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Subsets of data can be selected by column name, index, or Boolean expressions. For example, df[df['col1'] > 5] will return all rows in the DataFrame df for which the value of the column col1 exceeds 5. [4]: 126–128 Data can be grouped together by a column value, as in df['col1'].groupby(df['col2']), or by a function which is applied to the index.
Data-driven programming is similar to event-driven programming, in that both are structured as pattern matching and resulting processing, and are usually implemented by a main loop, though they are typically applied to different domains.
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.
Python has many different implementations of the spearman correlation statistic: it can be computed with the spearmanr function of the scipy.stats module, as well as with the DataFrame.corr(method='spearman') method from the pandas library, and the corr(x, y, method='spearman') function from the statistical package pingouin.
The best case input is an array that is already sorted. In this case insertion sort has a linear running time (i.e., O(n)).During each iteration, the first remaining element of the input is only compared with the right-most element of the sorted subsection of the array.
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
A pointer a pointing to the memory address associated with a variable b, i.e., a contains the memory address 1008 of the variable b.In this diagram, the computing architecture uses the same address space and data primitive for both pointers and non-pointers; this need should not be the case.
Example scatterplots of various datasets with various correlation coefficients. The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient (PPMCC), or "Pearson's correlation coefficient", commonly called simply "the correlation coefficient".