Scraping Books Dataset with Python & BeautifulSoup

Most data projects start with a dataset… Mine started with a website. 🌐 When you don’t have data, you create it. I built a web scraping pipeline using Python to extract book data directly from a website and transform raw HTML into a structured dataset ready for analysis. 📊 Tools used - 1. BeautifulSoup is a Python library that helps extract structured data from raw HTML. In this project, it was used to parse webpage content and retrieve key information such as book titles, prices, and ratings. 2. Matplotlib – For Data Visualisation. 3. Pandas - For Data Manipualtion and Cleaning 🔍 What I did: • Scraped data using BeautifulSoup • Extracted key features like Title, Price, and Rating • Cleaned messy real-world data (encoding issues like “”, currency symbols, text-to-numeric conversion) • Performed exploratory data analysis to uncover patterns 💡 Key takeaway: Data is not always readily available. The ability to collect, clean, and structure your dataset is a powerful skill for any data professional. 📎 I’ve attached my notebook below for a detailed walkthrough. #WebScraping #Python #DataScience #DataAnalysis #BeautifulSoup #Pandas #DataCleaning #EDA #Analytics

🐼 Pandas Preprocessing Cheat Sheet  A few years ago, I didn't know the difference between .isnull() and .isna() 😅 Now I'm building my own cheat sheets. I've been learning Data preprocessing with Python & Pandas — and honestly, the number of methods felt overwhelming at first. So I did what made sense: I started noting down every method I learned, with a simple example next to it. Over time, that list grew into a full reference sheet — 80+ methods covering: Here's a quick glance at the most important ones: 🔵 Missing Values → df.isnull().sum() — find nulls per column → df.fillna(df['col'].mean()) — fill with mean → df.dropna(subset=['col']) — drop specific nulls 🟢 Data Cleaning → df.drop_duplicates() — remove duplicate rows → df['col'].astype('category') — optimize memory → pd.to_numeric(df['col'], errors='coerce') — safe conversion 🟡 Exploration → df.describe() — instant stats summary → df['col'].value_counts() — frequency of each value → df.corr() — correlation between columns 🔴 Sorting & Filtering → df.sort_values('col', ascending=False) → df.nlargest(5, 'salary') — top 5 rows → df[df['age'] > 30] — filter by condition 🟣 GroupBy & Aggregation → df.groupby('dept')['salary'].mean() → df.pivot_table(values='salary', index='dept') ⚙️ Strings → df['col'].str.strip().str.lower() → df['col'].str.contains('keyword') I've compiled few with examples into a full cheat sheet Save this post for your next data interview! 🔖 #Python #Pandas #DataScience #MachineLearning #DataAnalysis #InterviewPrep #DataEngineering #100DaysOfCode #OpenToWork 👍

Are you struggling to understand APIs? Let me show you the simplest way to use one in Python 🌤️ After working with CSV data in my previous project, I wanted to try something different. A lot of beginners hear the word "API" and immediately think it's too advanced. It's not. Most people think APIs are difficult — until they actually build one. An API is simply a way for your Python script to ask the internet for data, and get a response back. That’s it. So I built a beginner-friendly project using a free weather API to fetch live temperature data. Here’s exactly what the script does: → Automatically calculates today’s date and the past 7 days → Sends a request to the weather API → Receives current temperature data → Organises it into a clean pandas DataFrame → Plots max and min temperature using matplotlib → Saves the data as a CSV file Libraries used and why 👇 🔹 requests — fetches data from the API 🔹 pandas — structures the data into a readable format 🔹 matplotlib — visualises the data 🔹 os — handles file creation and saving Want to try it yourself? Just change the latitude and longitude to get weather data for your own city. Like my last project, the full code is available on my GitHub — link is in my profile featured section👆 Drop a 🌤️ if you found this helpful! Follow me if you’re learning AI/ML and want simple real-world projects like this. #Python #API #LearningInPublic #DataScience #Matplotlib #Pandas #100DaysOfCode #LearnPython #PythonProjects #BeginnerPython #PythonTips

