Affichage des articles dont le libellé est Python. Afficher tous les articles
Affichage des articles dont le libellé est Python. Afficher tous les articles

Introduction to Matplotlib

Introduction to Matplotlib

Introduction to Matplotlib

Matplotlib is a Python library for plots. Developers use it to create charts for data analysis, machine learning, and scientific work. It gives full control over lines, labels, titles, and axes. It fits small and large projects.

Why use Matplotlib

  • It supports line charts, bar charts, scatter plots, histograms, and more.
  • It integrates with NumPy and pandas.
  • It works in Jupyter notebooks and Python scripts.
  • It offers stable output for research and industry tasks.

Basic installation

pip install matplotlib

Create your first plot

This example shows a simple line plot.

import matplotlib.pyplot as plt

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

plt.plot(x, y)
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()

This code creates a window with a chart. It links each x point with each y point. You can adjust labels and style as needed.

Common plot types

  • Line plots for trends.
  • Scatter plots for relations between two variables.
  • Bar charts for comparisons.
  • Histograms for distribution checks.

Tips for clean plots

  • Use short titles.
  • Label axes with clear words.
  • Keep grid lines simple.
  • Focus on readable font sizes.

Conclusion

Matplotlib helps build clear charts with few lines of code. It supports data analysis tasks for students and professionals. It works well in AI projects because it gives quick feedback on data patterns.

Introduction dialecte darija

Matplotlib hiya library f Python li katmken men rasm charts. Tkhdem mzyan f data analysis, machine learning, w t9dir tsayeb biha charts b kontrol kammel. Katshel f notebooks w scripts.

Ash nstafdo men Matplotlib

  • Katsupport line charts, bar charts, scatter plots, w histograms.
  • Katkhddem mzyan m3a NumPy w pandas.
  • Katrun b sor3a f Jupyter.
  • Katsayeb charts stables l research w l projects.

Tansib

pip install matplotlib

Awwel plot

import matplotlib.pyplot as plt

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

plt.plot(x, y)
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()

Had code kayrsam line plot basit. Katban window fiha chart. Kitrbat points dyal x m3a y. T9dar tbdel labels w style.

Ash men types

  • Line plot bach nshawfo trends.
  • Scatter plot bach nshawfo l relation bin variableat.
  • Bar chart bach nqarnu values.
  • Histogram bach nfhmou distribution.

Tips bach yban chart n9i

  • Khdem b title s7el.
  • Smi l axes b klam wadi.
  • Khelli grid s7el.
  • Khdem b font size maqbul.

Khitam

Matplotlib kay3awn bzaaf f rasm charts b sr3a. Kayfit l talaba, beginners, w professionals f AI w data. Kayb9a tool mohim f kol project kaytsana visualization.

Tutorial in Python Basics

Tutorial in Python Basics

Python Basics for AI Beginners

Python is the main language for AI and data. If you want to learn machine learning or deep learning, you need strong Python basics. This guide stays simple and clear. You can follow it even if you start from zero.

1. What Is Python and Why Do We Use It in AI

  • Python has clean and readable syntax.
  • Big AI ecosystem. NumPy, Pandas, Matplotlib, PyTorch, TensorFlow.
  • Large community and many examples.
  • Works on Windows, Linux, and macOS.

Python lets you focus on ideas instead of complex syntax. That helps a lot when you start with AI concepts.

2. Installing Python

Steps

  • Go to the official Python website.
  • Download the latest stable version of Python 3.
  • Install and check the option to add Python to PATH.
  • Open a terminal or command prompt.
  • Run this command.
python --version

If you see a version number, Python is ready.

3. Your First Python Program

Open a file called hello.py and write.

print("Hello AI world")

Run it.

python hello.py

You see the message in your terminal. This is your first step toward AI coding.

4. Python Syntax Basics

4.1 Indentation

Python uses spaces to mark blocks. No braces. Indentation is important.

if 5 > 3:
    print("True")

Use four spaces per level. Keep the style consistent.

4.2 Comments

Use comments to explain code.

# This is a comment
print("Hello")  # Inline comment

5. Variables and Data Types

5.1 Creating Variables

x = 10
name = "Sara"
pi = 3.14
is_ai_fun = True

Python detects the type from the value.

5.2 Main Data Types

  • int. whole numbers.
  • float. decimal numbers.
  • str. text strings.
  • bool. True or False.
  • list. ordered collection.
  • tuple. fixed ordered collection.
  • dict. key value pairs.

