This article compiles a collection of practical Python and Jupyter Notebook tips—including pandas profiling, interactive plotting with Cufflinks, useful magic commands, debugging shortcuts, and output handling techniques—to help data analysts work faster, produce richer visualizations, and avoid common pitfalls.
This tutorial walks through using pandas for data import, inspection, preprocessing, analysis, and visualization, providing practical code snippets and explanations to help readers understand essential data manipulation techniques in Python.
Discover the true execution sequence of SQL queries, why SELECT runs fifth, how window functions and GROUP BY interact, and the impact of engine optimizations, illustrated with diagrams and code examples spanning aliases, LINQ, and pandas.
This article analyzes 13,966 Chinese operations‑engineer job postings scraped from 51job, cleaning the data with Python and Pandas, then visualizing industry demand, city concentration, salary ranges, education requirements, company size distribution, and keyword trends to guide job seekers and recruiters.
This tutorial demonstrates how to combine Excel’s interactive features with Python’s Pandas library to perform comprehensive data operations—including reading, generating, filtering, sorting, handling missing values, deduplication, merging, grouping, calculation, statistics, visualization, sampling, pivot tables, and VLOOKUP—showing when each tool excels.
Although most SQL statements begin with SELECT, the actual execution follows a specific order—WHERE, GROUP BY, HAVING, then SELECT—illustrated by a diagram that also clarifies common misconceptions about filtering window functions, column aliases, and how databases may reorder operations for optimization.
This article explains the actual logical execution order of SQL statements, clarifies common misconceptions about WHERE, GROUP BY, and SELECT, shows how column aliases affect processing, discusses optimizer reordering, and compares the order with LINQ and pandas query patterns.
This tutorial demonstrates how to import CSV and Excel stock‑listing files using pandas, adjust parsing parameters, view data summaries, and combine multiple sheets into a single DataFrame through concatenation, providing a practical example of data import and merging in Python.
This article demonstrates how to replace typical Excel operations such as data import/export, VLOOKUP, pivot tables, and chart creation with Python libraries pandas and plotly, providing code examples and explanations for each task.
This tutorial explains how to load a CSV file of financial time‑series data into pandas, perform basic exploratory analysis, compute absolute and relative changes, generate log returns, visualize results, and apply weekly or monthly resampling while avoiding look‑ahead bias.
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 presents a comprehensive analysis of a global supermarket's four‑year sales dataset, covering data collection, preprocessing, exploratory visualizations, sales, quantity, profit, market segmentation, product performance, customer segmentation, RFM modeling, and actionable recommendations to improve revenue and customer retention.
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.
This tutorial demonstrates how to use pandas for calculating total and average scores, adding column means, removing duplicate records, and transposing Excel data, providing clear code examples and explanations of key functions such as sum, mean, duplicated, drop_duplicates, and transpose.
This tutorial demonstrates how to combine separate student tables using pandas' merge function, explains the merge parameters, shows how to fill missing values with fillna, validates data for anomalies, and splits a phone column into separate area code and number columns.
This article demonstrates how Python's pandas library can replace manual Excel operations by reading, calculating, sorting, filtering, and visualizing data, offering efficient solutions for large datasets and showcasing code examples for automatic column filling, price adjustments, and student score analysis.
This article introduces the cpca Python module that parses free‑form Chinese address strings into province, city, and district fields, explains how to install it, demonstrates basic and advanced usage with code examples, and describes optional full‑text and map‑drawing features.
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 walks through a complete Python workflow for analyzing a month of Taobao mobile app data, covering data import, cleaning, feature engineering, traffic and behavior metrics, funnel conversion, RFM segmentation, and visualizations to reveal user patterns and business performance.
This tutorial demonstrates how to download, read, explore, visualize, and preprocess the HR attrition dataset with pandas, covering tasks such as duplicate removal, missing‑value handling, categorical encoding, normalization, and conditional column updates to prepare the data for machine‑learning modeling.
