What is ismember?
ismember
is a Python library that checks whether the elements in X is present in Y. Or in other words, we can check if a particular element belongs to an array or not by using ismember() function. The result is in the form of logical 1 (True) or logical 0 (False).
Input
The input arrays can be one of the underneath types, and if the rows=True
parameter is specified in the syntax, then the input arrays should have the same number of columns.
logical
numeric
character
string
datetime
categorical
Each of these input arrays can be in the form of:
Pandas DataFrame
Numpy Array
List
Output
Lx = ismember(X, Y)
: This checks whether the elements in X is present in Y. If the elements are present, then it returns 1(True) else it returns 0(False). If X and Y are in the form of tables or timetables, then it returns the logical value for every row present and if X and Y are in the form of timetables, then row times are considered. The resultant value Lx is a column vector.
Lx = ismember (X, Y, "rows")
: This syntax considers the rows of X and Y as single entities and determines the logical values which are in the form of 1 and 0. If the values are present, then it returns logical 1 (True) else it returns logical 0 (False). The rows option is not valid if cell arrays are used, provided that the input array is categorical or datetime array.
Lx, LocationY = ismember (____)
: Here Location Y is used to find the lowest index values present in Y if the values present in X are a member of Y. If the value is 0, then it indicates that the elements present in X are not a part of Y. If there are rows option specified in the syntax, then LocationY contains the lowest index of Y, provided that rows in X should be a part of rows in Y. If the value is 0, then X is not in the row of Y. If X and Y are in the form of tables or timetables, then LocationY contains the lowest index of Y, provided that rows in X should be a part of rows in Y. If the value is 0, then X is not in the row of Y.
[1] https://www.datacamp.com/community/tutorials/pickle-python-tutorial