drop_time.py

train_df['year_month'] = train_df['time1'].apply(lambda t: 100 * t.year + t.month).values.reshape(-1, 1)
test_df['year_month'] = test_df['time1'].apply(lambda t: 100 * t.year + t.month).values.reshape(-1, 1)
print(train_df['year_month'].loc[y_train == 1].describe())

train_df = train_df[(train_df['year_month'] >= 201401)]#201309
print(train_df['year_month'].loc[y_train == 1].describe())


### Output

count      1241.000000
mean     201369.155520
std          44.192148
min      201303.000000
25%      201311.000000
50%      201402.000000
75%      201402.000000
max      201402.000000
Name: year_month, dtype: float64
count      1241.000000
mean     201402.705077
std           0.950407
min      201401.000000
25%      201402.000000
50%      201403.000000
75%      201403.000000
max      201404.000000
Name: year_month, dtype: float64