This article walks through seven core 3D plotting methods using Python's Matplotlib library—covering line, scatter, surface, wireframe, contour, triangulated surface, and Möbius strip visualizations—complete with setup steps, code examples, and practical tips for multivariate data analysis.
The article analyzes Stack Overflow’s monthly new‑question counts since 2008, shows a peak in 2020 followed by a sharp decline, explains how AI tools like ChatGPT now provide instant answers, and reflects on the cultural shift from community‑driven Q&A to AI‑driven problem solving.
This article explains why matching Python and Pandas versions is essential, provides a compatibility table, shows how to install the correct Pandas release for a given Python version, demonstrates checking current versions, and offers commands for upgrading or downgrading Pandas while recommending virtual environments and official documentation.
This article introduces ten highly practical Python libraries for data analysis—from Pandas and NumPy for data manipulation to Matplotlib, Seaborn, Plotly, Bokeh for visualization, and Scikit‑learn, Prophet, Dask, and PySpark for machine learning and big‑data processing—each illustrated with concise code snippets.
This tutorial provides a detailed overview of Pandas, covering its core data structures, data import/export, selection, cleaning, aggregation, merging, and a practical sales analysis example, with complete code snippets for each operation.
This tutorial explains how to bridge Python with Prometheus using the prometheus_api_client library, fetch time‑series metrics, process the data with pandas, and create insightful visualizations with Plotly, illustrating a complete workflow from data collection to presentation.
This tutorial introduces ten highly practical Python libraries—Pandas, NumPy, Matplotlib, Seaborn, Plotly, Scikit-learn, Dask, PySpark, Bokeh, and Prophet—providing code examples that guide readers through data cleaning, visualization, and predictive modeling to accelerate their data‑analysis expertise.
The article explains why Python cannot fully replace Excel in finance, outlines spreadsheet pain points such as slowness, data‑size limits, and reproducibility issues, and introduces Python tools like Pandas, Mito, openpyxl, and Lux that can complement and extend Excel workflows.
This tutorial showcases a comprehensive set of pandas techniques—including reading data from the clipboard, random sampling, multi‑condition filtering, handling missing values, string splitting, list expansion, multi‑function aggregation, slicing, descriptive statistics, categorical conversion, DataFrame styling, and profiling—to efficiently explore and transform DataFrames in Python.
This article demonstrates how to replace common Excel operations such as VLOOKUP, data pivot tables, and charting with Python's pandas and plotly libraries, providing code examples, explanations of data import/export settings, and performance considerations for large‑scale data analysis.
This tutorial demonstrates how to use Python's pandas library to create, read, modify, and manage Excel files, covering basic DataFrame creation, sheet handling, column operations, filtering, merging, pivot tables, charting with matplotlib, performance tips, database integration, and advanced visualizations with seaborn and Plotly.
This article demonstrates how to use Python, pandas, and Plotly to download, clean, and visualize World Happiness Report data from 2015‑2019, revealing the strongest correlations between happiness scores and factors such as GDP per capita, healthy life expectancy, and social support.
This tutorial introduces Pandas, a powerful Python data‑analysis library, covering installation, importing, creating DataFrames from various sources, basic inspection, selection, filtering, sorting, grouping, handling missing values, and a practical stock‑price analysis example with code snippets.
This article demonstrates how to fetch historical lottery data for two games, apply a custom matching rule to identify purchase opportunities, analyze consecutive win patterns over 100 days, and export the aggregated results to an Excel file using Python's requests, xlwt, and basic data‑processing techniques.
This tutorial introduces Pandas fundamentals, demonstrating how to create Series and DataFrames, read and write CSV files, perform basic filtering and sorting, and includes practical code examples and a hands‑on exercise to display the first five rows of a CSV dataset.
This article introduces the ten most commonly used Pandas functions—read_csv, head, tail, info, describe, dropna, fillna, groupby, merge, and to_csv—explaining their purposes and providing clear Python code examples for each in practical data analysis tasks.
This article provides a comprehensive guide to Pandas indexing in Python, covering basic loc and iloc selection, Boolean indexing, setting and resetting indices, multi‑level indexing, index alignment, sorting, dropping, and advanced methods such as at, iat, and query, with complete code examples.
This article demonstrates how to keep a CSV file's header unchanged while sorting its rows in descending order based on a specific column using Python's pandas library, providing step‑by‑step code, explanations, and sample output to help readers automate data processing tasks.
This article walks through five concise Pandas techniques for creating a new column that holds the maximum value of two existing columns on each row, complete with code snippets and visual examples to help Python learners master DataFrame operations.