Filtering rows in pandas is one of the first skills every data scientist needs to master and there are more ways to do it than most beginners realize. Boolean indexing is the foundation. isin() replaces messy OR chains. between() cleans up range filters. loc[] handles filtering and column selection together. query() makes complex conditions readable at a glance. Each method has its place. Knowing which one to reach for in which situation is what makes your data analysis code clean, efficient, and easy to maintain. Read the full post here: https://lnkd.in/eRnVAxN4 #Python #Pandas #DataScience #DataAnalysis #DataEngineering #Analytics

Day 4 — Python for Analytics When I started, I wasted weeks learning things I never used. Here are the 5 libraries that actually move the needle: 🐼 1. Pandas — The backbone of data analysis import pandas as pd df = pd.read_csv("sales_data.csv") top_product = (df.groupby("product")["revenue"]          .sum()          .sort_values(ascending=False)          .head(3)) print(top_product) If you learn nothing else — learn Pandas. 📊 2. Matplotlib / Seaborn — Turn numbers into stories Quick, beautiful charts with minimal code import seaborn as sns import matplotlib.pyplot as plt sns.lineplot(data=df, x="date", y="revenue") plt.title("Monthly Revenue Trend") plt.show() 🔢 3. NumPy — The engine under the hood Fast calculations on large datasets import numpy as np aov = np.mean(df["order_value"]) print(f"Average Order Value: ${aov:.2f}") 🤖 4. LangChain — Bridge between Python and LLMs Build GenAI workflows without starting from scratch from langchain_community.llms import OpenAI llm = OpenAI() response = llm("Summarize this sales report: ...") print(response) 📓 5. Jupyter Notebooks — Code + Story in one place Not just a coding tool — a communication format. Code → Output → Explanation → Chart All in one shareable document. Perfect for stakeholder walkthroughs. My honest learning path: Week 1 → Master Pandas Week 2 → Add Seaborn + Matplotlib Week 3 → Learn NumPy basics Week 4 → Explore LangChain Start with one. Build something real. Then add the next. #Python #Analytics #DataScience #Pandas #GenAI #30DayChallenge

I didn't become a better Data Analyst by learning more theory. I became better by learning the right Python libraries. 🐍 Here are the ones that changed how I work 👇 ● NumPy — The foundation of everything. Fast numerical computations, arrays, and math operations. If data science is a building, NumPy is the concrete. ● Pandas — Your best friend for data cleaning and analysis. Load, filter, group, and transform data in just a few lines. I use this every single day. ● Matplotlib & Seaborn — Because numbers alone don't tell stories. These libraries turn your data into visuals that stakeholders actually understand. ● Scikit-learn — Machine learning made approachable. From regression to clustering, it's the go-to library for building and evaluating models. ● Plotly — When your charts need to be interactive. Dashboards, hover effects, drill-downs — this is where analysis meets presentation. You don't need to master all of them at once. Pick one. Go deep. Build something with it. Then move to the next. The best Python skill is the one you actually use. 🎯 ♻️ Repost if this helped someone on your network! 💬 Which Python library do you use the most? Drop it below 👇 #Python #DataAnalytics #DataScience #Pandas #NumPy #LearningInPublic #DataAnalyst

Something I keep noticing in this field. No matter how much someone learns… it never feels enough. You finish SQL — feels like you should know Python. You learn Python — now statistics feels weak. Then comes Power BI. Then “maybe I should learn ML also.” It just keeps going. And after a point, it stops feeling like growth. It starts feeling like you’re always behind. I’ve seen people who actually know quite a lot… still hesitate to apply, still feel “not ready”, still think they need one more thing. At some point, it’s not about skills anymore. It’s just noise. Too many things to learn. Too many people to compare with. Too many “roadmaps” telling you what you’re missing. What helped a lot of people I’ve worked with is something very simple: Just picking a line and sticking to it for some time. Not the perfect roadmap. Just a clear direction. Because if everything feels important, nothing really is. And that’s usually where the confusion starts.

