Neha Sanjay Deshmukh’s Post

🚦 First Quality Check: Dataset Sanity with Python Before diving into transformations or analytics, the first thing I do when I receive a dataset is a sanity check. 🔍 Is the dataset empty? 🧱 Does it have the expected structure? These quick validations can save hours of debugging and prevent downstream failures in ETL pipelines. Here’s how I use Python’s assert to automate this first checkpoint: import pandas as pd df = pd.read_csv("your_data.csv") # Sanity checks assert df.shape[0] > 0, "Dataset is empty!" expected_columns = ["id", "timestamp", "value"] assert list(df.columns) == expected_columns, "Unexpected columns in dataset!" ✅ Why it matters: Catches broken pipelines early Flags schema drift Builds confidence in automation This is the first post in my series: Python for Data Quality Tagline: Automate. Validate. Elevate. Stay tuned for more checks — from missing values to schema validation and real-time monitoring! #Python #DataEngineering #ETL #QualityChecks #AWS #DataValidation #LinkedInSeries #WomenInTech #DataQuality #Automation

Project: Shipment Tracking Analytics I recently built a Python-based analytics tool that processes shipment tracking data to uncover key delivery insights. The project converts raw JSON shipment data into structured CSV reports and calculates important logistics metrics such as: Total transit time Number of facilities visited Delivery attempts & first-attempt success rate Comparison between express and standard services It helps logistics teams measure delivery efficiency and improve operational performance. Tech Stack: Python, Pandas, NumPy, JSON, DateTime, CSV, GitHub GitHub Repository: https://lnkd.in/g8sr3fyq #Python #DataAnalytics #DataEngineering #Pandas #DataScience

Learn how to increase data processing speed by over 160x. Ibrahim Salami shows you how to replace slow Python loops with NumPy vectorization, using a real-world sensor data project.

In data engineering, one of the most important things is orchestrating data workflows i.e. ensuring tasks run automatically in the right order and at the right time. This is where Apache Airflow shines. At the heart of Airflow is something called a DAG, which stands for Directed Acyclic Graph. A DAG simply defines how workflow runs. It indicates: ✅ which tasks should execute ✅ in what sequence ✅ and how often. Each task in a DAG might represent something like: 🔹 Running a Python script 🔹 Moving data from one source to another 🔹 Transforming data with SQL or pandas Airflow makes it possible to define all of this in Python code, making your workflows automated, structured, and easy to monitor through its intuitive UI. Here is what makes DAGs powerful: ✅ They remove the chaos of manual runs ✅ They help you visualize task dependencies ✅ They ensure reliability with retries and scheduling ✅ They scale easily as workflows grow Every solid data pipeline starts with a well-structured DAG. It is the backbone of automation in Airflow. #DataEngineering #ApacheAirflow #Python #ETL #Automation #DataPipelines #WorkflowOrchestration #Data

Ever wondered why many analysts switch from Excel or Power BI to Python for advanced analytics? This article by Mr. Murtaza Ali breaks it down perfectly. 👇 There are certain limitations when it comes to data visualization and exploratory data analysis (EDA) in tools like Excel or Power BI. While they’re excellent for quick summaries and dashboards, handling large datasets or performing advanced analytics and forecasting often requires a more powerful toolset. That’s where Python truly stands out. With libraries such as pandas, matplotlib, and seaborn, it provides greater flexibility, scalability, and control for deeper insights. I completely agree with Mr. Murtaza Ali on his perspective about using Python — particularly the pandas visualization library — for effective and scalable visual analytics. The article below explains this further in detail. Kudos to you, Sir! 👏👏👏 #DataAnalytics #Python #EDA #DataVisualization #PowerBI #Excel

