The multinomial distribution represents the probability of observing multiple possible outcomes in a single trial. By selecting these specific rows, we can analyze and draw conclusions about the underlying probability distribution. This article involves choosing specific rows from a dataset that are generated from a multinomial distribution with ‘n’ degrees, which can be done by using the following methods:

#### 1. Using “np.all()”:

Here, the `np.all()`

function is used to check if all elements of each row of `X`

are integers, by checking if the modulus of each element with `1`

is `zero`

. The `axis=1`

argument indicates that we want to apply this check to each row. The `&`

operator is used to perform an element-wise “and” operation between the two boolean arrays: the one obtained from the integer check, and the one obtained from the sum check. Finally, we print the rows of `X`

that satisfy both conditions by indexing `X`

with the boolean array `M`

.

#### 2. Using “np.sum()” and “np.astype()”:

This code first converts all values in `X`

to integers using the `np.astype()`

method and checks if all values in each row are integers by comparing with the original `X`

using `==`

. This is done using the `np.sum()`

method along the rows (`axis=1`

) and comparing the result with `X.shape[1]`

. Next, the code checks if the sum of each row of `X`

is equal to `n`

using the `np.sum()`

method along the rows (`axis=1`

) and compares the result with `n`

. Finally, the code combines the two conditions using logical AND (`np.logical_and()`

) and prints the rows of `X`

that satisfy both conditions.