date format in Python

import pandas as pd
import numpy as np
from dateutil import parser


df = pd.read_csv('movies_complete.csv', parse_dates = ['release_date'])

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all_ads_until_10_Oct['Date'] = [parser.parse(x) for x in all_ads_until_10_Oct['Date']]
all_ads_until_10_Oct.head()

pd.to_datetime(cars['model_year'], format= '%Y')

cars['model_year'].astype('datetime64[ns]')

#####################################################
# convert year (string) into full date
df_auto = df_auto.replace(r'^\s*$', np.nan, regex=True)
df_auto = df_auto.dropna(subset = ["Year Manifacture"])
df_auto['Year Manifacture'] = df_auto['Year Manifacture'].str.extract(r'(\d+)').astype(int)
df_auto["Year Manifacture1"] = df_auto["Year Manifacture"].apply(lambda row: f'12/31/{row}')
# and after that you can parse
df_auto["Year Manifacture_new"] = [parser.parse(x) for x in df_auto["Year Manifacture_new"]]

#####################################################
# How to extract the months from date
month_quantity_2017 = df_category_date_quantity.loc[mask1 & mask2].groupby(df_category_date_quantity.TransactionDate.dt.month).agg({'Quantity': 'sum'}).reset_index()
month_quantity_2017