This article presents a curated set of Python automation scripts covering clipboard management, code quality analysis, file integrity checks, stock price forecasting, bulk image downloading, network port scanning, secure password storage, mass email dispatch, README file creation, and intelligent file organization, each accompanied by concise explanations and ready‑to‑run code examples.
This article provides a series of Python pandas code snippets that demonstrate how to read, filter, sort, merge, remove duplicates, create pivot tables, perform statistical analysis, generate charts, apply formatting, and add conditional formatting to Excel files.
This article demonstrates how to use Python libraries such as itchat, matplotlib, jieba, and SnowNLP to collect WeChat friend information and perform data analysis on gender, location, avatars, and signatures, presenting results with charts and word clouds, including sentiment and face detection.
This guide walks you through installing pandas and openpyxl, reading all sheets from two Excel workbooks, comparing matching sheets to identify differences, analyzing those differences, and saving the results to a new Excel file, with a complete code example provided.
This guide presents ten practical Python scripts that cover installing required libraries, reading and writing Excel files, merging sheets, cleaning and filtering data, creating pivot tables, generating charts, styling cells, and automatically emailing Excel reports, providing a complete workflow for Excel automation.
This article introduces the open‑source Python library TRACC, explains its accessibility metrics, shows how to install it, prepare destination and travel‑cost data, and provides step‑by‑step code examples for calculating potential, passive, and minimum‑travel‑cost accessibility in a city such as Boston.
This article presents a series of Python code examples that demonstrate how to generate statistical reports, pivot tables, various charts, heatmaps, PDF reports, and an interactive dashboard for sales data analysis using libraries such as pandas, matplotlib, seaborn, reportlab, and plotly/dash.
This article walks through a Python web‑scraping solution using requests and pandas to fetch Eastmoney stock data, explains the limitations of AI‑generated code, and provides a complete, runnable script with sample output.
This article presents a curated set of Python automation scripts covering clipboard management, code quality analysis, file integrity verification, stock price forecasting, bulk image downloading, network port scanning, encrypted password storage, bulk email sending, README.md generation, and folder organization, each with explanations and complete source code.
This article presents a curated set of Python automation scripts covering clipboard monitoring, code quality analysis, file integrity verification, stock price forecasting, bulk image downloading, network port scanning, encrypted password storage, mass email sending, README.md generation, and intelligent folder organization, each illustrated with complete source code.
This tutorial explains how to perform complex queries, logical filtering, type conversion, sorting, adding, modifying, advanced filtering, iteration, and function application on pandas DataFrames, providing numerous code examples for each operation to help Python users master data manipulation.
This article explains what a Pandas Series is, describes its parameters, shows how to create empty or populated series from arrays, dictionaries, or scalar values, demonstrates element access, lists key Series attributes, and introduces useful Series functions with code examples.
This tutorial demonstrates how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu SDK to collect WeChat friend information and perform data analysis on gender distribution, avatar characteristics, signature sentiment, and geographic location, presenting the results with charts and word clouds.
This article introduces four Python packages—Pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive tables within Jupyter notebooks, providing code examples, visual demonstrations, and guidance on when to choose each tool for data analysis and visualization.
This article walks through a Python community question about combining three aggregated columns into a single table using Pandas, explains why direct multi‑table merging isn’t possible, and provides concise code examples and best‑practice tips for merging DataFrames efficiently.
This article demonstrates how to replace typical Excel operations such as data import, VLOOKUP, pivot tables, and charting with Python's pandas and Plotly libraries, providing code examples and step‑by‑step explanations for each task.
This article walks through a Python string fundamentals question raised in a community group, presents a concise code solution provided by an instructor, demonstrates the successful output, and offers practical tips for asking clear programming questions.
This article introduces the Python Statsmodels library, explains its key features such as linear regression, GLM, time‑series and robust methods, shows how to install it, load data with pandas, perform descriptive statistics, visualizations, hypothesis testing, and simple and multiple linear regression examples.
This article provides a comprehensive guide to using Pandas for complex queries, data type conversion, sorting, adding and modifying data, advanced filtering, iteration, and functional operations, offering numerous code examples that illustrate how to efficiently clean, transform, and analyze tabular data in Python.
This tutorial demonstrates how to use Python libraries such as itchat, jieba, matplotlib, SnowNLP, and Tencent Youtu SDK to collect WeChat friend information and perform data analysis on gender distribution, avatar characteristics, signature text (including word‑cloud and sentiment analysis), and geographic location, presenting the results with visual charts and maps.
