8. PreProcessing - FORMATTING - example

1. saving FORMATTED

1.1. Python

1.1.1. read like this

1.2. R

1.2.1. read like this

2. You are **assuming** data is clean

2.1. See DATA TYPES

2.1.1. R: **str()**

2.1.2. Python: **.info()**

3. Format numeric data

3.1. R: **as.numeric()**

3.1.1. If NAs created, STOP, Explore and CLEAN

3.1.1.1. from strings...

3.1.1.1.1. to numeric

3.2. Python: **pd.to_numeric()**

3.2.1. always use RAISE

3.2.1.1. If NAs created, STOP, Explore and CLEAN

3.2.1.1.1. from strings...

4. Format categorical data

4.1. Nominal

4.1.1. Python

4.1.1.1. categories using numbers

4.1.2. R

4.1.2.1. categories using numbers

4.2. Ordinal

4.2.1. Python

4.2.1.1. Current LEVELS

4.2.1.1.1. NUMERIC representation

4.2.2. R

4.2.2.1. Current LEVELS

4.2.2.1.1. NUMERIC representation

5. Formattting text

5.1. Python

5.1.1. case:

5.1.1.1. non-ascii:

5.1.1.1.1. the change:

5.2. R

5.2.1. case:

5.2.1.1. non-ascii:

5.2.1.1.1. the change:

6. Format dates

6.1. Python

6.1.1. string

6.1.1.1. converting

6.2. R

6.2.1. string

6.2.1.1. converting