This article explains how to interpret each tick in futures market data, compute directional open‑close positions, and visualize multi‑short and multi‑long volumes using Python libraries such as pandas, matplotlib, and TA‑Lib, providing a step‑by‑step tutorial with full source code.
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 article explains how the Mars framework enables parallel and distributed execution of core Python data‑science libraries—Numpy, Pandas, and Scikit‑Learn—while integrating with RAPIDS for GPU acceleration, and demonstrates its performance advantages through code examples and benchmark results.
This tutorial explains how to retrieve the current timestamp, format specific dates, convert between strings and datetime objects, perform time indexing with pandas, and execute time arithmetic and offsets in Python using datetime, pandas, and dateutil libraries.
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 guide introduces pandas, the essential Python library for data science, covering installation, data import/export, basic DataFrame operations, logical filtering, visualization with matplotlib, performance tips using tqdm, and advanced techniques like merging, grouping, and iterating, helping beginners become efficient data analysts.
This tutorial walks data analysts through a side‑by‑side comparison of common data‑manipulation tasks using pandas in Python and SQL, covering everything from basic selects and filters to joins, aggregations, unions, ordering, case expressions, and data updates with clear code examples.
This tutorial walks through loading order data into pandas and SQL, then demonstrates how to retrieve current dates, extract date components, convert between readable dates and Unix timestamps, transform between 10‑digit and 8‑digit date formats, and perform date arithmetic using pandas, MySQL, and Hive.
This article introduces essential Pandas techniques for data import, cleaning, type conversion, and merging, providing clear code examples that demonstrate reading from MySQL, handling missing values, transforming columns, and combining multiple DataFrames for comprehensive data analysis.
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.
This article explains how the Python library Camelot can quickly extract tables from PDF files, convert them into pandas DataFrames, and export the data to various formats, while also covering installation options and providing a concise code example.
This article demonstrates how to use Selenium and Python to crawl over 6,000 Chinese tourist attractions from Qunar, extract ratings, popularity and sales data, and visualize the results with pandas, seaborn, matplotlib, and pyecharts, revealing the most visited sites and regional travel trends during the 2019 National Day holiday.
This guide walks you through installing Pandas, understanding its core data structures like Series, DataFrame, and Panel, performing data access, adding or removing rows and columns, filtering datasets, and applying these techniques to a real‑world stock selection workflow using TuShare and TALib.
This tutorial walks you through using Python and Pandas to import Excel files, explore DataFrames, apply filters, perform statistical calculations, create pivot tables, and emulate Excel functions like VLOOKUP, providing code examples and visual guides for each step.
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 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 guide demonstrates how to quickly build Python-based HTTP/HTTPS API automation test scripts using requests for sending requests, pandas for handling test case data, and openpyxl for exporting results to Excel, covering request construction, response handling, data comparison, and result styling.
When rebuilding a Plotly environment on macOS, pip fails to uninstall the system‑installed NumPy, causing pandas installation errors, and the solution involves manually removing NumPy’s egg‑info, using a reliable PyPI mirror, and selecting compatible library versions.
This tutorial walks you through loading historical stock data from Yahoo Finance with pandas, computing moving averages and returns, comparing multiple tech stocks, engineering features, training linear, quadratic, and K‑Nearest‑Neighbor models using scikit‑learn, evaluating their confidence scores, and visualizing short‑term price forecasts—all in under a dozen minutes of reading and coding.
This tutorial walks you through creating a Python desktop weather application using PyQt5, covering environment setup, city code preprocessing with pandas, UI design in Qt Designer, API integration, JSON parsing, shortcut handling, and packaging with PyInstaller.
This tutorial walks you through importing Pandas, understanding its Series and DataFrame structures, loading CSV data, inspecting, filtering, indexing, reshaping, merging, visualizing, and finally saving datasets, providing a comprehensive foundation for scientific Python data analysis.
This tutorial shows how to collect book metadata such as publisher, publication date, ISBN, price, rating and review count from Douban for a list of titles stored in Excel, using Python requests, lxml XPath parsing, pandas for merging and analysis, and visualizing the results with matplotlib.
