Mô tả

Student Testimonials:

  • The instructor knows the material, and has detailed explanation on every topic he discusses. Has clarity too, and warns students of potential pitfalls. He has a very logical explanation, and it is easy to follow him. I highly recommend this class, and would look into taking a new class from him. - Diana

  • This is excellent, and I cannot complement the instructor enough. Extremely clear, relevant, and high quality - with helpful practical tips and advice. Would recommend this to anyone wanting to learn pandas. Lessons are well constructed. I'm actually surprised at how well done this is. I don't give many 5 stars, but this has earned it so far. - Michael

  • This course is very thorough, clear, and well thought out. This is the best Udemy course I have taken thus far. (This is my third course.) The instruction is excellent! - James


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"!

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!

Bạn sẽ học được gì

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

Yêu cầu

  • 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

Nội dung khoá học

15 sections

Installation and Setup

11 lectures
Introduction to the Course
11:13
macOS - Download and Install the Anaconda Distribution
04:02
Windows - Download and Install the Anaconda Distribution
04:12
How to Uninstall the Anaconda Distribution
00:57
Use Anaconda Navigator to Create a New Environment
10:24
Download Course Materials
00:13
Unpack Course Materials + The Startdown and Shutdown Process
11:30
Intro to the Jupyter Lab Interface
10:24
Code Cell Execution
14:24
Import Libraries into Jupyter Lab
08:14
Installation and Setup
7 questions

Python Crash Course

20 lectures
Comments
08:10
Basic Data Types
15:00
Operators
20:50
Variables
12:39
Declare Variables
1 question
Built-in Functions
08:41
Built-in Functions
1 question
Custom Functions
19:58
Custom Functions
1 question
String Methods
25:28
String Methods
1 question
Lists
15:26
Creating Lists
1 question
Index Positions and Slicing
18:18
Index Positions and Slicing
1 question
Dictionaries
17:26
Creating Dictionaries
1 question
Classes
10:09
Navigating Libraries using Jupyter Lab
12:09
Python Crash Course
8 questions

Series

32 lectures
Create a Series Object from a List
10:15
Create a Series Object from a Dictionary
04:51
Create a Series Object
1 question
Intro to Series Methods
04:37
Intro to Attributes
11:08
Attributes and Methods on a Series
1 question
Parameters and Arguments
14:33
Parameters and Arguments
1 question
Import Series with the pd.read_csv Function
22:42
Import Series with the read_csv Function
1 question
The head and tail Methods
03:13
The head and tail Methods
1 question
Passing Series to Python Built-In Functions
06:57
Check for Inclusion with Python's in Keyword
06:42
Check for Inclusion with Python's in Keyword
1 question
The sort_values Method
06:38
The sort_values Method
1 question
The sort_index Method
04:54
The sort_index Method
1 question
Extract Series Values by Index Position
10:39
Extract Series Values by Index Label
06:18
Extract Series Values by Index Position or Index Label
1 question
The get Method
07:24
Overwrite a Series Value
06:56
The copy Method
10:38
Math Methods on Series Objects
06:32
Broadcasting
03:57
The value_counts Method
05:13
The value_counts Method
1 question
The apply Method
07:44
The map Method
09:26
Series
6 questions

DataFrames I: Introduction

20 lectures
Methods and Attributes between Series and DataFrames
18:31
Differences between Shared Methods
07:36
Select One Column from a DataFrame
10:04
Select One Column from a DataFrame
1 question
Select Multiple Columns from a DataFrame
04:41
Select Multiple Columns from a DataFrame
1 question
Add New Column to DataFrame
10:49
A Review of the value_counts Method
02:58
Drop DataFrame Rows with Missing Values
07:51
Drop DataFrame Rows with Missing Values
1 question
Fill in Missing Values with the fillna Method
07:22
The astype Method I
07:29
The astype Method II
07:47
The astype Method
1 question
Sort a DataFrame with the sort_values Method I
07:49
Sort a DataFrame with the sort_values Method II
12:13
Sort a DataFrame with the sort_values Method
1 question
Sort DataFrame with the sort_index Method
03:27
Rank Series Values with the rank Method
06:30
DataFrames I
6 questions