🐍 Exciting Python Libraries Transforming Data Analysis in 2025! As data professionals, staying updated with the latest tools is crucial. Here are some game-changing Python libraries that are revolutionizing how we analyze data: 📊 Polars - Lightning-fast DataFrames with impressive memory efficiency. Perfect for handling large datasets with significantly better performance than traditional tools. ⚡ Vaex - Process billions of rows with minimal memory footprint. Ideal for big data analytics without the overhead. 🎯 PyCaret - Automates machine learning workflows, making model development faster and more accessible. 🔍 Sweetviz - Generate comprehensive data visualization reports in just a few lines of code. Great for quick EDA! 🚀 PyArrow - Apache Arrow's Python implementation offering high-performance data interchange and in-memory analytics. 💡 Streamlit - Build interactive data apps effortlessly, turning analysis into shareable web applications. These libraries complement the classics (Pandas, NumPy, Scikit-learn) and open new possibilities for data analysis efficiency. Which of these have you tried? Any other emerging libraries you'd recommend? #DataAnalysis #Python #DataScience #MachineLearning #Analytics #DataEngineering #PowerBI #BusinessIntelligence

See a direct comparison between standard Python loops and NumPy vectorization. Ibrahim Salami's new article on a million-record dataset shows NumPy completing the task in 1.49ms versus 244ms for the loop.

Let's talk about the unsung hero of Python for data analysis: the List. 📊 Before we get to complex Pandas DataFrames or sophisticated models, our data often starts its journey in a humble Python list. 🐍 What is a Python List? Think of it as a digital shopping list or a flexible container. It's an ordered collection of items, and it's mutable (meaning you can change it after it's created). It can hold anything—integers, strings, floats, and even other lists! my_data = [101, 'Sales', 4500.75, 'New York', True] ⚙️ Why Lists are Critical in Data Analysis Lists are the fundamental workhorse for data manipulation. Here’s where they shine: * Data Collection: When you fetch data from an API, query a database, or scrape a website, the results often land in a list first. It’s the initial "holding pen" for raw data. * Data Munging & Cleaning: This is where lists are invaluable. Before data is clean enough for a DataFrame, you use lists to: * Loop through thousands of records. * Filter out unwanted values (e.g., None or 0). * Transform data (e.g., convert strings to lowercase). * Remove duplicates. * Iteration: The for loop, a data analyst's best friend, works beautifully with lists. Need to apply a calculation to every single value? You'll be iterating over a list. * The Foundation for Pandas: That powerful Pandas Series or DataFrame you love? It's often built directly from a list or a list-of-lists. Understanding lists is key to understanding how DataFrames are structured. In short, mastering list operations (like comprehensions, .append(), and slicing) is a non-negotiable skill. It’s the difference between just using data tools and truly understanding how to manipulate data with precision. What's your favorite Python list trick or method you can't live without? Share in the comments! 👇 #Python #DataAnalysis #DataScience #Pandas #Programming #DataAnalytics #TechSkills #BusinessIntelligence

Data Structures Every Data Engineer Uses (Without Realizing It) Most data engineers don’t realize they use data structures every day. Here’s how they show up in our world:   • Hash maps become dictionary lookups in Python   • Queues become Kafka topics   • Graphs become Airflow DAGs   • Trees become hierarchical data models Understanding why these exist makes scaling and debugging so much easier. Which one do you think is hardest to explain to a non-engineer?

In today’s fast-paced data environment, optimizing Python scripts for faster data processing is more crucial than ever, especially as we approach 2025 workflows. As professionals handling vast datasets, we constantly seek ways to enhance efficiency and reduce processing time without compromising accuracy. Here are five practical ways to optimize your Python scripts for accelerated data processing: 1. **Use Efficient Data Structures:** Opt for libraries like NumPy and Pandas, which offer optimized data handling and vectorized operations over traditional lists and loops. 2. **Leverage Parallel Processing:** Utilize modules like multiprocessing or joblib to distribute workloads across multiple CPU cores, speeding up heavy computations. 3. **Profile Your Code:** Tools like cProfile or line_profiler help identify bottlenecks so you can focus optimization efforts where they matter most. 4. **Avoid Unnecessary Computations:** Cache results of expensive operations or skip redundant calculations by using memoization techniques. 5. **Optimize I/O Operations:** Reading and writing large files can slow down workflows; consider chunking large datasets and using efficient file formats like Parquet. As 2025 nears, with increasing data volumes and complexity, these strategies will become vital in maintaining competitive workflow speeds. What optimization techniques have transformed your data processing tasks? Let’s share insights and learn together. #PythonOptimization #DataProcessing #DataScience #WorkflowEfficiency #2025Trends #PythonProgramming #DataEngineering #TechInnovation