For my Day 16 I went back to the basics, but this time I went deeper. This is the start of my 5-day revision phase before I move into pandas and data analysis libraries. I wanted to make sure my foundation is not just familiar but actually solid. Here is what I revised and the deeper things I picked up: Variables & Data Types I already knew int, float, str, bool and list. But going deeper I revised NoneType, multiple assignment in one line, and the fact that Python is case sensitive with variable names. name, Name and NAME are three completely different variables! Casting I went beyond the basics here. I learned that int(3.99) gives you 3 not 4 — it drops the decimal, it does not round. I also learned that you cannot cast "3.14" directly to int, you have to go through float first: int(float("3.14")). And I was reminded that Python sometimes casts automatically — when you add an int and a float, the result is always a float. Strings & String Methods This one went deep. I revised slicing properly , including negative indexing, reversing a string with [::-1], and skipping characters with [::2]. I also covered .title(), .count(), .find(), .startswith(), and .endswith(). One important thing I locked in: strings are immutable. You cannot change a character in place. You have to rebuild the string. Numbers Revised all arithmetic operators including // for floor division and % for remainder. I also went into the math module — math.sqrt(), math.ceil(), math.floor(), and math.pi. Built-in functions like abs(), round(), max(), min() and sum() were also covered. String Formatting This is where everything came together. I went deeper into f-string modifiers aligning text with :> :< :^, filling gaps with custom characters, formatting large numbers with :, and :_, and displaying percentages with sign using :+.1%. I even built a mini sales report using only f-strings and it looked clean and professional. Revision is humbling. You think you know something until you go deeper and realise there was always more to learn. 😄 4 more revision days to go. Then pandas. #Python #100DaysOfCode #LearningInPublic #W3schools #NeverStopLearning #PythonForDataAnalysis

We’ve all been taught that tools are everything in data analysis, Python, SQL, you name it. Everyone tells you what to learn because “you’ll need them”, but here’s what still baffles me: you only need a tool as much as it actually fits your case. 🧠 In my projects, I was supposed to use Python for one and SQL for the other. I did the opposite because that’s what made sense to me. Take the sudden sales drop on an e-commerce platform (engagement tanked overnight), for example. I checked correlations across different columns, figured out which metrics were driving it, formed a hypothesis and tested it with Python. Clean, targeted and ready. 📊 The revenue anomaly was the opposite, financial reports didn’t match reality. Four simple SQL queries with WHERE, GROUP BY and ORDER BY uncovered the issue, so it turned out regional leverage was skewing everything. No complications, just clarity. Why am I sharing this? I used to feel intimidated by all the “learn this, learn that” pressure. Truth is, thinking and understanding come first: your hypothesis, the dataset, the real problem. Tools are learnable. Throw syntax at it blindly without that foundation and you’ll derail yourself, waste time or make it worse. Your analytical mind matters more than showing off fancy queries you might not even need. 🎯 #DataAnalysis #DataAnalytics #AnalyticalMindset #DataDriven #SQLvsPython

I used to be really confused about NumPy and Pandas before/while learning them. They both seem similar at first. Here’s a simple way I understood them: 1. Numpy was built first (2005) to solve Python numerical problems. Python lists were slow for numerical work. And numpy made it faster and easier with C-based arrays. And when I learned about substitution, like you don't even have to use loops for those kinda tasks. 2. Pandas came later(2008) because Numpy was great with numbers, but real-world data is messy. So, to work with missing data and to work with other apps like Excel and SQL, it was created. The important part is that in most real projects, you don’t really choose one over the other; you use both together. Use NumPy when: 1. Working with pure numerical computations (linear algebra, mathematical operations) 2. Handling arrays, images, or signal data 3. You need performance and memory efficiency Use Pandas when: 1. Working with tabular or relational data (like Excel or SQL) 2. Dealing with missing or messy real-world data 3. Performing data cleaning, aggregation, or analysis 4. Working with time series data So in practice: NumPy handles the fast numerical backbone, and Pandas builds on top of it to make data handling more practical and readable. #pandas #numpy #NumpyVsPandas