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 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 article explains why accurate trade data is essential for strategy backtesting, why Binance is chosen, and provides a detailed Python workflow—including argument parsing, using the Binance aggTrades endpoint, handling pagination with from_id, cleaning the resulting DataFrame, saving to CSV, and validating the data integrity.
This step‑by‑step guide shows how to download the FIFA 18 player dataset from GitHub, create a MySQL database and table, read the CSV with Python’s csv.DictReader, generate INSERT statements, batch‑load the data efficiently, and query Argentine players, illustrating a complete end‑to‑end data‑import workflow.
This article demonstrates how to use Python's pandas library to read CSV and XLS files, filter and merge data based on group assignments, compute derived columns, and export the results to a new Excel workbook, providing a complete automation workflow for Excel data processing.
This article provides a comprehensive guide to Python's file handling capabilities, covering built‑in functions, the csv module, numpy's load and save methods, pandas' extensive read/write utilities, Excel libraries such as xlrd and openpyxl, and popular database connectors like pymysql and sqlalchemy.
This guide demonstrates how to use Zabbix's API with Python to retrieve host information, item IDs, historical and trend data, process the metrics, and automatically write them into an Excel/CSV file, enabling scheduled monitoring reports.
This article compares CSV, JSON, Parquet, and Avro file formats, outlining their structures, advantages, and drawbacks, and explains how Apache Spark supports each format for efficient big‑data storage and processing.
This article explains how to scrape Chinese Basketball Association (CBA) match data from a portal website, analyzes the page structure, extracts table rows using regular expressions, converts them to CSV format, and provides a complete Java/Groovy code example for automated data collection.
This tutorial walks you through discovering a real‑time earthquake data source, analyzing its pagination API, building a Python scraper with requests, parsing the JSON responses, storing the results in CSV, and performing visual analyses such as top‑magnitude locations, monthly frequency, repeat‑site counts, magnitude distribution, and word‑cloud generation.
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 explains how to generate and download CSV files directly in the browser by converting JSON data into comma‑separated values, using the Blob API and the anchor element’s download attribute, and includes advanced techniques for custom headers and handling Excel formatting issues.
This tutorial walks through using Spring Batch to read and write flat files—including CSV, TXT, and XML—by configuring Maven dependencies, setting up a Spring Boot project structure, and implementing FlatFileItemReader, FlatFileItemWriter, and custom ItemProcessor components with concrete code examples.
This tutorial shows how ordinary users can replace spreadsheet work by using Linux command‑line utilities such as head, tail, wc, grep, tr, sort, sed, cut, uniq and awk to quickly explore, filter, transform and summarize CSV/TSV data sets.
The article explains why PowerShell’s ConvertFrom‑Csv fails with a duplicate “状态” (state) column, how the issue stems from language translation of column names, and provides a practical workaround using custom headers and Out‑GridView to display driver information.
This article explains practical methods for importing various external data formats—such as CSV, plain text, XML, and JSON—into MySQL, covering command-line utilities, LOAD DATA statements, stored procedures, third‑party tools like mysqljsonimport, and using MySQL Workbench, while addressing configuration and security considerations.
