Join Michele Vallisneri for an in-depth discussion in this video ✓ Solution: Personal email analytics, part of Python Statistics Essential Training.
(tech noise)…- [Instructor] Let's do some cleaning.…We import our standard packages…and then load the tb.csv file into a Panda's data frame.…Let's look at this table.…And let's print out the column names.…We need to apply the Panda's Melt Operation.…To identify our columns, are just country and year.…
All the columns that express the sex and age range…for an observation has been mounted into separate rows.…You can just copy their names from above.…The melted viable name will be sexage.…And the value variable name will be cases.…Let's see.…We also separate the combined sex/age column into two,…using a string slicing operation.…
We access string operations for a Panda series…by going through .str.…For the sex we just need the first character.…For the age range the characters that follow.…This worked.…We should also rename the age range values…with something more readable.…We can do such a replacement with a Panda's Method Map…and giving it a dictionary.…
For instance 04 returning to 0-4, and so on.…Let's go to a new line.…
- 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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