74 skills found · Page 1 of 3
mpquant / Ashare股票行情实时数据接口-A股,完全免费的沪深证券股票数据-中国股市,python最简封装的API接口,包含日线,历史K线,分时线,分钟线,全部实时采集，系统包括新浪腾讯双数据核心采集获取，自动故障切换，STOCK数据格式成DataFrame格式,可用来查询研究量化分析，股票程序自动化交易系统.为量化研究者在数据获取方面极大地减轻工作量，更加专注于策略和模型的研究与实现。
eakmanrq / SqlframeTurning PySpark Into a Universal DataFrame API
bluenote10 / NimDataDataFrame API written in Nim, enabling fast out-of-core data processing
cylondata / CylonCylon is a fast, scalable, distributed memory, parallel runtime with a Pandas like DataFrame.
onesuper / PandasticsearchAn Elasticsearch client exposing DataFrame API
Edwardvaneechoud / FlowfileFlowfile is a visual ETL tool and Python library combining drag-and-drop workflows with Polars dataframes. Build data pipelines visually, define flows programmatically with a Polars-like API, and export to standalone Python code. Perfect for fast, intuitive data processing from development to production.
SciNim / DatamancerA dataframe library with a dplyr like API
henridf / Apache Spark NodeNode.js bindings for Apache Spark DataFrame APIs
VirtusLab / IskraTypesafe wrapper for Apache Spark DataFrame API
yonghah / Esri2sfScrape features from ArcGIS Server REST API and create simple features dataframe
ejtraderLabs / EjtraderMTMetatrader 5 API - Trading and history OHLC Dataframe in Nano Seconds
data-apis / Dataframe ApiRFC document, tooling and other content related to the dataframe API standard
minio / Spark SelectA library for Spark DataFrame using MinIO Select API
ssavvides / Tpch SparkTPC-H queries in Apache Spark SQL using native DataFrames API
UMEssen / FHIR PYrateFHIR-PYrate is a package that provides a high-level API to query FHIR Servers for bundles of resources and return the structured information as pandas DataFrames. It can also be used to filter resources using RegEx and SpaCy and download DICOM studies and series.
blaylockbk / SynopticPyRetrieve mesonet weather data as Polars DataFrames from Synoptic's Weather API.
wesselhuising / PandanticGone are the days of black-box dataframes in otherwise type-safe code! Pandantic builds off the Pydantic API to enable validation and filtering of the usual dataframe types (i.e., pandas, etc.)
kelvingakuo / FychartsUnofficial Spotify Charts API. Get any and all data for top 200 and viral 50 music on Spotify. 27th Apr 2021 Update - fycharts returns empty dataframes due to CloudFlare protection on spotifycharts.com
mahdi-usask / Wind Speed Forecasting For Wind Power Generation Plant. Neural Network ML Based Prediction Algo. For largescale wind power penetration Wind speed prediction is a basic requirement of wind energy generation. There are many artificial neural network (ANN), ARMA, ARIMA approaches proposed in the recent literature in order to tackle this problem. This paper will use the artificial neural network (ANN) approach to get a prediction of wind speed using historical wind speed data. The historical data used here were gathered from NREL website ,as hourly basis from 80 meter hub height. The measurement location is NREL Flatirons Campus (M2). The readings displayed are derived from instruments mounted on or near a 82 meter (270 foot) meteorological tower located at the western edge of the Flatirons Campus (formerly NWTC) and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado. The tower is located at 39o 54' 38.34" N and 105o 14' 5.28" W (datum WGS84) with its base at an elevation of 1855 meters (6085 feet) above mean sea level. Data from year 2014 to 2018, in total 5 years of data has been used here as dataframe. Here the neural network has been implemented by Tensorflow’s Keras API. The used model is “sequential”. Four dense layer has been used in the optimized model. LSTM(Long- short-term memory) architecture has been used here as neural network architecture. Activation function being used in the dense layers are dropout function. The optimizer being used here is Adam. Here various range of Dropout function has been examined and chosen the best fit for this model. Also this paper examined various kinds of optimization method and used the best fitted one. The model performances were evaluated using the mean squared error using adam optimizer. Various kinds of data analytic techniques has been used here for better visualization and in depth understanding of the dataset and its variables. Since it is mostly a time series data so in the analytic part how the data is being changed with time has been shown. From the result of the predicted dataset it can be state that, this wind speed prediction model works best for all kinds of winds speed besides overfitted/ abnormal wind speeds which is a very rare case scenario.
Wassim17Labdi / Nitro PandasA lightweight pandas-compatible DataFrame API built on top of Polars for speed and simplicity
