Search results
Results from the WOW.Com Content Network
Many statistical and data processing systems have functions to convert between these two presentations, for instance the R programming language has several packages such as the tidyr package. 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 ...
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 ...
The transpose (indicated by T) of any row vector is a column vector, and the transpose of any column vector is a row vector: […] = [] and [] = […]. The set of all row vectors with n entries in a given field (such as the real numbers ) forms an n -dimensional vector space ; similarly, the set of all column vectors with m entries forms an m ...
Julia has the vec(A) function as well. In Python NumPy arrays implement the flatten method, [ note 1 ] while in R the desired effect can be achieved via the c() or as.vector() functions. In R , function vec() of package 'ks' allows vectorization and function vech() implemented in both packages 'ks' and 'sn' allows half-vectorization.
A default may specify a unit code or an expression that tests the input value, and which produces one of two different outputs depending on that value. In the expression, v represents the input value specified in the convert template, and exclamation marks (!) are used to separate the expression into either three or four fields.
Myth #2: You can access 100% of your home’s equity with a home equity loan or a HELOC. Unfortunately, very few lenders will finance a loan for 100% of your home equity.
The Whopper Melts, on the other hand, return to the Burger King in three different flavors, including Shroom n’ Swiss, Bacon Melt and Classic Melt.
Because both orientations represent the same data, it is possible to convert a row-oriented dataset to a column-oriented dataset and vice-versa at the expense of compute. In particular, advanced query engines often leverage each orientation's advantages, and convert from one orientation to the other as part of their execution.