Clean up for HCMST_2017-2022.csv

Loading CSV

Load the necessary libraries for the clean-up, as well as the provided dataset

import pandas as pd

df = pd.read_csv("../data/raw/how-couples-meet-and-stay-together/HCMST_2017-2022.csv")
df.head()

	caseid_new	w3_Weight	w3_Weight_LGB	w3_combo_weight	w3_attrition_adj_weight	w2_weight_genpop	w2_weight_LGB	w2_combo_weight	w2_attrition_adj_weights	w1_weight_combo	w1_weight_combo_freqwt	w3_xpartner_type	w3_xlast_contact_year	w3_xcohab	w3_xsamesex	w3_xlast_contact_mo	w3_xnamep_present	w3_duration	w3_gen_pop_sample	w3_ppage	w3_ppagecat	w3_ppagect4	w3_ppeduc	w3_ppeducat	w3_ppethm	w3_ppgender	w3_pphhhead	w3_pphhsize	w3_pphouse	w3_ppincimp	w3_real_inc	w3_log_real_inc	w3_ppmarit	w3_ppmsacat	w3_PPREG4	w3_ppreg9	w3_pprent	w3_PPT01	w3_PPT25	w3_PPT612	w3_total_kids_lt13	w3_PPT1317	w3_PPT18OV	w3_ppwork	w3_surveyed	w3_section	w3_partner_type	w3_married	w3_otherdate	w3_coronavirus_effect_combo	w3_corona_effect_combo_reversed	w3_live_w_partner	w3_same_sex_couple	w3_marriage_dp	w3_mar_dp_why_words	w3_mar_dp_why_len	w3_mar_dp_why_sum	w3_rel_qual	w3_Q23	w3_sex_frequency	w3_weekly_sex_frequency	w3_how_many	w3_how_many_corona	w3_p_monogamy	w3_monogamy	w3_past_partners_gender_1	w3_past_partners_gender_2	w3_past_partners_gender_3	w3_move_in_yr	w3_move_in_mo	w3_when_married_yr	w3_when_married_month	w3_why_not_cohab	w3_how_often	w3_how_communicate_1	w3_how_communicate_2	w3_how_communicate_3	w3_how_communicate_4	w3_how_communicate_5	w3_flirt	w3_flirt_other	w3_fight	w3_shelter	w3_shelter_4	w3_nonshelter	w3_shelter_combo_months	w3_WFH	w3_more_WFH	w3_WFH_future	w3_myhealth	w3_myhealth_why_1	w3_myhealth_why_2	w3_subject_had_COVID	w3_corona_vaccine	w3_partner_corona_vaccine	w3_vaccine_same	w3_corona_test	w3_parthealth	w3_parthealth_why_1	w3_parthealth_why_2	w3_partner_had_COVID	w3_partcorona_test	w3_pandemic_income	w3_pandemic_income_collapsed	w3_covid_masking	w3_COVID_agreement	w3_covid_complete_agree	w3_government_response	w3_relatives	w3_relationship_end_combo	w3_who_breakup	w3_women_wanted_breakup	w3_partner_type_for_het_breakups	w3_breakup_year	w3_breakup_month	w3_partner_passaway_year	w3_partner_passaway_month	w3_new_relationship	w3_coronavirus_effect_dating	w3_corona_longing	w3_app_use	w3_Q4	w3_Q5	w3_Q6A	w3_Q6B	w3_Q9	w3_Q10	w3_Q11	w3_Q12	w3_Q20	w3_how_far_apart	w3_Q21A_year	w3_Q21A_month	w3_Q21B_year	w3_Q21B_month	w3_Q21C_year	w3_Q21C_month	w3_Q21D_year	w3_Q21D_month	w3_Q21E_year	w3_Q21E_month	w3_q24_length	w3_q24_R_friend	w3_q24_P_friend	w3_q24_I_friend	w3_q24_R_cowork	w3_q24_R_family	w3_q24_R_sig_other	w3_q24_R_neighbor	w3_q24_P_cowork	w3_q24_P_family	w3_q24_P_sig_other	w3_q24_P_neighbor	w3_q24_I_cowork	