## Apply condition row by row in array

remove rows from numpy array based on condition

numpy select rows by multiple conditions

numpy where

numpy any

numpy delete

numpy select rows by column value

np.where multiple conditions

I'm new to python so this might be an easy question. Sorry if that's the case!

I have an array `a`

and I would like to known which values each row of `a`

are lower or equal to a value in the same row as `b`

.

a = np.array([[8,1,7],[4,3,9],[5,2,6]]) b = np.array([[7],[4],[6]])

The resulting array should be:

c = np.array([[False,True,True],[True,True,False],[True,True,True]])

I've tried:

np.where((a <= b), True, False)

and

np.apply_along_axis(np.where((a <= b), True, False),1,a)

None of them work.

How about the following:

s = np.where((a-b) < 0, True, False) print(a[s])

Would this help you?

**NumPy: Extract or delete elements, rows and columns that satisfy ,** Extract elements that satisfy the conditions Extract rows and columns that satisfy the rows and columns that satisfy the condition from the NumPy array that meet the condition, you can use ndarray[conditional expression] . In the ROWS function, Array can be an array, an array formula, or a reference to a single contiguous group of cells. Example 2. If we wish to get the address of the first cell in a named range, we can use the ADDRESS function together with the ROW and COLUMN functions. The formula to use is: =ADDRESS(ROW(B5:D5)+ROWS(B5:D5)-1,COLUMN(B5:D5)+ROWS

Numpy is great for doing element wise logical operation!

In this example a simple`a<=b`

should do the job. You can learn more about it here https://jakevdp.github.io/PythonDataScienceHandbook/02.06-boolean-arrays-and-masks.html

**numpy.where(): Process elements depending on conditions,** Using numpy.where(), elements of the NumPy array ndarray that satisfy the Related: NumPy: Extract or delete elements, rows and columns that satisfy in () and & or | is used, processing is applied to multiple conditions. Original Dataframe a b c 0 222 34 23 1 333 31 11 2 444 16 21 3 555 32 22 4 666 33 27 5 777 35 11 ***** Apply a lambda function to each row or each column in Dataframe ***** *** Apply a lambda function to each column in Dataframe *** Modified Dataframe by applying lambda function on each column: a b c 0 232 44 33 1 343 41 21 2 454 26 31 3 565 42

Sorry guys,

The problem was that the array b was not fully defined: shape was (x,). I reshaped it to be (x,1) and then everything worked.

Thank you all!

**Python Numpy : Select elements or indices by conditions from ,** Let's apply < operator on above created numpy array i.e. print('Select elements from Numpy Array based on conditions') Numpy : Select rows / columns by index from a 2D Numpy Array | Multi Dimension � numpy.append()� In using_apply, we does apply on each row, then access each column value separately, whereas in the other function, we only pass in the relevant columns, and unpack the row to get all columns at

c = [lambda x, y: x <= y] c(a,b)

**Extract all rows from a range that meet criteria in one column,** conditions [Array formula]; Extract all rows from a range based on range critera Lets filter records based on conditions applied to column D. Select the cell in the first row for that column in the table. In my case, that would be E6. On the Home tab of the Ribbon, select the Conditional Formatting drop-down and click on Manage Rules…. That will bring up the Conditional Formatting Rules Manager window. Click on New Rule. This will open the New Formatting Rule window.

**4 Subsetting,** To illustrate, I'll apply [ to 1D atomic vectors, and then show how this generalises to Each row in the matrix specifies the location of one value, and each column corresponds to 4.5.7 Selecting rows based on a condition (logical subsetting). The ROW function returns the row number for a cell or range. For example, =ROW(C3) returns 3, since C3 is the third row in the spreadsheet. When no reference is provided, ROW returns the row number of the cell which contains the formula. If you provide a range as a reference, ROW will return the number of the first row in the range. Examples

**remove rows from a matrix on a specific condition,** and I want to delete the rows of this matrix when the elements of the 5th column are equal to 0, so I will be left with the first and last row as an outcome: A= [ 5 3 3 � Selecting rows based on multiple column conditions using '&' operator. Code #1 : Selecting all the rows from the given dataframe in which ‘Age’ is equal to 21 and ‘Stream’ is present in the options list using basic method.

**Find rows and columns in matrix that meet a condition, fast ,** Learn more about find, find row and column in matrix, bsxfun, ismember. to use logical indexing, or is it strictly required to get the row and column indices ? Given numpy array, the task is to add rows/columns basis on requirements to numpy array. Let’s see a few examples of this problem. Method #1: Using np.hstack() method

##### Comments

- What about
`a <= b`

? - This doesn't work. It returns: ValueError: operands could not be broadcast together with shapes (14,31) (14,)