This article demonstrates how to replace typical Excel operations such as VLOOKUP, pivot tables, duplicate removal, missing‑value handling, multi‑condition filtering, fuzzy matching, column splitting, outlier replacement, grouping and labeling with concise Python pandas code to streamline data analysis workflows.
This tutorial demonstrates how to use Python's pandas library to read an Excel file, identify the first and last non‑zero entries in each row, and output their values and column positions, complete with code examples and performance tips.
This tutorial introduces the Pandas library, covering installation, data import from CSV and Excel, DataFrame creation, descriptive statistics, indexing with loc/iloc, and applying custom functions to clean and transform column values, all illustrated with code snippets and images.
This article examines the Bank Python architecture—four core building blocks and a three‑layer platform (interaction, domain, data)—and explains how a self‑service Python environment can deliver fast, real‑time, low‑latency analytics for financial professionals while addressing risk, compliance, and hybrid‑cloud challenges.
This article demonstrates how to replace Excel's common data‑analysis tasks—such as VLOOKUP, data import/export, pivot tables, and charting—by using Python's pandas library together with Plotly for visualization, providing code examples and performance tips for each scenario.
This tutorial demonstrates how to use Python's requests and BeautifulSoup libraries to scrape the titles and release years of the 250 movies listed on Douban, clean the extracted data, output it for Excel, and then create a pivot table and chart to visualize the yearly distribution of top films.
This tutorial walks through installing Pandas, importing CSV and Excel files, building DataFrames from dictionaries, describing data, indexing with loc/iloc, and applying custom functions to transform columns, providing clear code examples and visual outputs.
This article demonstrates how to collect, clean, and visualize Taobao mooncake sales data using Python libraries such as Pandas, Pyecharts, jieba and collections, revealing top‑selling flavors, regional distribution, price ranges and shop rankings through step‑by‑step data‑processing and charting techniques.
This article presents seven practical techniques for enhancing daily data‑analysis work in Jupyter notebooks, covering Pandas Profiling, Cufflinks/Plotly visualisation, IPython magic commands, markdown formatting, keyboard shortcuts, displaying multiple outputs simultaneously, and converting notebooks into live presentation slides.
This article presents ten core SQL techniques—including CTEs, recursive CTEs, temporary functions, CASE WHEN pivots, EXCEPT vs NOT IN, self‑joins, ranking window functions, delta calculations, cumulative sums, and date‑time manipulation—each explained with clear descriptions and practical query examples to help candidates ace data‑analysis interviews.
This article demonstrates how to replace common Excel operations such as data import, VLOOKUP, pivot tables, and charting with Python libraries like pandas and plotly, providing step‑by‑step code examples, performance tips, and comparisons that help analysts transition from spreadsheet‑based workflows to programmatic data analysis.
The article details vivo Game Center’s end‑to‑end intelligent gray‑release data analysis system, which combines experimental design, statistical significance testing, multi‑dimensional anomaly root‑cause analysis (via the Adtributor algorithm), automated reporting, and sample‑size estimation to enable scientific version evaluation, rapid issue detection, and a fast closed‑loop verification process for game updates.
This article demonstrates two concise methods to add a column of average marks to a Pandas DataFrame by using lambda with np.mean or directly applying NumPy's mean function, complete with sample data, expected output, and step‑by‑step code snippets.
This article demonstrates how to replace typical Excel tasks such as VLOOKUP, pivot tables, duplicate removal, missing‑value handling, multi‑condition filtering, and grouping with concise Python pandas code, providing step‑by‑step examples and practical tips for efficient data analysis.
This tutorial walks you through installing Pandas, importing data from CSV and Excel, creating DataFrames from dictionaries, describing datasets, indexing with loc/iloc, and cleaning columns using apply, all illustrated with clear code examples and visual outputs.
This article explains how to iterate over DataFrame rows with pandas iterrows(), demonstrates grouping data using pandas groupby() with examples of splitting, aggregation, transformation, and filtration, and provides complete code snippets for each operation.
This article provides a comprehensive, step‑by‑step tutorial on using pandas.read_excel, covering its syntax, file handling, sheet selection, header and column name options, additional parameters, and practical tips for efficiently loading Excel data into Python dataframes.
This tutorial demonstrates two concise Python projects—using pyautogui to keep the computer awake by moving the mouse and employing pandas to scrape tabular data from a website—each implemented with roughly five lines of code and explained step by step.