w3_q24_I_family	w3_q24_I_sig_other	w3_q24_I_neighbor	w3_q24_school	w3_q24_college	w3_q24_mil	w3_q24_church	w3_q24_vol_org	w3_q24_customer	w3_q24_bar_restaurant	w3_q24_party	w3_q24_internet_other	w3_q24_internet_dating	w3_q24_internet_soc_network	w3_q24_internet_game	w3_q24_internet_chat	w3_q24_internet_org	w3_q24_public	w3_q24_blind_date	w3_q24_vacation	w3_q24_singles_serve_nonint	w3_q24_business_trip	w3_q24_work_neighbors	w3_q24_summary_all_codes	w3_q24_met_online	w3_q24_met_through_friend	w3_q24_met_through_family	w3_q24_met_through_as_nghbrs	w3_q24_met_as_through_cowork	w3_Q25	w3_Q26	w3_Q27	w3_Q28	w3_Q32	w3_friend_connect_1	w3_friend_connect_2	w3_friend_connect_3	w3_friend_connect_4	w3_partner_source	w3_month_rel_started	w3_month_interview	w3_relationship_duration_mos	w3_relationship_duration_yrs	w3_breakup_source	w2_month_of_survey	w2_xpartner_type	xpartner_type_cohab	w2_xcohab	w2_xpppa_lgb_num	w2_duration	w2_ppage	w2_ppagecat	w2_ppagect4	w2_ppeduc	w2_ppeducat	w2_ppethm	w2_ppgender	w2_pphhhead	w2_pphhsize	w2_pphouse	w2_ppincimp	w2_income_real	w2_log_real_inc	w2_ppmsacat	w2_PPREG4	w2_ppreg9	w2_pprent	w2_PPT612	w2_kids_lt13	w2_total_kids_lt13	w2_PPT1317	w2_PPT18OV	w2_ppwork	w2_surveyed	w2_coronavirus_effect_combo	w2_corona_effect_combo_reversed	w2_pandemic_income_combo	w2_app_use_combo	w2_rel_qual_combo	w2_rel_qual_reduced	w2_partner_type	w2_married	w2_partner_type_cohab	w2_live_w_partner	w2_Q20	w2_same_sex_couple	w2_lesbian	w2_cohab	w2_section	w2_Q21B_yr	w2_q24_length	w2_q24_R_friend	w2_q24_P_friend	w2_q24_I_friend	w2_q24_R_cowork	w2_q24_R_family	w2_q24_R_sig_other	w2_q24_R_neighbor	w2_q24_P_cowork	w2_q24_P_family	w2_q24_P_sig_other	w2_q24_P_neighbor	w2_q24_I_cowork	w2_q24_I_family	w2_q24_I_sig_other	w2_q24_I_neighbor	w2_q24_school	w2_q24_college	w2_q24_mil	w2_q24_church	w2_q24_vol_org	w2_q24_customer	w2_q24_bar_restaurant	w2_q24_party	w2_q24_internet_other	w2_q24_internet_dating	w2_q24_internet_soc_network	w2_q24_internet_game	w2_q24_internet_chat	w2_q24_internet_org	w2_q24_public	w2_q24_blind_date	w2_q24_vacation	w2_q24_singles_serve_nonint	w2_q24_business_trip	w2_q24_work_neighbors	w2_q24_summary_all_codes_test	w2_q24_met_through_friend	w2_q24_met_through_family	w2_q24_met_through_as_nghbrs	w2_q24_met_as_through_cowork	w2_q24_met_online	w2_q24_answered	w2_q24_coworkers	w2_who_breakup_combo	yr_breakup_combo	w2_otherdate_combo	w2_how_many_combo	w2_how_many_corona_combo	w2_number_people_met	w2_corona_effect_dating_combo	w2_shelter_combo_months	w2_corona_longing_combo	w2_breakup_year	w2_breakup_month	w2_covid_breakup	w2_breakup_month_tm	w2_yr_relationship_started	w2_relationship_start_calmonth	w2_new_relationship	