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As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. [9]: 103–105 If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in ...
Conversely, in an eager language the above definition for ifThenElse a b c would evaluate (a), (b), and (c) regardless of the value of (a). This is not the desired behavior, as (b) or (c) may have side effects, take a long time to compute, or throw errors. It is usually possible to introduce user-defined lazy control structures in eager ...
Byte stuffing is a process that transforms a sequence of data bytes that may contain 'illegal' or 'reserved' values (such as packet delimiter) into a potentially longer sequence that contains no occurrences of those values. The extra length of the transformed sequence is typically referred to as the overhead of the algorithm.
Dataframe may refer to: A tabular data structure common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark;
Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function.Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.
Specifically, a for-loop functions by running a section of code repeatedly until a certain condition has been satisfied. For-loops have two parts: a header and a body. The header defines the iteration and the body is the code executed once per iteration. The header often declares an explicit loop counter or loop variable. This allows the body ...
Cowboy Names Go Next-Level. Call it the Yellowstone effect. "One of the biggest trends we’ll see for baby boy names in 2025 are 'Country Rebrand' names," says Sophie Kihm, editor-in-chief of ...
The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace).