Automating E-commerce Data Extraction with Python API

E-commerce Data Analysis using Python I worked on a small data analysis project to understand how e-commerce data can be used to derive business insights. 📊 What the dataset includes: Orders and customers Product categories Regions Payment methods Order status (Completed / Cancelled / Returned) What I analyzed: Revenue distribution across categories Monthly sales trends Top customers based on spend Cancellation rate Region-wise performance 💡 Some observations: A few categories contribute a major portion of revenue Sales patterns vary across time periods Cancellation rates are not uniform across regions ⚙️ Tools used: Python (Pandas) Jupyter Notebook Project link: https://lnkd.in/gKUYb88x #DataAnalytics #Python #Pandas #DataProjects #Ecommerce #DataAnalyst

This was my first Data Analysis project with Python. 🐍 Goal: clean and prepare customer data from a retail store for business analysis. Tools I used: → String manipulation: strip(), replace(), split() → Data type conversion → Error handling with try/except → List sorting and total spend calculation → Automated report generation with f-strings It looks basic. And it is. But this is exactly what happens before any real analysis: messy data that needs to be cleaned up first. I'll be uploading it to GitHub soon. This is the first of several projects I'll be sharing here as I move forward in my learning journey. Have you worked with data cleaning before? What was the hardest part at the beginning? #Python #DataAnalysis #DataCleaning #Bootcamp #LearningInPublic

I recently worked on an e-commerce dataset to understand what actually drives customer dissatisfaction. Using Python (Pandas, NumPy, Matplotlib, Seaborn), I explored the data and uncovered some interesting insights: I found that delayed deliveries reduce customer ratings significantly. I observed that customers are much more likely to give negative reviews when orders are delayed. One key insight was that a small number of extreme delays (30+ days) cause a huge drop in satisfaction. Surprisingly, I also found that in some product categories, customers were unhappy even when delivery was early. → which indicates product quality issues, not delivery problems. This project helped me understand how important it is to go beyond basic analysis and actually identify the root cause of problems. Still learning and improving — would love to hear your feedback! #DataAnalytics #DataAnalyst #Python #Pandas #DataScience #BusinessAnalytics #PowerBI #DataVisualization #AnalyticsProject #PortfolioProject #EcommerceAnalytics #CustomerExperience

🚀 Excited to share my latest project: Retail Sales Analysis using Python In this project, I worked on a real-world Superstore dataset to analyze sales performance and generate insights. 🔹 Tools & Libraries: Python (pandas, matplotlib, seaborn) 📊 Key Analysis: Sales and profit by category Top-performing states Data cleaning and EDA 📈 Key Insights: Technology category generated the highest sales California is the top-performing state Profit varies significantly across categories 🔗 GitHub Repository: https://lnkd.in/gQijwCnG #DataAnalytics #Python #DataAnalysis #SQL #Portfolio #Learning

Built a forecasting and BI dashboard for e-commerce operations. It combines sales forecasting, cash flow projections, ROAS tracking, and marketplace data into one decision-support tool. Stack: Python, Streamlit, Pandas, SQLAlchemy, Prophet, Plotly GitHub: https://lnkd.in/gRp6MM-a

🧠 Python Concept: setdefault() in dictionary Add default values smartly 😎 ❌ Traditional Way data = {} key = "fruits" if key not in data: data[key] = [] data[key].append("apple") print(data) ❌ Problem 👉 Extra condition 👉 More lines ✅ Pythonic Way data = {} data.setdefault("fruits", []).append("apple") print(data) 🧒 Simple Explanation Think of setdefault() like a smart helper 🤖 ➡️ If key exists → use it ➡️ If not → create with default value 💡 Why This Matters ✔ Cleaner code ✔ Avoid key checking ✔ Useful in grouping data ✔ Common in real-world apps ⚡ Bonus Example data = {} items = [("fruit", "apple"), ("fruit", "banana")] for key, value in items: data.setdefault(key, []).append(value) print(data) 👉 Output: {'fruit': ['apple', 'banana']} 🐍 Don’t check keys manually 🐍 Let Python handle it smartly #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

🚀 Update on my Walmart Sales Analysis project! I had earlier built this using SQL + Python to analyze retail data. Now I’ve taken it a step ahead by turning it into an interactive Streamlit dashboard. 🔗 Live App: https://lnkd.in/dFMakvAn 💡 What you can do now: • View all business questions (SQL queries) in one place • See the results with proper tables + visual charts for each query • Use filters (like branch) to explore different insights • Even run your own queries using the built-in SQL explorer 📊 This helped me understand how to move from just analysis → building something people can actually use and explore. Tech Stack: SQL | Python | Streamlit | Plotly #SQL #Python #Streamlit #DataAnalytics #Projects #Dashboard

Built an end-to-end Customer Behavior Analytics Dashboard using Python, MySQL, and Power BI. Cleaned and transformed raw data, performed EDA, executed SQL queries, and visualized key insights like revenue trends, customer segments, and purchase behavior. Github link: https://lnkd.in/eafrA__f #DataAnalytics #PowerBI #SQL #Python #DataVisualization

EDA Project: E-commerce Sales & Customer Behavior Analysis (Python) I recently worked on an e-commerce dataset to explore how customer behavior, sales patterns, and operational factors impact business performance. What I analyzed: Revenue trends across product categories Customer spending patterns by city Impact of discounts on order value Return behavior in relation to delivery time Key insights: A small set of product categories contributed a large portion of total revenue Longer delivery times were associated with higher return rates Discounts did not always lead to higher order values Customer spending varied significantly across cities Tech stack: Python (Pandas, Matplotlib) Data cleaning & exploratory data analysis EDA is not just about visualizing data — it’s about identifying patterns that influence real business decisions 🔗 GitHub Repository:https://lnkd.in/gyTb7Ye3 #Python #DataAnalytics #EDA  #DataScience #DataProjects  #Pandas #AnalyticsEngineering  #EcommerceAnalytics

import pandas as pd data= {     'Name' : ['dhananjay','preeti', 'shambhu'],     'age' : [50,40,15],     'DOB' : [10,30,24] } df=pd.DataFrame(data) This code snippet initializes a small dataset and displays its structural summary using the #Python #pandas library. Step-by-Step Code Explanation import pandas as pd: Imports the pandas library and assigns it the alias pd, which is the standard convention for accessing its functions. data = { ... }: Creates a Python #dictionary where keys represent column headers ('Name', 'age', 'DOB') and values are lists of data points associated with those headers. df = pd.DataFrame(data): Converts the dictionary into a DataFrame object—a two-dimensional, table-like structure—and assigns it to the variable df. df.info(): Executes a method that prints a concise technical summary of the DataFrame structure directly to the console. --------------------------------------- Understanding the Output Window When we run df.info(), the output provides a metadata report for our table: <class 'pandas.core.frame.DataFrame'>: Confirms that our variable df is indeed a Pandas DataFrame object. RangeIndex: Shows the index of the rows (0 to 2, indicating 3 total rows). Data columns: Lists the columns by name and displays: Non-Null Count: Indicates that all 3 rows have valid data (no missing or NaN values). Dtype: Shows the data type for each column; 'Name' will likely be object (text), while 'age' and 'DOB' will be int64 (integers). dtypes & memory usage: Summarizes the count of different data types used and estimates the amount of RAM the DataFrame is occupying. SkillCourse CoDing SeeKho