Chapter 9: Data Preprocessing
9.4 Practical Exercises: Chapter 9: Data Preprocessing
Exercise 9.1: Data Cleaning
You have a dataset with missing values and outliers.
import pandas as pd
import numpy as np
# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', None, 'Eve'],
'Age': [25, np.nan, 35, 40, 50],
'Salary': [50000, 70000, 120000, 110000, 90000],
'Experience': [2, 10, np.nan, 7, 15]}
df = pd.DataFrame(data)- Remove rows where
Nameis missing. - Fill missing values in the
AgeandExperiencecolumns with their respective means.
Solution
# Remove rows where Name is missing
df.dropna(subset=['Name'], inplace=True)
# Fill missing values with mean
df['Age'].fillna(df['Age'].mean(), inplace=True)
df['Experience'].fillna(df['Experience'].mean(), inplace=True)Exercise 9.2: Feature Engineering
Create a new feature called AgeGroup in the above DataFrame based on the Age column. Use the following groups: "Young" for Age <=30, "Middle-aged" for Age between 31 and 45, and "Senior" for Age > 45.
Solution
df['AgeGroup'] = pd.cut(df['Age'], bins=[0, 30, 45, np.inf], labels=['Young', 'Middle-aged', 'Senior'])Exercise 9.3: Data Transformation
Apply Min-Max scaling to the Salary column of the DataFrame.
Solution
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df['Salary'] = scaler.fit_transform(df[['Salary']])9.4 Practical Exercises: Chapter 9: Data Preprocessing
Exercise 9.1: Data Cleaning
You have a dataset with missing values and outliers.
import pandas as pd
import numpy as np
# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', None, 'Eve'],
'Age': [25, np.nan, 35, 40, 50],
'Salary': [50000, 70000, 120000, 110000, 90000],
'Experience': [2, 10, np.nan, 7, 15]}
df = pd.DataFrame(data)- Remove rows where
Nameis missing. - Fill missing values in the
AgeandExperiencecolumns with their respective means.
Solution
# Remove rows where Name is missing
df.dropna(subset=['Name'], inplace=True)
# Fill missing values with mean
df['Age'].fillna(df['Age'].mean(), inplace=True)
df['Experience'].fillna(df['Experience'].mean(), inplace=True)Exercise 9.2: Feature Engineering
Create a new feature called AgeGroup in the above DataFrame based on the Age column. Use the following groups: "Young" for Age <=30, "Middle-aged" for Age between 31 and 45, and "Senior" for Age > 45.
Solution
df['AgeGroup'] = pd.cut(df['Age'], bins=[0, 30, 45, np.inf], labels=['Young', 'Middle-aged', 'Senior'])Exercise 9.3: Data Transformation
Apply Min-Max scaling to the Salary column of the DataFrame.
Solution
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df['Salary'] = scaler.fit_transform(df[['Salary']])9.4 Practical Exercises: Chapter 9: Data Preprocessing
Exercise 9.1: Data Cleaning
You have a dataset with missing values and outliers.
import pandas as pd
import numpy as np
# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', None, 'Eve'],
'Age': [25, np.nan, 35, 40, 50],
'Salary': [50000, 70000, 120000, 110000, 90000],
'Experience': [2, 10, np.nan, 7, 15]}
df = pd.DataFrame(data)- Remove rows where
Nameis missing. - Fill missing values in the
AgeandExperiencecolumns with their respective means.
Solution
# Remove rows where Name is missing
df.dropna(subset=['Name'], inplace=True)
# Fill missing values with mean
df['Age'].fillna(df['Age'].mean(), inplace=True)
df['Experience'].fillna(df['Experience'].mean(), inplace=True)Exercise 9.2: Feature Engineering
Create a new feature called AgeGroup in the above DataFrame based on the Age column. Use the following groups: "Young" for Age <=30, "Middle-aged" for Age between 31 and 45, and "Senior" for Age > 45.
Solution
df['AgeGroup'] = pd.cut(df['Age'], bins=[0, 30, 45, np.inf], labels=['Young', 'Middle-aged', 'Senior'])Exercise 9.3: Data Transformation
Apply Min-Max scaling to the Salary column of the DataFrame.
Solution
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df['Salary'] = scaler.fit_transform(df[['Salary']])9.4 Practical Exercises: Chapter 9: Data Preprocessing
Exercise 9.1: Data Cleaning
You have a dataset with missing values and outliers.
import pandas as pd
import numpy as np
# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', None, 'Eve'],
'Age': [25, np.nan, 35, 40, 50],
'Salary': [50000, 70000, 120000, 110000, 90000],
'Experience': [2, 10, np.nan, 7, 15]}
df = pd.DataFrame(data)- Remove rows where
Nameis missing. - Fill missing values in the
AgeandExperiencecolumns with their respective means.
Solution
# Remove rows where Name is missing
df.dropna(subset=['Name'], inplace=True)
# Fill missing values with mean
df['Age'].fillna(df['Age'].mean(), inplace=True)
df['Experience'].fillna(df['Experience'].mean(), inplace=True)Exercise 9.2: Feature Engineering
Create a new feature called AgeGroup in the above DataFrame based on the Age column. Use the following groups: "Young" for Age <=30, "Middle-aged" for Age between 31 and 45, and "Senior" for Age > 45.
Solution
df['AgeGroup'] = pd.cut(df['Age'], bins=[0, 30, 45, np.inf], labels=['Young', 'Middle-aged', 'Senior'])Exercise 9.3: Data Transformation
Apply Min-Max scaling to the Salary column of the DataFrame.
Solution
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df['Salary'] = scaler.fit_transform(df[['Salary']])