Search Results for "json csv"
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Showing 31 open source projects for "json csv"
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JSON Crack
Innovative and open-source visualization application
JSON Crack is a free, open-source data visualization app capable of visualizing data formats such as JSON, YAML, XML, CSV and more, into interactive graphs. With its intuitive and user-friendly interface, JSON Crack makes it easy to explore, analyze, and understand even the most complex data structures. Whether you're a developer working on a large-scale project or a data enthusiast looking to uncover hidden insights, JSON Crack has the tools and features you need to unlock the full potential of your data. -
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Shale
Shale is a Ruby object mapper and serializer for JSON, YAML, TOML
Shale is a Ruby object mapper and serializer for JSON, YAML, TOML, CSV and XML. It allows you to parse JSON, YAML, TOML, CSV and XML data and convert it into Ruby data structures, as well as serialize data structures into JSON, YAML, TOML, CSV or XML. -
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World countries JSON
Constantly updated lists of world countries
Constantly updated lists of world countries and their associated alpha-2, alpha-3 and numeric country codes as defined by the ISO 3166 standard, available in CSV, JSON , PHP, SQL and XML formats, in multiple languages and with national flags included; also available are the ISO 3166-2 codes of provinces/ states associated with the countries. Also available are the ISO 3166-2 codes used for identifying the principal subdivisions (e.g., provinces or states) of all countries coded in ISO 3166-1. -
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Vince's CSV Parser
A modern C++ library for reading, writing, and analyzing CSV
There's plenty of other CSV parsers in the wild, but I had a hard time finding what I wanted. Inspired by Python's csv module, I wanted a library with simple, intuitive syntax. Furthermore, I wanted support for special use cases such as calculating statistics on very large files. With the deluge of large datasets available, a performant CSV parser is a necessity. By using overlapped threads, memory-mapped IO, and efficient data structures, this parser can quickly tackle large CSV files.... -
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Countries States Cities Database
Discover our global repository of countries, states, and cities
Full Database of city state country available in JSON, SQL, XML, YAML & CSV format. All Countries, States and cities are Covered and populated with Different Combinations and versions. The API database is regularly updated to ensure the latest data is available to users. Discover our global repository of countries, states, and cities! Get comprehensive data in JSON, SQL, XML, YAML, and CSV formats. -
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Easy Localization
Easy and Fast internationalizing your Flutter Apps
Easy and Fast internationalization for your Flutter Apps. You can use JSON, CSV, HTTP, XML, YAML files, etc. Code generation for localization files and keys. -
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pytablewriter
pytablewriter is a Python library to write a table in various formats
pytablewriter is a Python library to write a table in various formats: AsciiDoc / CSV / Elasticsearch / HTML / JavaScript / JSON / LaTeX / LDJSON / LTSV / Markdown / MediaWiki / NumPy / Excel / Pandas / Python / reStructuredText / SQLite / TOML / TSV / YAML. -
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jackson-core
Core part of Jackson that defines Streaming API
This project contains core low-level incremental ("streaming") parser and generator abstractions used by Jackson Data Processor. It also includes the default implementation of handler types (parser, generator) that handle JSON format. The core abstractions are not JSON specific, although naming does contain 'JSON' in many places, due to historical reasons. Only packages that specifically contain the word 'json' are JSON-specific. This package is the base on which Jackson's data-binding package builds on. It is licensed under Apache License 2.0. Alternate data format implementations (like Smile (binary JSON), XML, CSV, Protobuf, and CBOR) also build on this base package, implementing the core interfaces, and making it possible to use standard data-binding packages regardless of underlying data format. -
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omniparser
Native Golang ETL streaming parser and transform library
Omniparser is a native Golang ETL parser that ingests input data of various formats (CSV, txt, fixed length/width, XML, EDI/X12/EDIFACT, JSON, and custom formats) in streaming fashion and transforms data into desired JSON output based on a schema written in JSON. -
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Miller
Miller is like awk, sed, cut, join, and sort for name-indexed data
Miller is like awk, sed, cut, join, and sort for data formats such as CSV, TSV, JSON, JSON Lines, and positionally-indexed. With Miller, you get to use named fields without needing to count positional indices, using familiar formats such as CSV, TSV, JSON, JSON Lines, and positionally-indexed. Then, on the fly, you can add new fields which are functions of existing fields, drop fields, sort, aggregate statistically, pretty-print, and more. ... -
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Glaze
Extremely fast, in memory, JSON and interface library for modern C++
One of the fastest JSON libraries in the world. Glaze reads and writes from C++ memory, simplifying interfaces and offering incredible performance. Glaze also supports binary messages via BEVE and CSV support. And, the library has many more useful features for building APIs. -
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sq data wrangler
sq data wrangler
sq is a command line tool that provides jq-style access to structured data sources: SQL databases, or document formats like CSV or Excel. sq executes jq-like queries, or database-native SQL. It can join across sources: join a CSV file to a Postgres table, or MySQL with Excel. sq outputs to a multitude of formats including JSON, Excel, CSV, HTML, Markdown and XML, and can insert query results directly to a SQL database. sq can also inspect sources to view metadata about the source structure (tables, columns, size). ... -
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generatedata
A powerful, feature-rich, random test data generator
