Зміст курсу

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:

Завдання

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.}

Завдання

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.}

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 1. Розділ 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:

Завдання

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.}

Завдання

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.}

Все було зрозуміло?

Секція 1. Розділ 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:

Завдання

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.}

Завдання

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.}

Все було зрозуміло?

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:

Завдання

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.}

Секція 1. Розділ 1