Daily Dose of Data Science | Avi Chawla | Substack·1yPandas Mind Map
A detailed mind map of various Pandas methods categorized by their operation types, including I/O methods, DataFrame creation, statistical information, renaming, plotting, time-series, grouping, pivot, and categorical data methods. Additional ML resources and techniques are also provided for developing industry-relevant skills.
Tech With Tim·1yHow To Automate Your Finances with Python - Full Tutorial (Pandas, Streamlit, Plotly & More)
The post provides a step-by-step tutorial for building a personal finance automation tool using Python with libraries such as Pandas, Streamlit, and Plotly. The tool can upload bank statements in CSV format, categorize transactions, and summarize expenses using visualizations. It also explains how to convert bank statements into a format conducive to analysis and the importance of utilizing structured, project-based learning resources like Data Camp for Python and finance fundamentals.
Towards Data Science·48wBuilding A Modern Dashboard with Python and Taipy
Taipy is a Python web framework designed for data scientists and engineers to build production-ready dashboards without web development expertise. The tutorial demonstrates creating an interactive sales dashboard with filtering capabilities, key metrics display, multiple chart types, and raw data tables using 100,000 synthetic sales records from a CSV file. Taipy excels over Streamlit and Gradio when building complex, high-performance, enterprise-grade applications that require scalability and maintainability.
freeCodeCamp·46wHow to Transform JSON Data to Match Any Schema
Learn how to transform JSON data to match specific schemas using two approaches: pure Python and pandas. The tutorial covers loading JSON files, defining target schemas, cleaning and renaming fields, and validating the output. It demonstrates transforming customer records by removing unwanted fields and renaming others, while comparing performance between pure Python (faster for simple tasks) and pandas (better for complex datasets with built-in data cleaning methods).
Towards Data Science·44wI Analysed 25,000 Hotel Names and Found Four Surprising Truths
A data scientist analyzed 25,000 hotel names worldwide using the Hotel Data API to uncover why hotels are named after cities they're not located in. The study revealed that Paris is the most borrowed city name (1,100+ hotels), followed by Vienna and Rome. Three main reasons emerged: proximity for search visibility, branding to evoke luxury and sophistication, and historical tradition dating back to 18th-century aristocratic travel patterns. The analysis used Python, pandas, and geographic distance calculations to map naming patterns across countries.
Towards Data Science·51wBuilding a Modern Dashboard with Python and Gradio
A comprehensive guide to building an interactive sales performance dashboard using Gradio, a Python library for creating web applications. The tutorial covers setting up Gradio, processing CSV data with Pandas, implementing filtering capabilities, generating visualizations with Matplotlib, and creating a responsive interface with key metrics, charts, and data tables. The dashboard allows users to filter by date ranges and product categories while displaying revenue trends, top products, and raw data dynamically.
Daily Dose of Data Science | Avi Chawla | Substack·1yFireDucks vs. Pandas vs. DuckDB vs. Polars
FireDucks is an optimized alternative to Pandas with the same API, requiring just an import replacement to use. It demonstrates a significant speed boost for big data operations, achieving an average speed-up of 125x over Pandas. FireDucks' lazy execution builds and optimizes a logical execution plan, unlike Pandas' immediate execution. It can be used with IPython, Jupyter Notebooks, or within existing Pandas pipelines by replacing import statements. Detailed benchmarks and usage examples are provided, showing substantial performance improvements in practical scenarios.
Tech With Tim·42wLearn Pandas in 30 Minutes - Python Pandas Tutorial
A comprehensive beginner tutorial covering pandas fundamentals including dataframe creation, data loading from CSV files, basic operations like head/tail/info, column and row indexing with iloc/loc, filtering data with conditions, updating and deleting entries, data cleaning methods, and basic analysis functions like groupby and value_counts. The tutorial demonstrates both regular Python files and Jupyter notebooks for data manipulation workflows.
JetBrains·1yData Cleaning in Data Science
Data cleaning is essential for transforming real-world, messy datasets into reliable sources for analysis or machine learning. This involves removing duplicates, dealing with implausible values, addressing formatting issues, outliers, and missing values. Proper data cleaning ensures that conclusions drawn from the data can be generalized to a defined population. Best practices include defining your population boundaries, ensuring reproducibility, and keeping methods well-documented.
Machine Learning Mastery·45w7 Pandas Tricks That Cut Your Data Prep Time in Half
Seven practical pandas techniques to accelerate data preparation workflows: chaining transformations with assign(), filling missing values using dictionaries in fillna(), flattening list columns with explode(), readable filtering with query(), named aggregations with groupby().agg(), date parsing with pd.to_datetime(), and building modular workflows with pipe(). These methods help reduce boilerplate code, improve readability, and streamline the data cleaning process.
Learn Python·47w🏋️ How I Built a Gym & Diet Progress Tracker Using Python
A developer built a personal fitness tracking application using Python to monitor workouts, diet, and progress without relying on commercial apps. The project uses pandas for data management, matplotlib/seaborn for visualization, CSV files for storage, and optionally tkinter for GUI. It tracks daily workouts, nutrition data, body weight, and generates visual progress charts. Key challenges included creating flexible input formats, preventing data overwrites, and clear long-term progress visualization.
Machine Learning News·1yComplete Guide: Working with CSV/Excel Files and EDA in Python
This tutorial provides a comprehensive guide to working with CSV/Excel files and performing exploratory data analysis (EDA) using Python. It covers importing, cleaning, and preprocessing data, exploring data through statistics and visualization, and deriving insights from business data using libraries such as pandas, NumPy, matplotlib, and seaborn. The guide uses a realistic e-commerce dataset to demonstrate the entire workflow, including merging datasets and handling data quality issues.