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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.
While the terms "frame" and "picture" are often used interchangeably, the term picture is a more general notion, as a picture can be either a frame or a field.A frame is a complete image, and a field is the set of odd-numbered or even-numbered scan lines composing a partial image.
A typical implementation will raytrace each pixel of the display starting at the bottom of the screen using what is known as a y-buffer. When a voxel is reached that has a higher y value on the display it is added to the y-buffer overriding the previous value and connected with the previous y-value on the screen interpolating the color 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 .
A hash function is any function that can be used to map data of arbitrary size to fixed-size values, though there are some hash functions that support variable-length output. [1] The values returned by a hash function are called hash values, hash codes, hash digests, digests, or simply hashes. [2]
If we have a vector containing elements (2, 5, 7, 3, 8, 6, 4, 1), and want to create an array slice from the 3rd to the 6th elements, we get (7, 3, 8, 6). In programming languages that use a 0-based indexing scheme, the slice would be from index 2 to 5. Reducing the range of any index to a single value effectively removes the need for that index.
Slice sampling is a type of Markov chain Monte Carlo algorithm for pseudo-random number sampling, i.e. for drawing random samples from a statistical distribution.The method is based on the observation that to sample a random variable one can sample uniformly from the region under the graph of its density function.