w2_q21a_year	w2_q21a_month	w2_q21b_year	w2_q21b_month	w2_q21c_year	w2_q21c_month	w2_q21d_year	w2_q21d_month	w2_q21e_year	w2_q21e_month	w2_met_month	w2_sex_frequency	w2_weekly_sex_frequency	w2_relationship_duration	w2_relatives	w2_women_wanted_breakup	w2_partner_type_for_het_breakups	w2_Q23_old	w2_Q23_new	w2_p_monogamy	w2_Q21A_1	w2_Q21A_2	w2_Q21B_1	w2_Q21B_2	w2_move_in_yr	w2_move_in_mo	w2_move_in_2020	w2_separation_timing_1_1	w2_separation_timing_1_2	w2_separation_timing_2020	w2_how_far_apart	w2_why_not_cohab	w2_how_often	w2_how_communicate_1	w2_how_communicate_2	w2_how_communicate_3	w2_how_communicate_4	w2_how_communicate_5	w2_flirt	w2_flirt_other	w2_fight	w2_nonshelter	w2_myhealth	w2_myhealth_why_1	w2_myhealth_why_2	w2_corona_test	w2_parthealth	w2_parthealth_why_1	w2_parthealth_why_2	w2_partcorona_test	w2_partcorona_test_new	w2_roommates	w2_roommates_who_1	w2_roommates_who_2	w2_roommates_who_3	w2_roommates_who_4	w2_exroommates	w2_relationship_end	w2_relationship_end_yr	w2_relationship_end_mo	w2_partner_passaway_yr	w2_partner_passaway_month	w2_Q4	w2_Q5	w2_Q6A	w2_Q6B	w2_Q9	w2_Q10	w2_Q11	w2_Q12	w2_live_together_2020	w2_relationship_progress_2020	w2_Q25	w2_Q26	w2_Q27	w2_Q28	w2_Q32	w2_friend_connect_1	w2_friend_connect_2	w2_friend_connect_3	w2_friend_connect_4	w1_duration	w1_consent	w1_partyid7	w1_PERSNET_hom	w1_ppc10017	w1_ppc21310	w1_ppp20071	w1_ppp20072	w1_ppage	w1_ppagecat	w1_ppagect4	w1_ppeduc	w1_ppeducat	w1_ppethm	w1_ppRace_1	w1_ppRace_3	w1_pprace1	w1_pprace3	w1_ppgender	w1_pphhhead	w1_pphhsize	w1_pphouse	w1_ppincimp	w1_ppincimp_cat	w1_ppmarit	w1_ppmsacat	w1_PPREG4	w1_ppreg9	w1_pprent	w1_PPT612	w1_PPT18OV	w1_ppwork	w1_xlgb	w1_section	w1_partnership_status	w1_partnership_status_cohab	w1_female	w1_sex_frequency	w1_otherdate	w1_how_many	w1_how_meet_Refused	w1_otherdate_app	w1_how_many_app	w1_took_the_survey	w1_q4	w1_q5	w1_q6a	w1_q6b	w1_q9	w1_q10	w1_q11	w1_q12	w1_q14	w1_q15a1	w1_q15a2	w1_q15a4	w1_q15a5	w1_q15a7	w1_q16	w1_q17	w1_attraction	w1_q19	w1_q20	w1_q21a_year	w1_q21a_month	w1_q21b_year	w1_q21b_month	w1_q21c_year	w1_q21c_month	w1_q21c_month_flag	w1_q21d_year	w1_q21d_month	w1_q21e_year	w1_q21e_month	w1_q21f_year	w1_q21f_month	w1_identity_all	w1_identity_all_modified	w1_outness_all	w1_outness_timing_all	w1_q23	w1_q24_length	w1_q24_R_cowork	w1_q24_R_friend	w1_q24_R_family	w1_q24_P_cowork	w1_q24_P_friend	w1_q24_mil	w1_q24_church	w1_q24_customer	w1_q24_bar_restaurant	w1_q24_party	w1_q24_public	w1_q24_summary_all_codes	w1_q24_summary_all_codes_test	w1_q24_met_through_family	w1_q24_met_through_friend	w1_q24_met_as_through_cowork	w1_q25	w1_q26	w1_q27	w1_q28	w1_friend_connect_1_all	w1_friend_connect_2_all	w1_friend_connect_3_all	