Python and Data Science

Data science encompasses the art of extracting knowledge and insights from data. Python’s versatility and extensive data science libraries make it a top choice for data exploration, data analysis, and machine learning.

NumPy, the fundamental library for numerical computing, provides a foundation for efficient array operations, enabling fast and flexible data manipulation.

# Example: Using NumPy for data manipulation
import numpy as np

# Create a NumPy array
data = np.array([1, 2, 3, 4, 5])

# Perform array operations
mean = np.mean(data)
median = np.median(data)
std_dev = np.std(data)

print(“Mean:”, mean)
print(“Median:”, median)
print(“Standard Deviation:”, std_dev)
Pandas, a powerful library built on top of NumPy, offers easy-to-use data structures and data analysis tools, making data manipulation a breeze.

# Example: Using pandas for data manipulation
import pandas as pd

# Create a pandas DataFrame
data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],
‘Age’: [25, 30, 28],
‘Country’: [‘USA’, ‘UK’, ‘Canada’]}

df = pd.DataFrame(data)

# Perform data analysis
average_age = df[‘Age’].mean()
print(“Average Age:”, average_age)

Python’s rich ecosystem of data science libraries, including matplotlib and seaborn, empower data analysts to create captivating visualizations and gain deeper insights into the data.

# Example: Data visualization with matplotlib
import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5] y = [10, 12, 8, 15, 11]

# Create a line plot
plt.plot(x, y, marker=’o’, linestyle=’-‘, color=’b’)
plt.xlabel(‘X-axis’)
plt.ylabel(‘Y-axis’)
plt.title(‘Sample Line Plot’)
plt.grid(True)
plt.show()