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 tutorial introduces Pandas fundamentals, covering installation, data structures, importing CSV files, inspecting and reshaping data, filtering with boolean masks, indexing, applying functions, grouping, merging, quick plotting, and saving results, all illustrated with clear examples and images.
The article walks through a full Python workflow that scrapes up to 500 Douban movie reviews for "Dying to Survive" and "Hidden Blade," cleans and stores them in pandas, performs SnowNLP sentiment analysis, and visualizes city distribution, rating trends, and word clouds with pyecharts.
This report compiles publicly released Chinese government data on second‑class vaccine procurement across 28 provinces, cleans it into a unified CSV format, and uses pandas and echart to reveal a highly skewed distribution dominated by a few large manufacturers while noting data gaps and collection challenges.
This article documents the collection, cleaning, and statistical analysis of publicly released second‑category vaccine procurement data from 28 Chinese provinces, highlighting data sources, processing steps with pandas, top manufacturers, regional market shares, and the challenges encountered during the effort.
This article documents the collection of public second‑class vaccine procurement data from 28 Chinese provinces, describes the CSV schema, outlines the challenges faced during web scraping, and presents a pandas‑driven statistical analysis that highlights top manufacturers and their provincial market shares.
This article demonstrates how to use Python, pandas, and Jupyter Notebook to explore a comprehensive World Cup dataset, clean and enrich the data, visualize win and goal statistics for all teams, and finally predict the top three contenders for the 2018 tournament.
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.
Learn to quickly build a Python web scraper using requests and Xpath to extract Douban movie 'Black Panther' short reviews, covering setup, HTTP request analysis, data parsing, storage with pandas, and best practices like polite crawling intervals, all demonstrated with concise 12-line code.
This tutorial walks you through using Python's requests and XPath to scrape short comments from Douban's "Black Panther" page, covering tool setup, HTTP request analysis, data extraction, and saving results to CSV in just a dozen lines of code.
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 article introduces Python’s essential features for data analysis and mining, then reviews the most widely used libraries—NumPy, SciPy, Matplotlib, Pandas, Scikit‑Learn, Keras, and Gensim—each accompanied by concise code examples that demonstrate their core capabilities.
This article introduces seven must‑know Python tools—including IPython, GraphLab Create, Pandas, PuLP, Matplotlib, Scikit‑Learn, and Spark—explaining their key features and how they empower data scientists to work efficiently in production environments.
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 tutorial walks through using Python and pandas to fetch Yahoo Finance data, visualize stock prices with line and candlestick charts, and apply moving‑average techniques—including 20‑day, 50‑day, and 200‑day averages—to identify trends and build simple trading signals, all while emphasizing that the content is for educational purposes only and not investment advice.
This article documents a Python‑based web‑scraping project that harvested over 1.38 million Zhihu followers, filtered high‑impact users, and visualized insights such as follower distribution, gender ratio, top influencers, geographic spread, education, industry, and certification details, highlighting challenges and lessons learned.
This tutorial explains how to create rational, layered choropleth maps using Python libraries like Pandas together with Vega and GeoJSON, covering data preparation, FIPS mapping, and generating visualizations for population density, income, and unemployment rates.
The SkullGreymon team, winners of the biggest improvement award in the Tencent Social Ads university algorithm competition, share their journey from a late start in the preliminaries to significant performance gains in the finals, detailing memory‑saving feature extraction techniques, pandas and numpy usage, and their XGBoost modeling approach.
This tutorial explains why R programmers should add Python to their toolkit, compares core data types and structures between the two languages, introduces essential Python libraries for data analysis, and walks through a practical Boston housing dataset example to solidify the concepts.
This article explains the differences between pandas' apply, applymap, and map functions, then demonstrates how to perform principal component analysis on Germany's DAX30 index to identify key stocks, visualize contributions, and achieve effective dimensionality reduction.
This article presents a 16‑week Python data‑analysis learning roadmap covering environment setup, basic syntax, web‑scraping techniques, data‑analysis libraries such as pandas and NumPy, and data‑visualization with matplotlib, along with curated free resources and tutorials for each stage.