This tutorial walks through using pandas to import, review, preprocess (including integration, cleaning, transformation, handling missing and duplicate values, outlier detection, and sampling), analyze (descriptive statistics and correlation), and visualize e‑commerce data with Python, providing practical code examples for each step.
This tutorial demonstrates how to collect the top 2000 entries of the 2021 China Hurun Rich List using Python's requests library, process the data with pandas, and create insightful visualizations in Tableau, covering data extraction, cleaning, and analysis steps.
This article presents a series of Python examples using xlwings and pandas to batch‑sort worksheets, filter and aggregate data across multiple workbooks, compute summary statistics, perform correlation, ANOVA, regression, and generate pivot tables and visualizations, illustrating practical Excel automation and data‑analysis techniques.
This tutorial demonstrates how to use Python libraries such as itchat, jieba, matplotlib, SnowNLP, and Tencent Youtu SDK to collect WeChat friend information and perform data analysis and visualization on gender distribution, avatar characteristics, signature sentiment, and geographic location, providing practical code examples and visual results.
This article demonstrates how to collect historical Beijing weather data using Python web‑scraping, clean and transform it with pandas, visualize overall and monthly patterns, and extract insights such as the first winter snowfall each year and annual snow‑day counts.
This tutorial shows how to use Python to crawl Douban's Top250 movie list, handle anti‑scraping measures, extract detailed fields, store the data in Excel, and perform data cleaning, statistical analysis, and visualizations such as year distribution, rating trends, and genre word clouds.
This tutorial demonstrates how to scrape Maoyan movie comments for "The Battle at Lake Changjin", clean and store the data, perform comprehensive visual analyses such as likes, city, gender, watch status, rating, user level, and creator mentions, and finally predict next‑day box office using linear regression with sklearn.
This comprehensive tutorial translates the official Pandas documentation into Chinese, then walks readers through DataFrames, Series, indexing, I/O operations, data manipulation, string handling, merging, and advanced tips, providing clear code examples for each concept.
This tutorial walks through using Python's Selenium to automate scrolling and extract QQ Music hot comment data—including user avatars, names, timestamps, and content—then saves the information to CSV and creates a Chinese word cloud for visual analysis.
This article introduces twelve powerful NumPy and Pandas functions—six for each library—explaining their purpose, usage, and providing code snippets, enabling readers to perform efficient array manipulation, data filtering, aggregation, and I/O operations, with a link to the full Jupyter Notebook on GitHub.
This article introduces twelve high‑performance Numpy and Pandas functions—six for each library—explaining their purpose, typical use cases, and providing ready‑to‑run code examples to help data analysts work faster and more efficiently.
This article demonstrates how to use Python to scrape historical lottery data, apply a custom pattern‑matching algorithm to identify winning opportunities, aggregate the results over multiple days, and export the analysis to an Excel file for further review.
This tutorial explains how to create, read, and write JSON files using Python's json module, load them into Pandas dataframes, perform data manipulation, rename columns, save to CSV, fetch JSON from URLs, and visualize the data with Seaborn, providing code examples and visual illustrations.
To boost Xianyu’s user retention, the team built a long‑term promotion platform that combines configurable operational activities with algorithmic SPU recommendations, using Kunpeng extension points, supply‑demand analysis, and conditional search to personalize seller prompts, improve click‑through, and lay groundwork for broader scenario expansion.
This article presents ten concise Python and Jupyter Notebook tricks—including pandas‑profiling for quick data‑frame exploration, interactive plotting with Cufflinks, useful Jupyter magic commands, pretty‑printing, alert boxes, and shortcuts for debugging and cell output—that together dramatically accelerate everyday data‑analysis tasks.
This tutorial demonstrates how to use Python's requests and BeautifulSoup libraries to scrape the titles and release years of the top 250 movies from Douban, clean the data, export it to Excel, and then create pivot tables and charts to visualize yearly film production trends.
This article introduces Python's versatile ecosystem, highlighting eight key tools—including IDLE, scikit-learn, Theano, Selenium, TestComplete, Beautiful Soup, Pandas, and PuLP—that empower developers in IDE usage, machine learning, web automation, data analysis, and optimization tasks.
This article demonstrates how to use Python's time, NumPy, and Pandas libraries to build an RFM (Recency, Frequency, Monetary) model for customer value segmentation, covering data preparation, score calculation, weighting strategies, and exporting the results for marketing insights.
The article examines the 2020 Beijing points‑based household registration dataset, showing how to download the CSV, use shell commands like awk, sort, uniq, and grep to extract score, company, surname, given‑name, and age distributions, and highlights the top companies and common surnames.