5.3 Lists

numbers = [1, 2, 3, 4]
print(numbers[0])      # First element
numbers.append(5)      # Add element

5.4 Dictionaries

student = {
    "name": "Sara",
    "age": 21,
    "field": "AI"
}

print(student["name"])

6. Operators

6.1 Arithmetic Operators

a = 10
b = 3

print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a // b)   # Integer division
print(a % b)    # Remainder
print(a ** b)   # Power

6.2 Comparison Operators

x = 5
print(x == 5)
print(x != 5)
print(x > 3)
print(x < 10)

6.3 Logical Operators

age = 20
is_student = True

print(age > 18 and is_student)
print(age < 18 or is_student)
print(not is_student)

7. Control Flow

7.1 if, elif, else

score = 82

if score >= 90:
    print("Grade A")
elif score >= 80:
    print("Grade B")
else:
    print("Grade C or lower")

7.2 for Loop

for i in range(5):
    print(i)

Loop through a list.

names = ["Ali", "Sara", "Amine"]

for n in names:
    print(n)

7.3 while Loop

count = 0

while count < 3:
    print("Count:", count)
    count += 1

8. Functions

8.1 Defining Functions

def greet(name):
    print("Hello", name)

greet("Sara")

8.2 Functions With Return

def add(a, b):
    return a + b

result = add(3, 4)
print(result)

Functions help you reuse code and keep projects clean.

9. Modules and Packages

9.1 Importing Modules

import math

print(math.sqrt(16))

9.2 From Import

from math import sqrt

print(sqrt(25))

Modules hold functions and classes. You import them instead of writing everything in one file.

10. Working With Files

# Writing
with open("data.txt", "w") as f:
    f.write("AI is fun")

# Reading
with open("data.txt", "r") as f:
    content = f.read()
    print(content)

File handling is important for data projects. You load datasets and store results.

11. Virtual Environments

Virtual environments keep project packages isolated.

Steps

python -m venv venv

Activate.

  • Windows.
venv\Scripts\activate
  • Linux or macOS.
source venv/bin/activate

After that install packages.

pip install numpy pandas matplotlib

12. First AI Oriented Libraries

12.1 NumPy

NumPy handles arrays and numeric operations.

import numpy as np

arr = np.array([1, 2, 3])
print(arr * 2)

12.2 Pandas

Pandas handles tables and structured data.

import pandas as pd

data = {
    "name": ["Sara", "Ali"],
    "score": [90, 85]
}

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

12.3 Matplotlib

Matplotlib draws charts.

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [2, 4, 6]

plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("Simple line")
plt.show()

These three libraries form a strong base for AI and data science.

13. Mini Projects for Python Basics

Project 1. Simple Calculator

  • Ask the user for two numbers.
  • Ask for an operation. plus, minus, multiply, divide.
  • Use if statements.
  • Print the result.

Project 2. Student Grades Report

  • Store students in a list of dictionaries.
  • Each dictionary holds name and grade.
  • Loop over the list and print a report.
  • Find the highest grade.

Project 3. Simple Data Summary With Pandas

  • Create a CSV file with product, price, and quantity.
  • Load it with Pandas.
  • Compute total revenue.
  • Show basic statistics.

These projects connect Python basics with small data tasks. That prepares you for machine learning projects.

14. Typical Errors and How to Avoid Them

  • IndentationError. fix spaces and keep alignment.
  • NameError. check spelling of variable names.
  • TypeError. check types before operations.
  • ModuleNotFoundError. install packages with pip and check environment.

Read error messages. They show the line and the type of error. This skill is important for AI work.

15. Next Steps After Python Basics

  • Learn more about NumPy and Pandas.
  • Study data visualization.
  • Start with machine learning. scikit learn.
  • Move to deep learning. PyTorch or TensorFlow.

Python basics open the door. AI libraries build on these concepts.

Python Basics in Moroccan Darija

Db l AI kaybda m3a Python. Ila fhamti Python basics, t9der tdkhl l machine learning w deep learning bla t3qid.

1. 3lach Python

  • Syntax sahl.
  • Libraries kbar. NumPy, Pandas, Matplotlib, PyTorch, TensorFlow.
  • Community kbira w resources ktr.

2. Awel Code

print("Salam mn Python")

Run code w shof output f terminal. Hadi bidaya dyalek.

3. Variables

age = 22
name = "Yassine"
is_student = True

Python kayfham type mn value.

4. Lists w Dicts

numbers = [1, 2, 3]
student = {"name": "Aya", "score": 95}

Lists bach t7t data mratba. Dicts bach t7t key value.

5. Loops w Conditions

if age >= 18:
    print("Baligh")
else:
    print("Sghir")

for n in numbers:
    print(n)

6. Functions

def greet(name):
    print("Salam", name)

greet("Aya")

Functions kat7afdk code w kat3awnk f projects kbira.

7. Libraries dial Data

import numpy as np
import pandas as pd

NumPy l arrays. Pandas l tables. Hadchi howa base dial AI.

Conclusion

Python basics form the first step toward AI. Learn syntax, variables, data types, loops, and functions. Use NumPy, Pandas, and Matplotlib for data. Build small projects. With these tools, you can start machine learning and deep learning with confidence.

Inscription à : Articles (Atom)