Hello and welcome! In today's lesson, we will focus on creating a new feature called volume in the diamonds dataset using Pandas. Feature engineering is a crucial skill for data scientists because it helps extract additional information and insights from the data. By the end of this lesson, you will be able to create a new feature by multiplying multiple columns together and understand why this is useful.
The diamonds dataset is a popular dataset in data science, commonly used for practice and experimentation. It contains data on the physical characteristics of diamonds such as carat, cut, color, clarity, depth, table, and the three dimensions (x, y, z). Feature engineering involves creating new features based on the existing ones to better capture the underlying patterns in the data.
Why is feature engineering important?
- It can improve the performance of machine learning models.
- It helps in uncovering hidden relationships between variables.
- It aids in the interpretability of data analyses.
In the diamonds dataset, the x, y, and z columns represent the length, width, and depth of the diamonds, respectively. These dimensions are crucial for calculating the volume of each diamond.
Python1import seaborn as sns 2import pandas as pd 3 4# Load the diamonds dataset 5diamonds = sns.load_dataset('diamonds') 6 7# Display specific columns to understand the dimensions 8print(diamonds[['x', 'y', 'z']].head())
The output of the above code will be:
Plain text1 x y z 20 3.95 3.98 2.43 31 3.89 3.84 2.31 42 4.05 4.07 2.31 53 4.20 4.23 2.63 64 4.34 4.35 2.75
This output directly displays the first few values in the dimensions columns, indicating the length, width, and depth measurements of the first five diamonds in the dataset. Understanding these dimensions is crucial for our next step in feature engineering, which involves calculating the volume of each diamond.
These dimensions can be multiplied together to create a new feature that represents the volume of each diamond.
Now, let's create a new feature called volume by multiplying the x, y, and z columns. This new feature will provide us with information about the volume of each diamond.
Python1import seaborn as sns 2import pandas as pd 3 4# Load the diamonds dataset 5diamonds = sns.load_dataset('diamonds') 6 7# Creating a new feature 'volume' (x * y * z) 8diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] 9 10# Verify the new feature by displaying the first few rows 11print(diamonds.head())
This line of code adds a new column volume to the dataset, which is the product of the x, y, and z columns. To ensure that the new volume feature has been added correctly, we will display the first few rows of the dataset again.
The output will be:
Plain text1 carat cut color clarity depth ... price x y z volume 20 0.23 Ideal E SI2 61.5 ... 326 3.95 3.98 2.43 38.202030 31 0.21 Premium E SI1 59.8 ... 326 3.89 3.84 2.31 34.505856 42 0.23 Good E VS1 56.9 ... 327 4.05 4.07 2.31 38.076885 53 0.29 Premium I VS2 62.4 ... 334 4.20 4.23 2.63 46.724580 64 0.31 Good J SI2 63.3 ... 335 4.34 4.35 2.75 51.917250 7 8[5 rows x 11 columns]
This demonstrates that our new volume feature has been successfully added to the dataset, expanding upon the pre-existing attributes to provide new insights into the physical properties of these diamonds.
Once we have created the volume feature, it's essential to analyze and understand its properties. We can start by calculating some basic statistics and visualizing its distribution.
Python1import seaborn as sns 2import pandas as pd 3 4# Load the diamonds dataset 5diamonds = sns.load_dataset('diamonds') 6 7# Creating a new feature 'volume' (x * y * z) 8diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] 9 10# Descriptive statistics of the new feature 11print(diamonds['volume'].describe())
The output of the above code will be:
Plain text1count 53940.000000 2mean 129.849403 3std 78.245262 4min 0.000000 525% 65.136830 650% 114.808572 775% 170.842451 8max 3840.598060 9Name: volume, dtype: float64
This summary gives us an insight into the volume distribution across all diamonds in the dataset, showcasing the variability and range, from the smallest to the largest volumes observed.
Next, we'll visualize the distribution of the volume feature.
Python1import seaborn as sns 2import pandas as pd 3import matplotlib.pyplot as plt 4 5# Load the diamonds dataset 6diamonds = sns.load_dataset('diamonds') 7 8# Creating a new feature 'volume' (x * y * z) 9diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] 10 11# Visualizing the distribution of the 'volume' feature 12sns.histplot(diamonds['volume'], kde=True) 13plt.title('Distribution of Volume') 14plt.show()
This visualization helps us understand the distribution of volume across the diamonds in the dataset, presenting a clear picture of how volume varies, with the majority of diamonds having a volume that falls within a specific range, yet some outliers exist with significantly larger volumes.
In this lesson, you learned how to create a new feature called volume in the diamonds dataset by multiplying the dimensions (x, y, z). You also learned how to verify and analyze this new feature. These steps are crucial in feature engineering, helping data scientists derive more meaningful insights from their data.
As a practice exercise, try creating another feature called density by dividing the carat by the volume. Verify and analyze the density feature to reinforce your understanding.
Keep practicing these skills to become proficient in feature engineering and enhance your data analysis capabilities. Great work!