checkmark

Python

Basics of Python

Installing Python Setting up a Development Environment Writing and Running Your First Python Program Python's Syntax and Indentation

Variables and Data Types

Variables in python Numeric Data Types (int, float) Text Data Type (str) Boolean Data Type (bool) Type Conversion (Casting) Dynamic Typing in Python

Control Flow

Conditional Statements (if, elif, else) Comparison Operators Logical Operators Loops (for and while) Loop Control Statements (break and continue)

Functions

Defining Functions Function Parameters and Arguments Return Statements Scope and Lifetime of Variables Lambda Functions Recursion

Data Structures

Lists and List Operations Dictionaries and Dictionary Operations Tuples Sets List Comprehensions Dictionary Comprehensions

File Handling

Opening and Closing Files Reading and Writing Text Files Handling Exceptions in File Operations

Modules and Libraries

Importing Modules Commonly Used Standard Libraries External Libraries: NumPy and Pandas Installing and Managing Libraries with pip

Error Handling

Exceptions in Python Handling Exceptions with try and except Custom Exceptions
menu Back to Edkool

External Libraries: NumPy and Pandas

External Libraries: NumPy and Pandas

Python's strength lies not only in its core language but also in its rich ecosystem of external libraries. Two of the most popular libraries for data manipulation and analysis are NumPy and Pandas. This section introduces these libraries and their key features.

NumPy

NumPy (Numerical Python) is a fundamental library for numerical computations in Python. It provides support for working with large, multi-dimensional arrays and matrices, as well as a collection of mathematical functions to operate on these arrays.

Key features of NumPy:

  1. Arrays: NumPy's ndarray is a powerful array object that allows you to store and manipulate data efficiently. These arrays are more memory-efficient and faster than traditional Python lists.

  2. Mathematical Functions: NumPy offers a wide range of mathematical functions, including arithmetic operations, statistical functions, and linear algebra operations.

  3. Broadcasting: NumPy allows for element-wise operations on arrays with different shapes. It simplifies operations on data of different dimensions.

  4. Random Number Generation: NumPy includes functions for generating random numbers, which is valuable for simulations and data analysis.

  5. Integration with C/C++: Many NumPy functions are implemented in C or C++, making them extremely fast and efficient.

Example of using NumPy:

import numpy as np

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

# Perform mathematical operations on the array
squared = np.square(arr)
mean = np.mean(arr)

Pandas

Pandas is a powerful library for data manipulation and analysis. It provides easy-to-use data structures for working with structured data, such as tables or spreadsheets. The primary data structures in Pandas are Series and DataFrame.

Key features of Pandas:

  1. DataFrame: The DataFrame is a two-dimensional table that can store and manipulate data. It is similar to a database or an Excel spreadsheet.

  2. Data Cleaning and Preparation: Pandas offers extensive tools for data cleaning, data transformation, and missing data handling.

  3. Data Indexing and Selection: You can select, filter, and manipulate data easily using various indexing methods.

  4. Grouping and Aggregation: Pandas enables you to group data by a specific column or criterion and apply aggregation functions, making it ideal for data analysis.

  5. Integration with NumPy: Pandas is built on top of NumPy, allowing seamless integration with NumPy arrays.

Example of using Pandas:

import pandas as pd

# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# Perform data manipulation and analysis
average_age = df['Age'].mean()

Both NumPy and Pandas are essential tools for data science and analysis in Python. They simplify data handling, provide efficient data structures, and offer a wide range of functions for data manipulation and analysis. Understanding these libraries is crucial for anyone working with data in Python.

Previous Commonly Used Standard Libraries
Next Installing and Managing Libraries with pip