Filter data without altering date format

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I have a large dataframes with date column like this one:

import pandas as pd

cars = {'Date': ['2020-09-11','2020-10-11','2021-01-12','2020-01-03', '2021-02-01'],
        'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4','Mercedes'],
        'Price': [22000,25000,27000,35000,45000]
        }

df = pd.DataFrame(cars, columns = ['Date','Brand', 'Price'])

print (df)

Now the date column is an object and I want to filter out only 2021 data. Originally I am changing date column from object to datetime but this approach wont work if I try to convert that data to json format. This what I tried

 yest_date = date(2021, 1, 01) - timedelta(days=1)
 yest_date = yest_date.strftime("%Y-%m-%d")

cur_date = date(2021, 2, 05)
cur_date = cur_date.strftime("%Y-%m-%d")

df['Date] = pd.to_datetime(df['Date'])

if yest_date is not None:
   yest_data = df.loc[df['Date'] <= pd.to_datetime(yest_date), :]

if cur_date is not None:
   cur_data = data2.loc[data2[project.date_var] <= pd.to_datetime(cur_date), :]

My question not is, can I be able to filter out values between 2 dates without converting the date object date to datetime? If so How can filter date between 2 dates without changing date structure to datetime.

pandas python
1 Answers

Treat the Date column as string and do a substring match with year, like 2021

Here I am using str.contains

import pandas as pd

cars = {'Date': ['2020-09-11','2020-10-11','2021-01-12','2020-01-03', '2021-02-01'],
        'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4','Mercedes'],
        'Price': [22000,25000,27000,35000,45000]
        }

df = pd.DataFrame(cars, columns = ['Date','Brand', 'Price'])
df = df[df['Date'].str.contains("2021")]
df.head()


Date    Brand   Price
2   2021-01-12  Ford Focus  27000
4   2021-02-01  Mercedes    45000
user_71823500
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