Data sources and pandas methods
Data sources for a data science project can be divided into the following categories:
Databases: Most CRM, ERP, and other enterprise archive tools on the website. Depending on the volume, speed, and variability, a traditional or NoSQL database. To connect with many popular details, we need JDBC / ODBC drivers from Python. Fortunately, there are drivers like that is available on all popular databases. Data processing is such a website including making a connection with Python to these sources, asking questions aboutdata via Python, and then trick it down using pandas. We will look at an example of how to do this later in this chapter.
Web Services: Many business application tools, especially Software such as Service tools (SaaS), making their data accessible through the App Programming Interfaces (APIs) instead of a website. This reduces the cost of permanent website hosting infrastructure. Instead, data is generated is available as a service, if required. An API call can be made Python, which returns data packets in formats such as JSON or XML. And data it is processed and used using pandas for continuous use.
Data files: Most data prototyping data science models come as data files.One example of data stored as a portable file is data from IoT sensors in most cases, data from these sensors is stored in a flat file, a .txt file, or .csv file. Another source of data file is a sample of existing data extracted from the website and stored in such files. Excessive data extraction machine science and learning algorithms are also stored in such files, as CSV, Excel, and .txt files. Another example is that weighted matrices are trained of the neural network model for deep learning can be saved as an HDF file.
Web and document scratches: Two other sources of data tables and text available on web pages. This data is collected on these pages using Python packages like BeautifulSoup and Scrapy and are included in the data file or database to be used continuously. Tables and data available in another nondata format file, such as PDF or Documents, are also a major source of data. This, then is released using Python packages such as Tesseract and Tabula-py.
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