Python for Data Analytics - Quick Cheatsheet with Code Example 🚀

1️⃣ Data Manipulation with Pandas

import pandas as pd  
df = pd.read_csv("data.csv")  
df.to_excel("output.xlsx")  
df.head()  
df.info()  
df.describe()  
df[df["sales"] > 1000]  
df[["name", "price"]]  
df.fillna(0, inplace=True)  
df.dropna(inplace=True)  

2️⃣ Numerical Operations with NumPy

import numpy as np  
arr = np.array([1, 2, 3, 4])  
print(arr.shape)  
np.mean(arr)  
np.median(arr)  
np.std(arr)  

3️⃣ Data Visualization with Matplotlib & Seaborn

import matplotlib.pyplot as plt  
plt.plot([1, 2, 3, 4], [10, 20, 30, 40])  
plt.bar(["A", "B", "C"], [5, 15, 25])  
plt.show()  
import seaborn as sns  
sns.heatmap(df.corr(), annot=True)  
sns.boxplot(x="category", y="sales", data=df)  
plt.show()  

4️⃣ Exploratory Data Analysis (EDA)

df.isnull().sum()  
df.corr()  
sns.histplot(df["sales"], bins=30)  
sns.boxplot(y=df["price"])  

5️⃣ Working with Databases (SQL + Python)

import sqlite3  
conn = sqlite3.connect("database.db")  
df = pd.read_sql("SELECT * FROM sales", conn)  
conn.close()  
cursor = conn.cursor()  
cursor.execute("SELECT AVG(price) FROM products")  
result = cursor.fetchone()  
print(result)

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Python for Data Analysis: Must-Know Libraries 👇👇

Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.

🔥 Essential Python Libraries for Data Analysis:

✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.

📌 Example: Loading a CSV file and displaying the first 5 rows:

import pandas as pd df = pd.read_csv('data.csv') print(df.head()) 

✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.

📌 Example: Creating an array and performing basic operations:

import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average 

✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.

📌 Example: Creating a basic bar chart:

import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show() 

✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.

✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.

💡 Challenge for You!
Try writing a Python noscript that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization

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Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

🔰 Python Libraries And Frameworks

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Top 4 Python Projects for Beginners

1. To-Do List App: Create a simple to-do list application where users can add, edit, and delete tasks. This project will help you learn about basic data handling and user interface design.

2. Weather App: Build a weather application that allows users to enter a location and see the current weather conditions. This project will introduce you to working with APIs and handling JSON data.

3. Web Scraper: Develop a web scraper that extracts information from a website and saves it to a file or database. This project will teach you about web scraping techniques and data manipulation.

4. Quiz Game: Create a quiz game where users can answer multiple-choice questions and receive a score at the end. This project will help you practice working with functions, loops, and conditional statements in Python.

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