DataFrames II: Filtering Data

15 lectures
This Module's Dataset + Memory Optimization
19:40
Filter a DataFrame Based on a Condition
15:00
Filter a DataFrame Based on a Condition
1 question
Filter with More than One Condition (AND - &)
08:50
Filter DataFrame with More than One Condition (AND - &)
1 question
Filter with More than One Condition (OR - |)
10:27
Filter DataFrame with More than One Condition (OR - |)
1 question
The isin Method
04:38
The isin Method
1 question
The isnull and notnull Methods
03:55
The between Method
06:07
The between Method
1 question
The duplicated Method
09:43
The drop_duplicates Method
11:06
The unique and nunique Methods
04:33

DataFrames III: Data Extraction

13 lectures
This Module's Dataset
02:20
The set_index and reset_index Methods
10:32
Retrieve Rows by Index Position with iloc Accessor
05:08
Retrieve Rows by Index Label with loc Accessor
10:03
Second Arguments to loc and iloc Accessors
10:15
Overwrite Value in a DataFrame
08:01
Overwrite Multiple Values in a DataFrame
09:59
Rename Index Labels or Columns in a DataFrame
08:52
Delete Rows or Columns from a DataFrame
06:56
Create Random Sample with the sample Method
02:46
The nsmallest and nlargest Methods
07:30
Filtering with the where Method
04:03
The apply Method with DataFrames
13:33

Working with Text Data

8 lectures
This Module's Dataset
05:30
Common String Methods
08:32
Common String Methods
1 question
Filtering with String Methods
06:32
String Methods on Index and Columns
04:28
The split Method
05:06
More Practice with Splits
07:20
The expand and n Parameters of the split Method
07:55

MultiIndex

16 lectures
Intro to the MultiIndex Module
05:26
Create a MultiIndex
13:58
Create a MultiIndex
1 question
Extract Index Level Values
03:58
Extract Index Level Values
1 question
Rename Index Lebels
04:42
The sort_index Method on a MultiIndex DataFrame
06:15
Extract Rows from a MultiIndex DataFrame
19:09
Extract Rows from a MultiIndex DataFrame
1 question
The transpose Method
03:11
The stack Method
09:02
The unstack Method
11:42
The pivot Method
10:50
The melt Method
08:13
The melt Method
1 question
The pivot_table Method
13:35

GroupBy

7 lectures
Intro to the GroupBy Module
03:16
The groupby Method
08:11
Retrieve A Group with the get_group Method
02:30
Methods on the GroupBy Object
07:03
Grouping by Multiple Columns
04:51
The agg Method
03:31
Iterating through Groups
07:25

Merging DataFrames

10 lectures
Intro to the Merging DataFrames Module
06:18
The pd.concat Function I
08:34
The pd.concat Function II
09:21
Left Joins
08:35
The left_on and right_on Parameters
06:41
Inner Joins I
10:00
Inner Joins II
08:20
Full-Outer Joins
09:16
Merging by Indexes with the left_index and right_index Parameters
07:02
The join Method
04:01

Working with Dates and Times

8 lectures
Intro to the Working with Dates and Times Module and Review of Python's datetime
07:52
The Timestamp and DatetimeIndex Objects
09:50
Create Range of Dates with pd.date_range Function
11:41
The dt Attribute
07:04
Selecting Rows from a DataFrame with DatetimeIndex
06:54
The DateOffset Object
09:26
Specialized Date Offsets
06:56
Timedeltas
13:06

Input and Output

6 lectures
URL for Next Lesson's Dataset
00:10
Intro to the Input and Output Module
03:58
Export DataFrame to CSV File
05:27
Install openpyxl Library to Read and Write Excel Files
02:28
Import Excel File into pandas
10:38
Export Excel File from pandas
09:47

Visualization

5 lectures
Install matplotlib Library for Visualization
01:52
The plot Method
06:39
Modifying Plot Aesthetics with Templates
04:25
Bar Charts
04:58
Pie Charts
03:46

Options and Settings

4 lectures
Introduction to the Options and Settings Module
02:28
Changing Options with Attributes
13:59
Changing Options with Functions
07:51
The precision Option
04:02

Conclusion

2 lectures
Conclusion
01:39
Bonus!
00:20

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