Объединить 3 numpy.ndarray в один pandas.DataFrame

...
def fun(x):
    tmp = x.value_counts()
    if len(tmp) > 1 and tmp.iloc[0] == tmp.iloc[1]:
        return 'random'
    else:
        return x.mode()
 
df['t4'] = df.apply(fun, axis=1)
...

import numpy as np
import pandas as pd
 
n = 10
t1 = np.random.randint(1, 4, size = (n, 1))
t2 = np.random.randint(1, 4, size = (n, 1))
t3 = np.random.randint(1, 4, size = (n, 1))
 
df = pd.DataFrame(np.concatenate((t1, t2, t3), axis=1), columns=list('ABC'))
df['Mode'] = df.mode(axis=1)[0]
print(df)

import numpy as np
import pandas as pd
 
n = 10
t1 = np.random.randint(1, 4, size = (n, 1))
t2 = np.random.randint(1, 4, size = (n, 1))
t3 = np.random.randint(1, 4, size = (n, 1))
 
df = pd.DataFrame(np.concatenate((t1, t2, t3), axis=1), columns=list('ABC'))
df1 = df.mode(axis=1)
df['mode'] = np.where(df1[2].isna(), df1[0], 'random')
print(df)

import numpy as np
import pandas as pd
 
n = 10
t1 = np.random.randint(1, 4, size = (n, 1))
t2 = np.random.randint(1, 4, size = (n, 1))
t3 = np.random.randint(1, 4, size = (n, 1))
 
df = pd.DataFrame(np.concatenate((t1, t2, t3), axis=1), columns=list('ABC'))
df1 = df.mode(axis=1)
df['Mode'] = df1[0]
# df['Mode'][df1.count(axis=1)>1] = 'random'  # - выдает предупреждение
df['Mode'].loc[df1.count(axis=1)>1] = 'random'  # - тоже выдает предупреждение
print(df)

type(t1)
type(predicted_rfc)
numpy.ndarray
print(t1)

t1 = t1.reshape(-1,1)
t2 = t2.reshape(-1,1)
t3 = t3.reshape(-1,1)
df = pd.DataFrame(np.concatenate((t1, t2, t3), axis=1), columns=list('ABC'))

df['Mode'] = df.apply(lambda x: np.random.choice(x.mode()), axis = 1)

predicted_rfc = predicted_rfc.reshape(-1, 1)
pred_mlp2 = pred_mlp2.reshape(-1, 1)
logreg_pred = logreg_pred.reshape(-1, 1)
 
pred_df = pd.DataFrame(np.concatenate((predicted_rfc, pred_mlp2, logreg_pred), axis=1), columns=list('ABC'))
pred_df1 = pred_df.mode(axis=1)
pred_df['target'] = np.where(pred_df1[2].isna(), pred_df1[0], 'randome')
pred_df['target'] = df.apply(lambda x: np.random.choice(x.mode()), axis = 1)
pred_df

df['target'] = df[['A','B','C']].apply(lambda x: np.random.choice(x.mode()), axis = 1)