w1_friend_connect_4_all	w1_q32	w1_q34	w1_q34_reduced	w1_otherdate_all	w1_how_many_all	w1_otherdate_app_all	w1_how_many_app_all	w1_q40	w1_married	w1_relate_duration_in2017_years	w1_number_people_met_app	w1_q40_phone_app_incl_other	w1_year_fraction_met	w1_year_fraction_relstart	w1_age_when_met	w1_time_from_met_to_rel	w1_year_fraction_first_cohab	w1_time_from_rel_to_cohab	w1_subject_race	w1_interracial_5cat	w1_partner_mother_yrsed	w1_subject_mother_yrsed	w1_partner_yrsed	w1_subject_yrsed	w1_how_met_online	w1_met_after_2009	w1_max_relation_status	w1_smart_phone	w1_days_access_internet_perwk	w1_year_met_from_1990	w1_ever_marriedq21	change_in_rel_quality_w1w2	w1_relationship_start_calmonth	w1_met_month	w1_weekly_sex_frequency	w1w2_sex_freq_diff	p17_core_emp	p17_ppcmdate2017	p17_pppa_lgb	p17_pppa1634	p17_pppa1635	p17_pppa1648	p17_ppp20072	p17_ppp20071	p17_ppp10209	p17_ppp10212	p17_ppp10213	p17_pppadate2017	p17_ppfs0596	p17_ppfs1480	p17_ppfs1482	p17_ppfsdate2017	p18_core_emp	p18_ppmarit	p18_ppincimp	p18_ppcmdate2018	p18_pppa_lgb	p18_pppa1634	p18_pppa1635	p18_pppa1648	p18_ppp20072	p18_ppp20071	p18_ppp10206	p18_ppp10207	p18_ppp10209	p18_ppp10210	p18_ppp10211	p18_ppp10212	p18_ppp10213	p18_pppa1704	p18_pppa1802	p18_pppadate2018	p18_ppfs0596	p18_ppfs1480	p18_ppfs1482	p18_ppfsdate2018	p19_core_emp	p19_ppmarit	p19_ppincimp	p19_ppcmdate2019	p19_pppa_lgb	p19_pppa1634	p19_pppa1902	p19_pppa1903	p19_pppa1648	p19_ppp20072	p19_ppp20071	p19_ppp10208	p19_ppp10212	p19_ppp10213	p19_pppadate2019	p19_ppfs0596	p19_ppfs1480	p19_ppfs1482	p19_ppfsdate2019	p19_ppsi1927	p19_ppsi1928	p19_ppsi1929	p19_ppsi1930	p19_ppsi1931	p19_ppsi1932	p19_ppsi1933	p19_ppsi1934	p19_ppsi1935	p19_ppsi1936	p19_ppsidate2019	p20_core_emp	p20_ppmarit	p20_ppcmdate2020	p20_ppfs0596	p20_ppfs1480	p20_ppfs1482	p20_ppfsdate2020	p20_ppsi1927	p20_ppsi1928	p20_ppsi1929	p20_ppsi1930	p20_ppsi1931	p20_ppsi1932	p20_ppsi1933	p20_ppsi1934	p20_ppsi1935	p20_ppsi1936	p20_ppsidate2020	p20_ppp10206	p20_ppp10210	p20_ppp10211	p20_ppp10212	p20_ppp10213	p20_ppp1date2020	p20_pppa1634	p20_pppa1902	p20_pppa1903	p20_pppa1648	p20_ppp20072	p20_ppp20071	p20_ppp2date2020
0	53001	0.4422	NaN	0.495308	0.400185	0.3856	NaN	0.437670	0.380351	0.426861	29628	2	2020	2.0	2.0	9	1	5.88	1.0	53.0	4.0	3.0	9.0	2.0	1.0	2.0	0.0	2.0	1.0	10.0	37500.0	10.473937	3.0	0.0	2.0	3.0	1.0	0.0	0.0	0.0	0.0	0.0	2.0	1.0	1	3.0	2	0.0	NaN	1.0	3.0	1.0	0.0	NaN	NaN	NaN	NaN	1.0	3.0	3.0	1.50	NaN	NaN	1.0	1.0	NaN	19.0	NaN	2021.0	11.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	6.0	1.0	3.0	NaN	NaN	0.75	4.0	0.0	2.0	2.0	NaN	NaN	0.0	4.0	4.0	1.0	2.0	1.0	1.0	0.0	1.0	1.0	3.0	3.0	2.0	1.0	1.0	3.0	3.0	0.0	NaN	NaN	3.