🌟 A new and comprehensive book "Mastering pandas"

👨🏻‍💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.

⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.

🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:

🔹 How to clean and prepare large amounts of data for analysis,

🔹 How to analyze real business data and draw conclusions,

🔹 How to automate repetitive tasks with a few lines of code,

🔹 And improve the speed and accuracy of your analyses significantly.

🌐 #DataScience #DataScience #Pandas #Python

Python Libraries You Should Know ✅

⦁ NumPy: Numerical Computing ⚙️
NumPy is the foundation for numerical operations in Python. It provides fast arrays and math functions.

Example:

import numpy as np

arr = np.array([1, 2, 3])
print(arr * 2)  # [2 4 6]

Challenge: Create a 3x3 matrix of random integers from 1–10.

matrix = np.random.randint(1, 11, size=(3, 3))
print(matrix)

⦁ Pandas: Data Analysis 🐼
Pandas makes it easy to work with tabular data using DataFrames.

Example:

import pandas as pd

data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)
print(df)

Challenge: Load a CSV file and show the top 5 rows.

df = pd.read_csv("data.csv")
print(df.head())

⦁ Matplotlib: Data Visualization 📊
Matplotlib helps you create charts and plots.

Example:

import matplotlib.pyplot as plt

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

plt.plot(x, y)
plt.title("Simple Line Plot")
plt.show()

Challenge: Plot a bar chart of fruit sales.

fruits = ["Apples", "Bananas", "Cherries"]
sales = [30, 45, 25]

plt.bar(fruits, sales)
plt.title("Fruit Sales")
plt.show()

⦁ Seaborn: Statistical Plots 🎨
Seaborn builds on Matplotlib with beautiful, high-level charts.

Example:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Challenge: Create a heatmap of correlation.

corr = tips.corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.show()

⦁ Requests: HTTP for Humans 🌐
Requests makes it easy to send HTTP requests.

Example:

import requests

response = requests.get("https://api.github.com")
print(response.status_code)
print(response.json())

Challenge: Fetch and print your IP address.

res = requests.get("https://api.ipify.org?format=json")
print(res.json()["ip"])

⦁ Beautiful Soup: Web Scraping 🍜
Beautiful Soup helps you extract data from HTML pages.

Example:

from bs4 import BeautifulSoup
import requests

url = "https://example.com"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")

print(soup.title.text)

Challenge: Extract all links from a webpage.

links = soup.find_all("a")
for link in links:
    print(link.get("href"))

Next Steps:
⦁ Combine these libraries for real-world projects
⦁ Try scraping data and analyzing it with Pandas
⦁ Visualize insights with Seaborn and Matplotlib

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✅ Top 5 Mistakes to Avoid When Learning Python ❌🐍

1️⃣ Skipping the Basics
Many learners rush to libraries like Pandas or Django. First, master Python syntax, data types, loops, functions, and OOP. It builds the foundation.

2️⃣ Ignoring Indentation Rules
Python uses indentation to define code blocks. One wrong space can break your code — always stay consistent (usually 4 spaces).

3️⃣ Not Practicing Enough
Watching tutorials alone won’t help. Code daily. Start with small scripts like a calculator, quiz app, or text-based game.

4️⃣ Avoiding Errors Instead of Learning from Them
Tracebacks look scary but are helpful. Read and understand error messages. They teach you more than error-free code.

5️⃣ Relying Too Much on Copy-Paste
Copying code without understanding kills learning. Try writing code from scratch and explain it to yourself line-by-line.

💬 Tap ❤️ for more!

The #Python library #PandasAI has been released for simplified data analysis using AI.

You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.

And here's the link 😍