When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Wikipedia:CSVLoader/Find and replace - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:CSVLoader/Find...

    If csv file contains non-english characters than it needs to be saved in UTF-8 format. One way to do this is to change .csv file extension to .txt, and open resulting file in Notepad and use save as option to save in UTF-8 format. Copy CSVLoader.dll to the AWB directory. Log in to AWB; Create replacement rules in option tab Normal settings button.

  3. Comma-separated values - Wikipedia

    en.wikipedia.org/wiki/Comma-separated_values

    Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record. Each record consists of the same number of fields, and these are separated by commas in the ...

  4. Tab-separated values - Wikipedia

    en.wikipedia.org/wiki/Tab-separated_values

    Tab-separated values (TSV) is a simple, text-based file format for storing tabular data. [3] Records are separated by newlines , and values within a record are separated by tab characters . The TSV format is thus a delimiter-separated values format, similar to comma-separated values .

  5. Open energy system models - Wikipedia

    en.wikipedia.org/wiki/Open_energy_system_models

    It uses the pandas library for data manipulation and analysis and the PYPOWER library [83] to solve for power flow. Unlike some open source power system tools, pandapower does not depend on proprietary platforms like MATLAB. pandapower supports the automated analysis and optimization of distribution and transmission networks. This allows a ...

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. [49]