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	728.0	746.0	18.0	1.500000	NaN	728.0	1.0	1.0	1.0	2.0	13.550000	51.0	4.0	3.0	9.0	2.0	1.0	2.0	1.0	2.0	1.0	10.0	37500.0	10.532096	0.0	2.0	3.0	1.0	0.0	0.0	0.0	0.0	2.0	1.0	1	2.0	2.0	3.0	NaN	2.0	2.0	2	0.0	3.0	0.0	2.0	0.0	0.0	0.0	2.0	2020.0	247.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	1.0	1	0.0	1.0	2020.0	2.0	3.0	NaN	13.0	NaN	0.25	NaN	2020.0	4.0	0.0	723.0	2020.0	728.0	3.0	2020.0	9.0	2020.0	9.0	NaN	NaN	NaN	NaN	NaN	NaN	728.0	5.0	0.250	0.0	4.0	1.0	1.0	1.0	3.0	1.0	2020.0	9.0	2020.0	9.0	NaN	NaN	NaN	NaN	NaN	NaN	3.0	2.0	1.0	0.0	0.0	1.0	1.0	0.0	6.0	1.0	1.0	NaN	2.0	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	3.0	NaN	NaN	NaN	NaN	3.0	1.0	2020.0	4.0	NaN	NaN	1.0	NaN	1.0	1.0	51.0	13.0	9.0	3.0	NaN	1.0	2.0	NaN	2.0	2.0	2.0	0.0	1.0	0.0	0.0	4158	1	6.0	1	1	1	-2.0	6.0	48	4	3	9	2	5	1	1	1	1	2	1	1	1	13	2	1	0	2	3	1	0	1	1	2	1	1	1	1	3.0	1.0	NaN	NaN	NaN	NaN	1	1.0	NaN	1.0	1.0	46.0	11.0	14.0	1.0	9.0	1.0	14.0	1.0	14.0	1.0	7.0	3.0	1.0	1.0	NaN	2013.0	6.0	2013.0	12.0	2013.0	12.0	NaN	2014.0	9.0	NaN	NaN	NaN	NaN	1.0	1	NaN	NaN	3.0	130.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	2.0	2.0	0.0	0.0	0.0	1.0	NaN	1.0	1.0	1.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN	NaN	1.0	3.583333	NaN	NaN	2013.458374	2013.958374	44.0	0.500000	2013.958374	0.000000	3.0	1.0	20.0	12.0	14.0	12.0	NaN	1.0	2.0	1.0	30.0	23.0	1	1.0	647.0	641.0	1.500	-1.25	0.0	20170807.0	2.0	2.0	2.0	13.0	6.0	NaN	0.0	0.0	1.0	20171025.0	1.0	3.0	5.0	20171227.0	0.0	1.0	13.0	20180820.0	2.0	2.0	2.0	13.0	6.0	NaN	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	20190621.0	2.0	3.0	5.0	20190317.0	0.0	1.0	10.0	20191101.0	2.0	2.0	0.0	1.0	11.0	6.0	2.0	0.0	0.0	1.0	20200603.0	2.0	4.0	5.0	20200306.0	2.0	4.0	3.0	2.0	3.0	3.0	4.0	4.0	4.0	4.0	20190716.0	0.0	3.0	20201215.0	2.0	3.0	5.0	20210222.0	3.0	4.0	2.0	3.0	2.0	4.0	4.0	4.0	5.0	5.0	20201016.0	1.0	1.0	0.0	0.0	0.0	20200913.0	2.0	0.0	1.0	13.0	6.0	NaN	20210506.0
1	71609	0.8284	NaN	0.927891	0.879258	0.9196	NaN	1.043778	0.953948	1.295508	89921	1	2020	1.0	2.0	9	1	10.47	1.0	72.0	6.0	4.0	10.0	3.0	1.0	2.0	1.0	1.0	1.0	14.0	80000.0	11.231623	1.0	0.0	4.0	9.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1	1.0	1	1.0	NaN	2.0	2.0	1.0	0.0	NaN	NaN	NaN	NaN	2.0	1.0	5.0	0.25	NaN	NaN	1.0	1.0	0.0	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	6.0	8.0	1.0	NaN	1.0	0.00	4.0	0.0	4.0	2.0	NaN	NaN	0.0	1.0	1.0	1.0	2.0	2.0	NaN	NaN	0.0	2.0	2.0	2.0	2.0	2.0	0.0	3.0	11.0	0.0	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	57.0	746.0	689.0	57.416668	NaN	728.0	1.0	1.0	1.0	2.0	31.116667	71.0	6.0	4.0	10.0	3.0	1.0	2.0	1.0	1.0	1.0	13.0	67500.0	11.119883	0.0	4.0	9.