Trying to get year not the entire year & time

[mbrown009]

I am trying to convert the string into the date format

Here is my code

#!/usr/bin/env python

# make sure to install these packages before running:
# pip install pandas
# pip install sodapy

import pandas as pd
from sodapy import Socrata
import datetime as dt

# Unauthenticated client only works with public data sets. Note 'None'
# in place of application token, and no username or password:
client = Socrata("opendata.maryland.gov", "##KEY##")

# First 2000 results, returned as JSON from API / converted to Python list of
# dictionaries by sodapy.
results = client.get("q4mw-f34p", limit=2000)

# Convert to pandas DataFrame
results_df = pd.DataFrame.from_records(results)

#creating dataframe + adding first field from the originating data frame
cleandata = results_df[['legal_description_line_2_mdp_field_legal2_sdat_field_18']].copy()

#changing column name in data frame
cleandata.rename(columns = {'legal_description_line_2_mdp_field_legal2_sdat_field_18':'address'}, inplace = True)

#copying over columns from originating data frame
cleandata['accountnumber'] = results_df['record_key_account_number_sdat_field_3']
cleandata['housetype'] = results_df['mdp_street_address_type_code_mdp_field_resityp']
cleandata['landuse'] = results_df['land_use_code_mdp_field_lu_desclu_sdat_field_50']
cleandata['exemptclass'] = results_df['exempt_class_mdp_field_exclass_descexcl_sdat_field_49']
cleandata['assessmentyear'] = results_df['assessment_cycle_year_sdat_field_399']
cleandata['currentyeartotalassessment'] = results_df['current_assessment_year_total_phase_in_value_sdat_field_171']
cleandata['owneroccupancycode'] = results_df['record_key_owner_occupancy_code_mdp_field_ooi_sdat_field_6']
cleandata['homesteadcreditqualificationcode'] = results_df['homestead_qualification_code_mdp_field_homqlcod_sdat_field_259']
cleandata['homesteadqualificationdate'] = results_df['homestead_qualification_date_mdp_field_homqldat_sdat_field_260']
cleandata['yearbuilt'] = results_df['c_a_m_a_system_data_year_built_yyyy_mdp_field_yearblt_sdat_field_235']
cleandata['datepurchased'] = results_df['sales_segment_1_transfer_date_yyyy_mm_dd_mdp_field_tradate_sdat_field_89']
cleandata['zoning'] = results_df['zoning_code_mdp_field_zoning_sdat_field_45']

cleandata['assessmentyear'] = [dt.datetime.strptime(x, '%Y')
                for x in cleandata['assessmentyear']]


#Saving to CSV
cleandata.to_csv('cleandata.csv')

#printing data frame to screen
cleandata

The output for assessmentyear is 2023-01-01 00:00:00. For this instance I just want it to be 2023.

Any help would be great!

[buran]

Why do you convert what is clearly year as string to a datetime object?

cleandata['assessmentyear'] = [dt.datetime.strptime(x, '%Y') for x in cleandata['assessmentyear']]

i.e. datetime object always has all components. It's a representation to show just the year.
If you want a number consider

cleandata['assessmentyear'] = [dt.datetime.strptime(x, '%Y').year for x in cleandata['assessmentyear']]

Note, I didn't test the code above

[snippsat]

The output for assessmentyear is 2023-01-01 00:00:00. For this instance I just want it to be 2023.

First convert the date to pandas datetime64,then can covert to year only.
Example.

import pandas as pd

data = {
    'date': ['2023-01-02 00:00:01', '2023-01-03 00:00:02', '2023-01-04 00:00:02'],
    'country': ['USA', 'USA', 'USA'],
    'population': [328000000, 328000000, 328000000],
}

df = pd.DataFrame(data)
# Convert to pandas datetime
df['date'] = pd.to_datetime(df['date'])

>>> df
                 date country  population
0 2023-01-02 00:00:01     USA   328000000
1 2023-01-03 00:00:02     USA   328000000
2 2023-01-04 00:00:02     USA   328000000

# Always check this,see that date type is now ok
>>> df.dtypes
date          datetime64[ns]
country               object
population             int64
dtype: object

# Convert to year only
>>> df['date'] = df['date'].dt.year
>>> df
   date country  population
0  2023     USA   328000000
1  2023     USA   328000000
2  2023     USA   328000000