...The script is essentially an engine to generate any sort of random data in any format. It currently comes with 30 or so Data Types (types of data it generates), 12 Export Types (formats for the data, like CSV, SQL, JSON), plus around 32 data sets for specific countries (city names, regions, etc). But more importantly, it can be extended in any way you want. Check out the developer documentation for more information on that. The current major version of the script is 4.x, which was a big change over earlier releases. Earlier versions were written in PHP and MYSQL and 3.x offered a REST API to let you generate the data programmatically rather than via an API. ... -
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PHP7
PHP7 / Laravel Multi-format Streaming Parser
When it comes to parsing XML/CSV/JSON/... documents, there are 2 approaches to consider. DOM loading loads all the documents, making it easy to navigate and parse, and as such provides maximum flexibility for developers. Streaming implies iterating through the document, acts like a cursor, and stops at each element in its way, thus avoiding memory overkill. Thus, when it comes to big files, callbacks will be executed meanwhile file is downloading and will be much more efficient as far as memory is concerned. -
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Extract TOTP/HOTP secrets
Extract one time password (OTP) secrets from QR codes
The Python script extract_otp_secrets.py extracts one-time password (OTP) secrets from QR codes exported by two-factor authentication (2FA) apps such as "Google Authenticator". -
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python-benedict
dict subclass with keylist/keypath support
python-benedict is a dict subclass with keylist/keypath/keyattr support, I/O shortcuts (base64, cli, csv, ini, json, pickle, plist, query-string, toml, xls, xml, yaml) and many utilities... for humans, obviously. -
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DocArray
The data structure for multimodal data
...Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
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Murex
A smarter shell and scripting environment with advanced features
Murex is a shell, like bash / zsh / fish / etc however Murex supports improved features and an enhanced UX. Support for additional type information in pipelines, which can be used for complex data formats like JSON or tables. Meaning all of your existing UNIX tools to work more intelligently and without any additional configuration. Usability improvements such as in-line spell checking, context-sensitive hint text that details the behavior of a command before you hit return, and auto-parsing... -
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Chronicle Wire
Low Garbage Java Serialisation Library that supports multiple formats
Chronicle Wire is a Wire Format abstraction library. Chronicle Wire uses Chronicle Bytes for bytes manipulation, and Chronicle Core for low-level JVM access. Configuration includes aliased type information. This supports easy extension by adding new classes/versions, and cross-platform using type aliasing. By supporting types, a configuration file can bootstrap itself. You control how the configuration file is decoded. See example Queue Enterprise config. To send the configuration of a... -
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xsd2pgschema
Relational database replication tool based on XML Schema
xsd2pgschema is a Java application suite, which converts XML Schema 1.1 (hierarchical data model) to PostgreSQL DDL (relational data model) and supports XML data migration into PostgreSQL based on the XML Schema without defects on information content. It also supports full-text indexing via either Apache Lucene or Sphinx Search utilizing the relational data model. File conversion from XML to CSV, TSV, or JSON is possible as well as mapping XML Schema to JSON Schema. Obtained PostgreSQL database can be optimized at user's discretion. Moreover, differential update, and XPath query evaluation over PostgreSQL are possible. PgSchema server, serialized relational data model server, can be used to speed up the analysis of complex XML Schema. ... -
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SPyQL
Query data on the command line with SQL-like SELECTs powered by Python
...SPyQL is a query language that combines the simplicity and structure of SQL with the power and readability of Python. SPyQL offers a command-line interface that allows running SPyQL queries on top of text data (e.g. CSV, JSON). Data can come from files but also from data streams, such as as Kafka, or from databases such as PostgreSQL. Basically, data can come from any command that outputs text :-). More, data can be generated by a Python expression! And since SPyQL also writes to different formats, it allows to easily convert between data formats. -
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dsq
Commandline tool for running SQL queries against JSON, CSV, Excel
Commandline tool for running SQL queries against JSON, CSV, Excel, Parquet, and more. This is a CLI companion to DataStation (a GUI) for running SQL queries against data files. So if you want the GUI version of this, check out DataStation. -
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vscode-data-preview
Extension for importing, viewing, slicing, dicing, and charting JSON
Data Preview extension for importing, viewing, slicing, dicing, charting & exporting large .json array .arrow .avro .parquet data files, .config .env .properties .ini .yml configurations files, .csv/.tsv & .xlsx/.xlsb Excel files and .md markdown tables with Perspective - streaming data analytics WebAssembly library. -
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Tweet Parser
Analyse bases de données tweets pour rapport d'impact social media
Le logiciel permet d'analyser des bases de données de tweets (API Twitter extraites sous format .csv) afin de pouvoir créer un rapport d'impact social media d'un événement suivis par des hashtags. Le logiciel permet de créer différents graphiques pertinents et de pouvoir placer dans le presse-papier des tweets à mettre dans le rapport. -
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GoJSONQ
A simple Go package to Query over JSON/YAML/XML/CSV Data
A simple Go package to Query over JSON Data. It provides a simple, elegant, and fast ODM-like API to access, and query JSON documents. You can query your document using the various query methods such as Find, First, Nth, Pluck, Where, OrWhere, WhereIn, WhereStartsWith, WhereEndsWith, WhereContains, Sort, GroupBy, SortBy, and so on. Also, you can aggregate data after query using Avg, Count, Max, Min, Sum, etc.
