Data Analysis Libraries in Python

1. Introduction to Pandas

Pandas is a popular data analysis library that provides high-level data structures and functions to make data manipulation easy and intuitive. The two primary data structures in Pandas are:

• Series: A one-dimensional labeled array capable of holding data of any type.
• DataFrame: A two-dimensional labeled data structure with columns, similar to a table in SQL or Excel.

1.1 Working with Series

The Series data structure is essentially a column, but it can hold data of any type. Here’s how to create a basic Series:

import pandas as pd
data = pd.Series([10, 20, 30, 40, 50])
print(data)

This code creates a Series object with five numbers. You can perform indexing, filtering, and other operations on this Series.

1.2 Working with DataFrames

DataFrames are the core data structure of Pandas, representing a table of data with rows and columns. Here’s an example of creating a DataFrame:

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)

This code creates a DataFrame with columns for names and ages. DataFrames support numerous operations, such as grouping, filtering, and merging.

2. Introduction to NumPy

NumPy (Numerical Python) is a library for numerical computing in Python. It provides the foundation for data manipulation in Python, enabling efficient array computations and various mathematical functions. The main data structure in NumPy is the ndarray (n-dimensional array).

2.1 Creating Arrays

NumPy arrays are similar to lists, but they allow for more efficient numerical operations. Here’s how to create an array:

import numpy as np
array = np.array([1, 2, 3, 4, 5])
print(array)

This code creates a one-dimensional NumPy array. NumPy arrays support broadcasting, element-wise operations, and much more.

2.2 Array Operations

NumPy arrays allow for mathematical operations to be performed across the entire array efficiently. Here’s an example of basic array operations:

import numpy as np
array = np.array([1, 2, 3, 4, 5])
print(array + 5)   # Adds 5 to each element
print(array * 2)   # Multiplies each element by 2

These operations are vectorized in NumPy, making them significantly faster than equivalent operations with lists.

3. Combining Pandas and NumPy

Pandas and NumPy are often used together, with Pandas providing the data handling functionality and NumPy enabling fast numerical computations.

import pandas as pd
import numpy as np

# Creating a DataFrame with NumPy arrays
df = pd.DataFrame({
    'A': np.random.rand(5),
    'B': np.random.rand(5)
})
print(df)

This code creates a DataFrame with two columns, A and B, populated with random numbers. NumPy functions can be directly applied to DataFrame columns for more complex computations.