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  2. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a ...

  3. Data preprocessing - Wikipedia

    en.wikipedia.org/wiki/Data_Preprocessing

    Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame. The data-frame is then used to ...

  4. Comma-separated values - Wikipedia

    en.wikipedia.org/wiki/Comma-separated_values

    CSV is a delimited text file that uses a comma to separate values (many implementations of CSV import/export tools allow other separators to be used; for example, the use of a "Sep=^" row as the first row in the *.csv file will cause Excel to open the file expecting caret "^" to be the separator instead of comma ","). Simple CSV implementations ...

  5. Apache Spark - Wikipedia

    en.wikipedia.org/wiki/Apache_Spark

    The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged [3] even though the RDD API is not deprecated. [4] [5] The RDD technology still underlies the Dataset API. [6] [7]

  6. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance.

  7. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.

  8. Iris flower data set - Wikipedia

    en.wikipedia.org/wiki/Iris_flower_data_set

    Scatterplot of the data set. The Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. [1]

  9. R (programming language) - Wikipedia

    en.wikipedia.org/wiki/R_(programming_language)

    R is a programming language for statistical computing and data visualization.It has been adopted in the fields of data mining, bioinformatics and data analysis. [9]The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.