Libraries

NumPy

NumPy is the fundamental package for scientific computing in Python. It provides the ndarray object for efficient array operations.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr.shape)       # (5,)
print(arr.reshape(5, 1))

zeros = np.zeros((3, 4))
ones = np.ones((2, 3))
range_arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]

matrix = np.array([[1, 2], [3, 4]])
print(matrix + 10)          # Broadcasting
print(matrix @ matrix.T)    # Matrix multiplication

Pandas

Pandas provides Series and DataFrame for data manipulation and analysis.

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Salary": [50000, 60000, 70000]
}

df = pd.DataFrame(data)
print(df.head())
print(df.describe())

grouped = df.groupby("Age").mean()
merged = pd.merge(df, other_df, on="id")

df.to_csv("output.csv", index=False)
read_df = pd.read_csv("data.csv")

SciPy

SciPy builds on NumPy and provides modules for optimization, statistics, integration, and signal processing.

from scipy.optimize import minimize
from scipy import stats
from scipy.integrate import quad

# Optimization
result = minimize(lambda x: x**2 + 10, x0=2)
print(result.x)

# Statistics
data = [1, 2, 3, 4, 5]
mean = stats.tmean(data)
z_scores = stats.zscore(data)

# Integration
area, error = quad(lambda x: x**2, 0, 1)
print(f"Area under x^2 from 0 to 1: {area}")

Matplotlib

Matplotlib is the standard plotting library in Python.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y, marker="o", linestyle="--", color="b")
plt.title("Simple Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.grid(True)
plt.show()

# Bar chart and histogram
categories = ["A", "B", "C"]
values = [3, 7, 5]
plt.bar(categories, values)

data = [1, 2, 2, 3, 3, 3, 4, 4, 5]
plt.hist(data, bins=5)

# Subplots
fig, axes = plt.subplots(2, 2)
axes[0, 0].plot(x, y)
axes[0, 1].bar(categories, values)
plt.tight_layout()

Django Overview

Django is a high-level web framework following the MTV (Model-Template-View) pattern. It includes an ORM, admin interface, URL routing, and templating.

# models.py
from django.db import models

class BlogPost(models.Model):
    title = models.CharField(max_length=200)
    content = models.TextField()
    pub_date = models.DateTimeField(auto_now_add=True)

    def __str__(self):
        return self.title

# views.py
from django.shortcuts import render
from .models import BlogPost

def home(request):
    posts = BlogPost.objects.all()
    return render(request, "home.html", {"posts": posts})

# urls.py
from django.urls import path
from . import views

urlpatterns = [
    path("", views.home, name="home"),
]

OpenCV Basics

import cv2

img = cv2.imread("image.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 100, 200)

cv2.imshow("Original", img)
cv2.imshow("Edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()