Note, everything is returned as string/texts. You can choose how many and what data types to be generated. If you just say ‘city’ instead of ‘city_real’, you will get fictitious city names :) print(myDB.gen_data_series(num=8,data_type='city')) > New Michelle Robinborough Leebury Kaylatown Hamiltonfort Lake Christopher Hannahstad West Adamborough How to generate a Pandas dataframe with random entries? import pydbgen from pydbgen import pydbgen myDB=pydbgen.pydb()Īfter that, you can access the various internal functions exposed by the pydbobject. You have to initiate a pydb object to start using it. Note, it’s currently only tested on Python 3.6. Remember you need to have Faker installed to make this work. It’s (current version 1.0.5) hosted on PyPI (Python Package Index repository). ![]() name, address, credit card number, date, time, company name, job title, license plate number, etc.) and save them in either Pandas dataframe object, or as a SQLite table in a database file, or in a MS Excel file. It is a lightweight, pure-python library to generate random useful entries (e.g. I am going to go over similar details in the short article. You can read in details about the package here. I am glad to introduce a lightweight Python library called pydbgen. Use TABBED for TAB delimited text results (best option for opening. Would it not be great to have a simple tool or library to generate a large database with multiple tables, filled with data of one’s own choice?Īpart from the beginners in data science, even seasoned software testers may find it useful to have a simple tool where with a few lines of code they can generate arbitrarily large data sets with random (fake) yet meaningful entries. Use RAW if you want COMMA delimited text results. But access to a large enough database with real data (such as name, age, credit card, SSN, address, birthday, etc.) is not nearly as common as access to toy datasets on Kaggle, specifically designed or curated for machine learning task. Now, for data science - having a basic familiarity of SQL is almost as important as knowing how to write code in Python or R. However, from my personal experience, I found that the same is not true when it comes to learning SQL. Fortunately, there are many high-quality real-life datasets available on the web for trying out cool machine learning techniques. We conclude that the proven feasibility of FL in our simulated distributed setting lays the groundwork for utilising this approach in realistic environments of grander scale while overcoming potential privacy concerns or logistical challenges in the setting of centralised analytics.When you start learning and practicing data science, often the biggest worry is not the algorithms or techniques but availability of raw data. We compare the resulting training outcomes with the centralised model training (CL) approach and find CIIL performed similarly to CL but less stable, while FL outperformed CL by 7.5%. We introduce a rainfall generator training procedure relying on Generative Adversarial Networks (GANs) and evaluate two DA algorithms: Federated Learning (FL) and Cyclic Institutional Incremental Learning (CIIL). As example of use, we choose the decentralised training of rainfall data generators. In this work, we propose a feasibility study evaluating the applicability of DA on hydrological data. Distributed Analytics (DA) aims to overcome these challenges through decentralised model training by bringing the algorithm to the data instead of vice versa. ![]() However, data centralisation entails challenges regarding data-stream logistics, data locality, and memory overhead. Capturing processes for rainfall data are often highly distributed, with multiple radar stations contributing to a centralised data set. Such synthetic data instances can be produced by precipitation generators trained in an adversarial setting on historical rainfall data. Newly introduced ML-based flood forecasting methods rely on high-intensity synthetic rainfall events due to the sparsity of their real counterpart. Recent heavy rainfall-induced flood events, for example in Germany, Australia and USA, have highlighted the relevance of countermeasures in saving human lives and preventing property damage.
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