for every k elements in a dataframe, I want to add a number to a list. for example, if k = 3 my list would be [0,0,0,1,1,1,2,2,2,3,3,3,…]
Here is what I tried:
<code>
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
# Create X & y
X, y = load_breast_cancer(return_X_y = True, as_frame=True)
X = np.array(X)
y = np.array(y)
kay = 5
model = LogisticRegression(max_iter = 10000)
random_seed = 18
ids = 0
fold_ids = []
k = len(X)/kay
r = len(X) % kay
for i in range(X):
while i <= k:
print(i)
fold_ids.append(i)
ids += 1
k = k * (ids + 1)
print(fold_ids)
</code>
<code>
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
# Create X & y
X, y = load_breast_cancer(return_X_y = True, as_frame=True)
X = np.array(X)
y = np.array(y)
kay = 5
model = LogisticRegression(max_iter = 10000)
random_seed = 18
ids = 0
fold_ids = []
k = len(X)/kay
r = len(X) % kay
for i in range(X):
while i <= k:
print(i)
fold_ids.append(i)
ids += 1
k = k * (ids + 1)
print(fold_ids)
</code>
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
# Create X & y
X, y = load_breast_cancer(return_X_y = True, as_frame=True)
X = np.array(X)
y = np.array(y)
kay = 5
model = LogisticRegression(max_iter = 10000)
random_seed = 18
ids = 0
fold_ids = []
k = len(X)/kay
r = len(X) % kay
for i in range(X):
while i <= k:
print(i)
fold_ids.append(i)
ids += 1
k = k * (ids + 1)
print(fold_ids)
It returned an error on the “for i in range(X):” line saying “TypeError: only integer scalar arrays can be converted to a scalar index”,
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