This article shares ten practical Python plotting tricks—covering subplots, axis labels, titles, annotations, color hues, point sizes, threshold lines, multiple Y‑axes, overlapping plots, and bar‑plot ordering—to help data scientists create clear, explanatory visualizations.
This tutorial shows how to use Matplotlib's animation module to create a Game of Life visualisation in Python, covering the rules, grid setup, code implementation, and how to generate and display the animated plot for data‑science applications.
Python’s ecosystem—Jupyter notebooks, Pandas for data manipulation, scikit-learn for machine learning, and matplotlib/Basemap for powerful visualizations—offers a streamlined, scriptable environment that outperforms traditional Java or PHP workflows, enabling researchers to write, run, and document code seamlessly in a single web interface.
Learn how to install Jupyter via Anaconda or pip, create and manage notebooks, understand cells and kernels, write and run Python code, explore a Fortune 500 dataset with pandas, clean missing values, and visualize profit and revenue trends using matplotlib and seaborn—all illustrated with screenshots and code snippets.
This article demonstrates building a Python web scraper to collect JD.com product reviews, extract product IDs, store comments in MongoDB, clean color and size data, and visualize the most popular colors and sizes using matplotlib, providing a complete end‑to‑end data analysis pipeline.
This article introduces Python’s core data‑analysis stack—NumPy for fast multidimensional arrays, pandas for labeled DataFrames, and Matplotlib for interactive plotting—while showing how to set up a Jupyter/VS Code environment, perform basic indexing, slicing, and visualisation, and clean log files with pandas.
This tutorial walks through extracting Python job postings from Zhaopin, storing them in MongoDB, cleaning the data with pandas, visualizing national and city‑level salary distributions, and generating word clouds of required skills using matplotlib and wordcloud, providing a complete end‑to‑end data analysis pipeline.
This beginner-friendly tutorial walks you through setting up Matplotlib, installing the library, and creating a variety of basic visualizations—including line plots, scatter plots, pie charts, bar charts, and histograms—while also covering multiple figures, subplots, and customization options for each chart type.
This article uses a Kaggle dataset of roughly 40,000 matches from 1872 to the present, applies pandas and matplotlib in Python to compute win‑team columns, compare the historical performance of the top five and top nine World Cup nations, explore the impact of friendly matches, and generate visualisations that help assess each team's championship likelihood.
This tutorial introduces Matplotlib, a powerful Python 2D plotting library, covering installation, basic usage, multiple figure handling, subplots, and common chart types such as line, scatter, pie, bar, and histogram with clear code examples and visual illustrations.
This tutorial demonstrates how to collect Python job postings from Lagou using Python's requests library, process the JSON response with pandas, and create insightful visualizations—including bar charts, word clouds, and geographic heatmaps—while handling anti‑scraping measures and data cleaning steps.
This guide walks you through extracting Python job listings from Lagou.com with Python's requests library, parsing the JSON response, storing the results in CSV, and visualizing key insights such as education requirements, work experience, salary distribution, word clouds, and geographic salary heatmaps using pandas, matplotlib, and pyecharts.
This guide walks you through importing stock data, cleaning column names, visualizing price and volume trends, creating candlestick (K‑line) charts, analyzing relative changes, exploring correlations, and implementing a simple moving‑average trading strategy using pandas, matplotlib, and numpy.
This article walks through Python’s mature scientific stack by loading a real openflights dataset, cleaning it with pandas, and visualizing it using a variety of libraries—including matplotlib, seaborn, bokeh, pygal, and folium—to illustrate each tool’s strengths and typical use cases.
This tutorial revisits a K‑Means clustering case on basketball player statistics, walks through the Python implementation using scikit‑learn, demonstrates Matplotlib visualizations, offers plotting optimizations, and addresses common Spyder IDE issues for data‑science workflows.
