Course Content

Data Preprocessing

1. Brief Introduction

Data Types Data Processing Methods Dataset: Test and Training Deleting an "Extra" Data Changing the Data Type

2. Processing Quantitative Data

Data Scaling Data Scaling vs Data Normalization Removing Outliers Removing Missing Values Data Augmentation: Synthetic Data

3. Processing Categorical Data

Methods for Encoding the Categorical Data One-Hot Encoding Ordinal Encoding Label Encoding of the Target Variable Challenge

4. Time Series Data Processing

Data Type Conversion Data Cleaning Stationarity Denoising Train/Test Split & Cross Validation Challenge

5. Feature Engineering

Technique Idea Realization Feature Extraction from Text Feature Extraction from Images Feature Extraction from Time Series Challenge

6. Moving on to Tasks

Challenge 1 Challenge 2 Challenge 3

Data Types

The main tool we will use to manipulate data is pandas. We can start right away by loading the data:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.head())

As you understand, each dataset can contain many different data types, for example, numeric (integers, floating point numbers), strings (str), and datetime. To find out what data type a column has, you can call the .dtypes property:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.dtypes)

Let's say you have a column with numeric values but in string format and want to change the data type to numeric. To do this, use the .astype() method:

Task

Read the penguins.csv dataset and change the data type in the body_mass_g column from float to int.

Don't modify the initial code, only replace the gaps ___ with the correct code.

_{Once you've completed this task, click the button below the code to check your solution.}

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

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 1

Data Types

The main tool we will use to manipulate data is pandas. We can start right away by loading the data:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.head())


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.dtypes)

Let's say you have a column with numeric values but in string format and want to change the data type to numeric. To do this, use the .astype() method:

Task

Read the penguins.csv dataset and change the data type in the body_mass_g column from float to int.

Don't modify the initial code, only replace the gaps ___ with the correct code.

_{Once you've completed this task, click the button below the code to check your solution.}

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

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 1

Data Types

The main tool we will use to manipulate data is pandas. We can start right away by loading the data:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.head())


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.dtypes)

Let's say you have a column with numeric values but in string format and want to change the data type to numeric. To do this, use the .astype() method:

Task

Read the penguins.csv dataset and change the data type in the body_mass_g column from float to int.

Don't modify the initial code, only replace the gaps ___ with the correct code.

_{Once you've completed this task, click the button below the code to check your solution.}

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

Everything was clear?

Thanks for your feedback!

The main tool we will use to manipulate data is pandas. We can start right away by loading the data:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.head())


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/penguins.csv')

print(df.dtypes)

Let's say you have a column with numeric values but in string format and want to change the data type to numeric. To do this, use the .astype() method:

Task

Read the penguins.csv dataset and change the data type in the body_mass_g column from float to int.

Don't modify the initial code, only replace the gaps ___ with the correct code.

_{Once you've completed this task, click the button below the code to check your solution.}

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

Section 1. Chapter 1

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