Ending In:
access
lifetime
content
2.0 Hours
Description
Python Pandas are one of the most used libraries in Python when it comes to data analysis and manipulation. Whether in finance, scientific fields, or data science, familiarity with Pandas is a must-have. This course teaches you how to work with real-world data sets for analyzing data in Python using Pandas. Not only will you learn how to manipulate and analyze data you will also learn powerful and easy to use visualization techniques for representing your data.
4.0/5 average rating:
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- Access 33 lectures & 2 hours of content 24/7
- Use Pandas DataFrames to work w/ tabular data
- Use conditional filtering to select relevant information from datasets
- Create graphical plots such as bar, line, area, scatter, & more
Tony Staunton
Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. He loves all things to do with programming, productivity, books, and the internet. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.
4.2/5 Instructor Rating:
★ ★ ★ ★
★
Tony Staunton is a Python development and productivity consultant, helping over 20,000 students. He loves all things to do with programming, productivity, books, and the internet. Tony created and ran his own software business and won several awards from, a most innovative startup to the best product.
Important Details
- Length of time users can access this course: lifetime
- Access options: desktop & mobile
- Certificate of completion included
- Redemption deadline: redeem your code within 30 days of purchase
- Updates included
- Experience level required: intermediate
Requirements
- Any device with basic specifications
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
Course Outline
- Course Introduction
- Course overview - 3:36
- How to access the source code of this course
- Setting up Python, Anaconda, Atom and GitHub
- Setting up Anaconda - 7:02
- Setting up the Atom text editor - 4:28
- How to create a virtual environment - 3:28
- How to clone a GitHub Repository - 5:44
- New Lecture
- How to use the Atom Text Editor to push code to GitHub
- Introduction to Python Pandas
- Introduction to Pandas - 1:05
- Introduction to Dataframes - 2:13
- Inspecting data - 17:46
- Conditional filtering - 3:51
- Using NumPy and Pandas together - 2:21
- Creating Dataframes from NumPy - 3:03
- Creating Dataframes from dictionaries - 6:16
- Using Broadcasting in Pandas - 1:40
- Labelling data in a Dataframe - 1:29
- Building Dataframes with broadcasting - 1:55
- Cleansing, Importing and Exporting Data - 12:19
- Creating plots with pandas - 10:29
- Visual Data Analysis
- Creating graphs with pandas line plot - 4:15
- Creating graphs with pandas scatter plot - 4:22
- Creating graphs with pandas bar plot - 2:01
- Statistical Exploratory Data Analysis - 6:07
- Filtering data - 5:38
- Managing Dates and Times with Python Pandas
- Introduction to pandas DateTime - 0:47
- Indexing panda time series - 5:38
- Creating and using a DateTimeIndex - 2:08
- Resampling Pandas Time Data - 5:36
- Method Chaining - 2:36
- Separating and Resampling - 2:29
- Additional Filtering Methods - 3:05
- Visualizing Pandas Time Data - 5:02
access
lifetime
content
2.0 Hours