This article demonstrates how to use Python to scrape Meituan for Shenzhen barbecue restaurant data, clean and enrich the dataset with Pandas, and visualize market insights such as regional distribution, average spending, ratings, and shop types using pyecharts and Matplotlib.
This tutorial demonstrates how to import Excel data into Pandas, reproduce IF and VLOOKUP functionality, and generate pivot tables with Python, highlighting the differences between Excel and Pandas and showing why Python can be a more reusable and scalable solution for data analysis.
This article demonstrates how to employ Pandas' read_html function to quickly fetch, parse, and analyze the Wikipedia table of the world's richest billionaires, covering basic usage, ranking by net worth, selective column extraction, and advanced parameter options.
The article demonstrates how a programmer can replace tedious manual Excel operations by using Python and pandas to batch‑process 128 sales spreadsheets, calculate brand‑level revenue, and dramatically reduce processing time from hours to seconds, illustrating a practical data‑analysis workflow.
This tutorial demonstrates how to read an Excel file with pandas, convert an 8‑digit birthdate column to datetime, calculate age, and then use groupby to produce aggregated statistics by age and gender, including combined summaries for all students and females only.
This article introduces Python's core concepts and hierarchy, highlights ten frequent beginner mistakes, and walks through building a basic web scraper that extracts book information from Douban, processes it with pandas, and displays the resulting data, providing a practical learning path for Python fundamentals.
This tutorial shows how to fetch restaurant review data from online platforms, clean and structure it in Python, and then use pyecharts along with Python's Counter to create bar and pie visualizations that reveal overall impressions and star‑rating distributions.
This article explains why iterating over Pandas DataFrames with iterrows() is slow, demonstrates the performance gain of using itertuples()—often over a hundred times faster—and provides a simple example to help Python data‑analysis practitioners write more efficient code.
This comprehensive tutorial by data‑science instructor Kevin Markham presents 25 practical pandas techniques—including data loading, cleaning, transformation, aggregation, visualization, and performance optimization—demonstrated with real‑world datasets such as drinks, movies, Titanic, Chipotle orders, UFO sightings, and stock prices.
This article introduces Python's core data structures and the most widely used libraries—NumPy, SciPy, Matplotlib, Pandas, Scikit‑Learn, Keras, and Gensim—providing concise explanations and runnable code examples to help readers quickly start data analysis and machine‑learning projects.
This tutorial walks you through extracting dynamic Maoyan review data for the hit animation "Ne Zha" using Python, parsing the JSON payload, storing it as JSON, and then visualizing ratings, gender distribution, city hotspots, and word‑cloud insights with pyecharts and wordcloud.
This guide presents twenty indispensable Pandas code examples, covering data import, inspection, cleaning, transformation, and advanced DataFrame operations, organized into three sections to help Python users quickly master essential data‑analysis practical tasks.
This article demonstrates how to scrape the full 2018 Chinese variety‑show list from Douban using Python Selenium and BeautifulSoup, compile detailed metadata and actor information into Excel, and then analyze popularity rankings, rating distributions, frequent celebrity appearances, and common negative feedback.
This article presents seven practical Python projects—including a Zhihu image scraper, dual chatbot conversation, AI poetry author classifier, 35‑choose‑7 lottery generator, automatic apology letter writer, screen‑capture tool, and GIF creator—complete with ready‑to‑run code snippets and step‑by‑step explanations for developers eager to expand their programming repertoire.
Using a Kaggle dataset of over 40,000 matches from 1872 to 2018, this notebook demonstrates how to clean, transform, and visualize World Cup data with Python, pandas, and Matplotlib to identify top‑winning teams, total goal statistics, and forecast the most likely 2018 champion.
This article walks through a Python‑based data analysis of World Cup matches from 1872 to 2018, using pandas and Jupyter Notebook to clean the data, compute win counts and total goals, visualize the top teams, and finally predict that Germany, Argentina and Brazil are the strongest contenders for the 2018 title.
This article explains why Python has become the preferred language for data analysis, highlighting the power of Jupyter notebooks, Pandas data frames, scikit-learn machine‑learning tools, and Matplotlib/Basemap visualization libraries, and compares them with Java and PHP alternatives.
This tutorial walks through loading Apple stock CSV data with NumPy, computing basic statistics like mean, median, variance, weighted average, daily returns, volatility, and handling dates, while demonstrating essential NumPy functions and code snippets for practical financial data analysis.
