00:01
In this video, we want to talk about elements of a tidy data frame.
00:06
So basically, this is essential to organizing data in a way that follows generic patterns and people can look at it and understand it really well and interpret it.
00:18
So in a tidy data frame, basically the principles are that you have variables and then these are denoted in the columns of your data frame.
00:37
So you have headers representing each of the variables.
00:41
And then the observations pertaining to each variable make up the rows of your data frame.
00:52
And then collectively, this creates a data table.
01:00
Okay, so saying each column is an observation, each row is a feature, that's backwards.
01:05
Each column is a variable and each row is an observation.
01:08
So that's not true.
01:09
Explicitly identifying missing values is important with numeric values um that's partial well it doesn't get to the heart of the the definition of a tidy data frame but it is important to have tidy data especially for pre -processing like if you're going to use your data for machine learning missing values have to be filled in so i would say that counts okay each column has a label known as an index nope that would be the rows are labeled with indices, the columns are going to be labeled with names of the variables.
01:43
So that's not consistent...