outline
01:49
READ ME: Important Notes for New Students
02:13
DOWNLOAD: Course Resources
00:13
Introducing the Course Project
00:52
Setting Expectations
01:18
Jupyter Installation & Launch
05:35

NumPy Primer

29 lectures
Pandas & NumPy Intro
02:53
Numpy Arrays & Array Properties
07:41
ASSIGNMENT: Array Basics
01:47
Array Creation
08:14
SOLUTION: Array Basics
02:02
Random Number Generation
05:58
ASSIGNMENT: Array Creation
01:30
SOLUTION: Array Creation
04:22
Indexing & Slicing Arrays
09:09
ASSIGNMENT: Indexing & Slicing Arrays
01:06
SOLUTION: Indexing & Slicing Arrays
02:23
Array Operations
07:45
ASSIGNMENT: Array Operations
02:06
SOLUTION: Array Operations
04:17
Filtering Arrays & Modifying Array Values
10:56
The Where Function
04:00
ASSIGNMENT: Filtering & Modifying Arrays
01:57
SOLUTION: Filtering & Modifying Arrays
03:11
Array Aggregation
06:51
Array Functions
07:41
Sorting Arrays
03:51
ASSIGNMENT: Aggregation & Sorting
01:11
SOLUTION: Aggregation & Sorting
01:35
Vectorization
04:19
Broadcasting
07:08
ASSIGNMENT: Bringing it all together
02:45
SOLUTION: Bringing it all together
06:18
Key Takeaways
01:56
QUIZ: NumPy Primer
10 questions

Pandas Series

33 lectures
Series Basics
08:06
Pandas Data Types & Type Conversion
06:46
ASSIGNMENT: Data Types & Type Conversion
02:23
SOLUTION: Data Types & Type Conversion
03:05
The Series Index & Custom Indices
07:06
The .iloc Accessor
04:33
The .loc Accessor
07:03
Duplicate Index Values & Resetting The Index
06:33
ASSIGNMENT: Accessing Data & Resetting The Index
02:01
SOLUTION: Accessing Data & Resetting The Index
02:39
Filtering Series & Logical Tests
08:19
Sorting Series
03:45
ASSIGNMENT: Sorting & Filtering Series
00:57
SOLUTION: Sorting & Filtering Series
03:24
Numeric Series Operations
06:31
Text Series Operations
07:04
ASSIGNMENT: Series Operations
01:36
SOLUTION: Series Operations
03:53
Numerical Series Aggregation
05:43
Categorical Series Aggregation
03:32
ASSIGNMENT: Series Aggregation
00:50
SOLUTION: Series Aggregation
04:00
Missing Data Representation in Pandas
04:29
Identifying Missing Data
02:15
Fixing Missing Data
09:27
ASSIGNMENT: Missing Data
00:45
SOLUTION: Missing Data
01:35
Applying Custom Functions to Series
04:06
Pandas Where (vs. NumPy Where)
06:03
ASSIGNMENT: Apply & Where
01:09
SOLUTION: Apply & Where
04:37
Key Takeaways
01:24
QUIZ: Pandas Series
8 questions

Intro to DataFrames

47 lectures
DataFrame Basics
04:20
Creating a DataFrame
04:59
Exploring DataFrames: Heads, Tails & Sample
03:35
ASSIGNMENT: DataFrame Basics
00:53
SOLUTION: DataFrame Basics
01:46
Exploring DataFrames: Info & Describe
08:20
ASSIGNMENT: Exploring a DataFrame
00:59
SOLUTION: Exploring a DataFrame
04:03
Accessing DataFrame Columns
04:53
Accessing DataFrame Data with .iloc & .loc
06:06
ASSIGNMENT: Accessing DataFrame Data
01:18
SOLUTION: Accessing DataFrame Data
03:23
Dropping Columns & Rows
05:54
Identifying & Dropping Duplicates
07:00
ASSIGNMENT: Dropping Data
01:01
SOLUTION: Dropping Data
02:38
Missing Data
03:17
ASSIGNMENT: Missing Data
00:51
SOLUTION: Missing Data
02:13
Filtering DataFrames
04:29
PRO TIP: The Query Method
04:15
ASSIGNMENT: Filtering DataFrames
01:30
SOLUTION: Filtering DataFrames
06:46
Sorting DataFrames
06:53
ASSIGNMENT: Sorting DataFrames
00:44
SOLUTION: Sorting DataFrames
02:45
Renaming & Reordering Columns
03:10
ASSIGNMENT: Renaming & Reordering Columns
00:54
SOLUTION: Renaming & Reordering Columns
03:18
Arithmetic & Boolean Column Creation
06:22
ASSIGNMENT: Arithmetic & Boolean Columns
01:40
SOLUTION: Arithmetic & Boolean Columns
03:58
PRO TIP: Advanced Conditional Columns with Select
06:00
ASSIGNMENT: The Select Function
01:46
SOLUTION: The Select Function
03:34
The Map Method
04:24
PRO TIP: Multiple Column Creation with Assign
08:19
ASSIGNMENT: Map & Assign
01:24
SOLUTION: Map & Assign
02:38
The Categorical Data Type
05:31
Type Conversion
01:37
PRO TIP: Memory Usage & DataTypes
06:02
PRO TIP: Downcasting Numeric Data Types
04:58
ASSIGNMENT: DataFrame DataTypes
01:24
SOLUTION: DataFrame DataTypes
03:19
Key Takeways
01:33
QUIZ: Intro to DataFrames
10 questions

