Grouping and Filtering Data with Pandas in Python

Understanding the Problem and the Solution

In this article, we’ll delve into the world of data manipulation with pandas in Python. Specifically, we’ll explore how to find the minimum value of a column (‘Age’) for each class (‘Pclass’) in the Titanic dataset, given that the fare paid by passengers is above the average.

Introduction to Pandas and Data Manipulation

Pandas is a powerful library in Python that provides data structures and functions designed to make working with structured data (such as tabular data) more efficient. In this article, we’ll use pandas to manipulate and analyze the Titanic dataset.

Installing and Importing Libraries

Before we begin, let’s ensure that you have the necessary libraries installed:

pip install pandas numpy scipy matplotlib

Now, import the required libraries in your Python script:

import pandas as pd
import numpy as np

Loading and Exploring the Titanic Dataset

The Titanic dataset is a classic example of tabular data. It consists of 7 features: PassengerId, Survived, Pclass, Name, Sex, Age, and Fare. In this article, we’ll focus on Age and Pclass.

# Load the Titanic dataset from a CSV file
df = pd.read_csv('https://drive.google.com/file/d/1NEHvlUMTNPusHZvHUFTqeUR_9yY1tHVz/view')

Exploring the Data

Let’s take a closer look at the data to understand its distribution:

# Print the first few rows of the dataset
print(df.head())

This will give us an idea of how the data is structured.

Finding the Mean Fare

To find the mean fare, we can use the mean() function provided by pandas:

# Calculate the mean fare
avrg_Fare = df['Fare'].mean()
print(avrg_Fare)

Filtering the Data: Above Average Fare

Now that we have the mean fare, let’s filter the data to include only rows where the fare is above the average.

# Filter the data for fares above the average
df_filtered = df.loc[df['Fare'] > avrg_Fare]

This filtered dataset will contain only the passengers who paid a fare above the average.

Grouping by Pclass and Finding Minimum Age

To find the minimum age for each class of Pclass, we can use the groupby() function provided by pandas:

# Group the data by Pclass and find the minimum age for each group
min_age_by_Pclass = df_filtered.groupby('Pclass')['Age'].min().reset_index()

This will give us a new DataFrame with the minimum age for each class of Pclass.

Using Pivot Table to Find Minimum Age

Another way to achieve this is by using the pivot_table() function:

# Create a pivot table to find the minimum age for each Pclass group
pvt_min_age = df_filtered.pivot_table(index='Pclass', aggfunc={'Age':np.min}).reset_index()

This will also give us a new DataFrame with the minimum age for each class of Pclass.

Visualizing the Results

To visualize the results, we can use matplotlib to create a bar chart:

import matplotlib.pyplot as plt

# Plot a bar chart to compare the minimum ages for each Pclass group
plt.figure(figsize=(10,6))
plt.bar(pvt_min_age['Pclass'], pvt_min_age['Age'])
plt.xlabel('Pclass')
plt.ylabel('Minimum Age')
plt.title('Minimum Age by Class')
plt.show()

This will give us a visual representation of the minimum age for each class of Pclass.

Conclusion

In this article, we’ve explored how to find the minimum value of a column (‘Age’) for each group (‘Pclass’) in the Titanic dataset, given that the fare paid by passengers is above the average. We used pandas to manipulate and analyze the data, including filtering, grouping, and using pivot tables.

We also touched on the importance of exploring and visualizing the data to gain insights into its distribution and patterns. By following these steps, you can perform similar analyses on your own datasets using pandas in Python.

Last modified on 2024-06-20