sklearn-pandas | Pandas integration with sklearn | Machine Learning library

d = {'id': [1,2,3], 
     'human_id': ["('apples', '2022-12-04', 'a5ted')", 
                  "('bananas', '2012-2-14')",
                  "('2012-2-14', 'reda21', 'ss')"
                 ]}

df = pd.DataFrame(data=d)

list_human_id = tu

data={"col1":[1,2,3,5], "col_2":[6,7,8,9], "col_3":[10,11,12,14], "col_4":[7,8,9,10]}

from itertools import chain
pd.DataFrame({'col': chain.from_iterable(data.values())})

df = pd.DataFrame.from_dict(data)
pd.Seri

out = pd.DataFrame.from_dict(data).melt().drop(['variable'],axis=1)
Out[109]: 
    value
0       1
1       2
2       3
3       5
4       6
5       7
6       8
7       9
8      10
9      11
10     12
11     14
12      7
13      8
14      9

X = dataset.drop('Class', axis=1)
y = dataset['Class']
# assuming X has multiple columns and y only one column
X = X.values
y = y['Class'].tolist()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)

df = df.groupby(['location']).agg({'new_deaths': sum, 'population': max})
df['rate_death'] = df['new_deaths'] / df['population'] * 100

             new_deaths  population  rate_death
location
Afghanistan          1