Aggregating & Reshaping DataFrames

25 lectures
Basic Aggregations
04:14
The Groupby Method
04:32
ASSIGNMENT: Groupby
01:18
SOLUTION: Groupby
02:11
Grouping By Multiple Columns
04:41
ASSIGNMENT: Grouping By Multiple Columns
01:09
SOLUTION: Grouping By Multiple Columns
03:00
Multi-Index DataFrames
07:39
Modifying Multi-Indices
04:25
ASSIGNMENT: Multi-Index DataFrames
01:17
SOLUTION: Multi-Index DataFrames
04:01
The Agg Method & Named Aggregations
07:22
ASSIGNMENT: The Agg Method
01:22
SOLUTION: The Agg Method
03:01
PRO TIP: Transforming DataFrames
06:50
ASSIGNMENT: Transforming a DataFrame
01:18
SOLUTION: Transforming a DataFrame
04:27
Pivot Tables in Pandas
06:40
Multiple Aggregation Pivot Tables
02:54
PRO TIP: Pivot Table Heatmaps
04:35
Melting DataFrames
06:26
ASSIGNMENT: Pivot & Melt
01:04
SOLUTION: Pivot & Melt
05:39
Key Takeaways
01:53
QUIZ: Aggregating & Reshaping DataFrames
9 questions

Basic Data Visualization in Python

26 lectures
The matplotlib API & The .plot() Method
09:33
ASSIGNMENT: Basic Line Chart
00:49
SOLUTION: Basic Line Chart
03:01
Chart Titles
03:26
Chart Colors
05:13
Line Styles
02:01
Chart Legends & Gridlines
03:51
Chart Styles
04:08
ASSIGNMENT: Stylized Line Chart
01:11
SOLUTION: Stylized Line Chart
01:21
Subplots & Figure Size
05:28
ASSIGNMENT: Subplots
01:33
SOLUTION: Subplots
02:59
Bar Charts
06:13
Grouped & Stacked Bar Charts
05:09
ASSIGNMENT: Bar Charts
01:11
SOLUTION: Bar Charts
02:19
Pie Charts & Scatterplots
06:52
ASSIGNMENT: Scatterplots
01:00
SOLUTION: Scatterplots
02:10
Histograms
03:46
ASSIGNMENT: Histograms
00:33
SOLUTION: Histograms
01:18
Saving Plots & Further Exploration
03:41
Key Takeaways
02:12
QUIZ: Basic Data Visualization in Python
9 questions

MID-COURSE PROJECT

2 lectures
Mid-Course Project Intro
07:11
SOLUTION: Mid-Course Project
18:22

Analyzing Dates & Times

25 lectures
Times in Python and Pandas
03:08
Converting To Datetimes
06:16
Formatting Dates
05:20
Date & Time Parts
03:04
ASSIGNMENT: Pandas Datetime Basics
01:23
SOLUTION: Pandas Datetime Basics
02:10
Time Deltas & Arithmetic
06:54
ASSIGNMENT: Time Deltas
01:10
SOLUTION: Time Deltas
01:29
Time Series Indices
03:58
Missing Time Series Data
04:45
ASSIGNMENT: Missing Time Series Data
01:44
SOLUTION: Missing Time Series Data
02:13
Shifting Time Series
03:16
PRO TIP: DIFF()
02:54
ASSIGNMENT: Shift & Diff
01:39
SOLUTION: Shift & Diff
02:47
Aggregation & Resampling
04:06
ASSIGNMENT: Resampling
00:41
SOLUTION: Resampling
01:53
Rolling Aggregations
04:35
ASSIGNMENT: Rolling Aggregations
00:45
SOLUTION: Rolling Aggregations
00:55
Key Takeaways
01:37
QUIZ: Analyzing Dates & Times
8 questions

Importing & Exporting Data

15 lectures
Preprocessing with read_csv
05:30
Column Selection
03:46
Row Selection & Missing Values
04:44
Parsing Dates & Data Types
03:34
PRO TIP: Converters
02:48
ASSIGNMENT: Importing Data
01:44
SOLUTION: Importing Data
04:19
Importing from Text & Excel Files
05:21
Exporting to Flat Files
02:09
ASSIGNMENT: Importing & Exporting Excel Data
01:19
SOLUTION: Importing & Exporting Excel Data
01:54
Working With SQL Databases
06:22
Other Supported File Formats
02:34
Key Takeaways
01:11
QUIZ: Importing & Exporting Data
6 questions

Joining DataFrames

13 lectures
Why Multiple Tables
01:56
Appending DataFrames
04:41
ASSIGNMENT: Appending DataFrames
01:25
SOLUTION: Appending DataFrames
01:38
Joining DataFrames
02:23
Join Types
03:42
Inner Joins
03:22
Left Joins
04:22
ASSIGNMENT: Joining DataFrames
01:55
SOLUTION: Joining DataFrames
05:41
The Join Method
01:56
Key Takeaways
01:17
QUIZ: Joining DataFrames
6 questions

FINAL COURSE PROJECT

2 lectures
Final Project Intro
05:28
SOLUTION: Final Project
17:16

BONUS LESSON

1 lectures
BONUS LESSON
01:42

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