具有2D数组位置的Python-1D索引链接

Question

Introduction

Sometimes, I want to get the value of an 2-d array at a random location.
For example, there is an array data in the shape of (20,20). There is a random number-pair (5,5). Then, I get the data[5,5] as my target value.

On the purpose of using genetic algorithm. I want to get the samples from an 2-d array as several individuals. So, I want to generate an linked table which connect an 1d value to 2d position.

My attempt

## data was the 2-d array in the shape of 20x20
data = np.random.randint(0,1000,400)
data = data.reshape(20,20)

## direction was my linked table
direction = {"Indice":[],"X":[],"Y":[]}
k = 0
for i in range(0,data.shape[0],1):
    for j in range(0,data.shape[1],1):
        k+=1
        direction["Indice"].append(k)
        direction["X"].append(j)
        direction["Y"].append(i)
direction = pd.DataFrame(direction)   

## generate an random int and connect with the 2-d value.  
loc = np.random.randint(0,400)  
XX = np.array(direction[direction.Indice == loc ].X)
YY = np.array(direction[direction.Indice == loc ].Y)
target_value = data[YY,XX]

My question

Are there any neat way to achieve my attempt?
Any advice would be appreciate!

Answer 1

You could use np.ravel to make data 1-dimensional, then index it using the flat index loc :

target_value = data.ravel()[loc-1]

---

Or, if you want XX and YY , perhaps you are looking for np.unravel_index . It maps a flat index or an array of flat indices to a tuple of coordinates.

For example, instead of building the direction DataFrame, you could use

np.unravel_index(loc-1, data.shape)

instead of

XX = np.array(direction[direction.Indice == loc ].X)
YY = np.array(direction[direction.Indice == loc ].Y)

Then you could define target_value as :

target_value = data[np.unravel_index(loc-1, data.shape)]

---

Alternatively, to simply get a random value from the 2D array data , you could use

target_value = np.random.choice(data.flat)

Or to get N random values, use

target_values = np.random.choice(data.flat, size=(N,))

---

Why the minus one in loc-1 :

In your original code, the direction['Indice'] column uses k values which start at 1, not 0. So when loc equals 1, the 0th-indexed row of direction is selected. I used loc-1 to make

target_value = data[np.unravel_index(loc-1, data.shape)]

return the same result that

XX = np.array(direction[direction.Indice == loc ].X)
YY = np.array(direction[direction.Indice == loc ].Y)
target_value = data[YY,XX]

returns. Note however, that if loc equals 0, then np.unravel_index(-1, data.shape) raises a ValueError , while your original code would return an empty array for target_value .