Learn about applying functions to columns; simple regular expressions; datetime transformations; dropping null data; and pandas categorical variables.
- [Instructor] In this age of big data…our life and behaviors are constantly being recorded…in digital artifacts that we leave around us…and in the cloud.…It's sometimes easy to obtain this data…and to analyze them to perhaps…understand ourselves better and improve our lives.…For instance, if you use Google mail,…it's easy to retrieve all your messages…at takeout.google.com.…With these messages we will go through a combination…of data cleaning and visualization to analyze mail behavior.…
This video was inspired by a post…by my ex-colleague, Justin Ellis.…So I have now loaded my mailbox from Google…and I have used this standup Python library module mailbox…to convert my sent messages into a pandas frame…by way of a CSV file.…For privacy I'm not giving you my actual sent mailbox,…but an anonymized CSV file.…However, in the exercise files notebook…I'm showing you the code that I used to convert my mailbox.…
You may have to do something slightly different…depending on the settings of your main server.…So I read the anonymized mailbox back into pandas…
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
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