Course Content

Preprocessing Data

1. Data Exploration

Data Extracting Explore the Dataset Data Types

2. Data Cleaning

Missing and Wrong Data Work with NaNs Replace Numerical Missing Data with Values Dealing with Missing Ages Replace Categorical Missing Data with Values Replace Missing Values with Interpolation Removing Duplicates Removing Outliers Visualizing Outliers

3. Data Validation

Types Conversion Data Binning

4. Normalization & Standardization

Data Normalization Data Standardization

5. Data Encoding

Label Encoding One Hot Encoding

Types Conversion

You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:

storing integer or float values as string variables.
storing date and time values as strings.
storing values in a form that can be converted to a more suitable one.

Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:

unnamed	id	diet	pulse	time	kind
35	12	low fat	104	30 min	walking
64	22	low fat	104	15 min	running
10	4	low fat	82	15 min	rest
18	7	no fat	87	1 min	rest
48	17	no fat	103	1 min	walking

It makes sense to modify the time column data: all rows contain the duration in minutes, so info about time units (min, sec, ot hours) is useless. We're gonna remove the extra symbols and store only numerical values, which additionally will be converted to int.

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 1

Types Conversion

You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:

storing integer or float values as string variables.
storing date and time values as strings.
storing values in a form that can be converted to a more suitable one.

Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:

unnamed	id	diet	pulse	time	kind
35	12	low fat	104	30 min	walking
64	22	low fat	104	15 min	running
10	4	low fat	82	15 min	rest
18	7	no fat	87	1 min	rest
48	17	no fat	103	1 min	walking

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 1

Types Conversion

You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:

storing integer or float values as string variables.
storing date and time values as strings.
storing values in a form that can be converted to a more suitable one.

Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:

unnamed	id	diet	pulse	time	kind
35	12	low fat	104	30 min	walking
64	22	low fat	104	15 min	running
10	4	low fat	82	15 min	rest
18	7	no fat	87	1 min	rest
48	17	no fat	103	1 min	walking

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:

storing integer or float values as string variables.
storing date and time values as strings.
storing values in a form that can be converted to a more suitable one.

Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:

unnamed	id	diet	pulse	time	kind
35	12	low fat	104	30 min	walking
64	22	low fat	104	15 min	running
10	4	low fat	82	15 min	rest
18	7	no fat	87	1 min	rest
48	17	no fat	103	1 min	walking

Task

Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below