Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Data analysis with Python & Pandas
Getting Started
Course Introduction (4:21)
An Example of Using Python for Data Analysis And Visualization (8:03)
Installing Python and its Libraries (8:24)
Python Editors: Spyder and iPython (3:21)
Python Basics
Section Intro
Variables (2:47)
Strings and Numbers (4:25)
If, Else, and Indentation (4:06)
Functions (3:09)
Sequences (2:57)
Collections (3:28)
Working with Sequences (7:27)
Iterating (3:37)
Working with Files
Section intro
Working with Files (5:29)
Handling Files Easily (1:44)
Working with Directories (3:50)
Working with File Paths - Advanced (6:47)
Iterating Through Files (6:09)
Downloading Files from FTP Sites
Section Intro (1:34)
Navigating Through FTP Directory Trees with Python (7:00)
Storing Python Code (4:32)
Creating an FTP Function (2:29)
Downloading an FTP File (8:32)
Note
Practice No.1: Creating an FTP File Downloader (13:42)
Working with Archive Files
Extracting ZIP, TAR, GZ and Other Archive Formats (3:41)
Extracting RAR Files (1:57)
Practice No.2: Creating a Batch Archive Extractor (5:52)
Working with TXT and CSV Files
Section Intro (1:22)
Reading Delimited TXT and CSV Files (10:06)
Exporting Data from Python to Files (4:14)
Reading Fixed Width Files (1:58)
Exporting Data Back to HTML and Other File Formats (1:02)
Exercise 1 of 6
Solution 1 of 6
Getting Started with Pandas
Get Started with Pandas (6:16)
Practice No.3: Calculating and Adding Columns to CSV Files (4:57)
Exercise 2 of 6
Solution 2 of 6
Concatenating and Joining Tables of Dat a with Pandas
Practical No.4: Concatenating Multiple CSV files (6:18)
Exercise 3 of 6
Solution 3 of 6
Practice No. 5: Joining Data Based on a Matching Column (8:59)
Exercise 4 of 6
Solution 4 of 6
Exercise 5 of 6
Solution 5 of 6
Data Aggregation
Practice No. 6: Pivoting Large Amounts of Data (7:41)
Visualizing Data
Data Visualization with Python (11:31)
More Visualization Techniques (12:23)
Practice No. 7: Producing JPG Files (3:08)
Exercise 6 of 6
Solution 6 of 6
Mapping Spatial Data
Programmatically Creating KML Google Earth Files with Python (4:37)
Practice No. 8: Creating KML Google Earth Files from CSV Data (7:46)
Putting Everything Together
User Interaction (6:07)
Practice No. 9: Polishing the Program I (5:00)
Practice No, 10: Polishing the Program II (5:30)
Practice No. 11: Creating Python Modules (5:00)
Bonus Section: Using Python in Jupyter Notebooks to Boost Productivity
Getting Started with Jupyter Notebooks (12:10)
Data Cleaning Project, Part I (8:40)
Data Cleaning Project, Part II (20:18)
More Visualization Techniques
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock