Manipulating Data

Let’s Do Digital Team

What is NumPy?

• NumPy is a Python library for working with numbers and arrays
• Arrays are like lists but faster and more powerful
• Great for mathematical and scientific calculations
• Core tool in data science and machine learning

Key Concepts in NumPy

• Array: A grid of values (1D, 2D, or more)
• Efficient for storing and working with lots of data
• NumPy makes math operations fast and easy
• Use NumPy for calculations across whole arrays at once

Common NumPy Tasks

• Create an array: np.array([1, 2, 3])
• Do math: np.add(arr1, arr2) or arr1 + arr2
• Reshape arrays: arr.reshape(2, 3) (change shape)
• Find max, min, sum: arr.max(), arr.min(), arr.sum()

Why Use NumPy?

• Very fast and efficient for working with numbers
• Easy to perform complex calculations
• Used in data analysis, machine learning, and more
• Essential for handling large datasets

What is Pandas?

• Pandas is a tool for working with data in Python
• It helps you organise and analyse data in tables
• Great for working with spreadsheets or databases
• Widely used in data science

Key Concepts in Pandas

• DataFrame: A table of data (rows and columns)
• Series: A single column of data
• You can filter, sort, and change the data
• Easy to read from and write to files like CSVs

Common Pandas Tasks

• Load data from a file: pd.read_csv('file.csv')
• View data: df.head() (shows first few rows)
• Filter data: df[df['Age'] > 50]
• Save data: df.to_csv('new_file.csv')

Why Use Pandas?

• Easy to learn and very useful
• Works well with big datasets
• Helps you clean and analyse data
• A key tool for data analysis in Python