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	1	2.0	2.0	3.0	NaN	1.0	1.0	1	1.0	1.0	1.0	NaN	0.0	0.0	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	NaN	1964.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.0	0.250	56.0	6.0	NaN	1.0	3.0	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	6.0	4.0	1.0	2.0	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	3.0	NaN	NaN	NaN	NaN	3.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	38	1	3.0	1	2	1	1.0	4.0	68	6	4	10	3	1	1	0	1	0	2	1	2	1	12	2	1	0	4	9	1	0	2	1	2	1	1	1	1	5.0	1.0	NaN	NaN	NaN	NaN	1	1.0	NaN	1.0	1.0	71.0	10.0	9.0	3.0	5.0	1.0	37.0	1.0	5.0	1.0	7.0	1.0	1.0	1.0	NaN	1964.0	8.0	1964.0	10.0	1969.0	3.0	NaN	1969.0	3.0	NaN	NaN	NaN	NaN	1.0	1	NaN	NaN	3.0	400.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	4.0	4.0	1.0	1.0	0.0	1.0	1.0	1.0	2.0	0.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN	NaN	1.0	52.750000	NaN	NaN	1964.625000	1964.791626	15.0	0.166626	1969.208374	4.416748	1.0	0.0	12.0	9.0	13.0	13.0	NaN	0.0	2.0	0.0	30.0	-26.0	1	0.0	57.0	55.0	0.250	0.00	1.0	20170702.0	2.0	2.0	2.0	1.0	4.0	2.0	0.0	0.0	1.0	20180122.0	7.0	4.0	5.0	20180216.0	0.0	1.0	11.0	20180910.0	2.0	2.0	2.0	1.0	4.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	1.0	20190311.0	6.0	4.0	4.0	20190318.0	0.0	1.0	13.0	20191209.0	2.0	2.0	0.0	1.0	1.0	4.0	1.0	0.0	0.0	1.0	20190912.0	1.0	4.0	5.0	20200515.0	4.0	4.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	5.0	20190728.0	1.0	1.0	20210207.0	NaN	NaN	NaN	NaN	3.0	5.0	1.0	3.0	3.0	2.0	3.0	5.0	1.0	5.0	20201118.0	0.0	0.0	0.0	0.0	1.0	20200917.0	2.0	0.0	1.0	2.0	5.0	1.0	20201118.0
2	106983	0.8255	NaN	0.924643	0.706467	0.7748	NaN	0.879425	0.724682	1.126573	78196	1	2020	1.0	2.0	9	1	4.43	1.0	43.0	3.0	2.0	11.0	3.0	1.0	1.0	0.0	5.0	1.0	14.0	80000.0	11.231623	1.0	1.0	4.0	9.0	1.0	0.0	0.0	0.0	0.0	2.0	3.0	1.0	1	1.0	1	1.0	NaN	2.0	2.0	1.0	0.0	NaN	NaN	NaN	NaN	1.0	1.0	3.0	1.50	NaN	NaN	1.0	1.0	10.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	6.0	3.0	1.0	NaN	1.0	0.00	4.0	0.0	4.0	2.0	NaN	NaN	0.0	1.0	1.0	1.0	2.0	2.0	NaN	NaN	0.0	2.0	2.0	2.0	1.0	1.0	1.0	1.0	6.0	0.0	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	479.0	747.0	268.0	22.333334	NaN	728.0	1.0	1.0	1.0	2.0	485.633333	42.0	3.0	2.0	11.0	3.0	1.0	1.0	0.0	5.0	1.0	15.0	92500.0	11.434964	1.0	4.0	9.0	1.0	1.0	1.0	1.0	1.0	3.0	1.0	1	2.0	2.0	2.0	NaN	1.0	1.0	1	1.0	1.0	1.0	NaN	0.0	0.0	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	NaN	1999.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	1.500	21.0	5.0	NaN	1.0	1.0	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	6.0	3.0	1.0	2.0	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	3.0	NaN	NaN	NaN	NaN	3.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	72	1	7.0	1	1	1	2.0	4.0	39	3	2	11	3	1	1	0	1	0	1	0	5	1	15	3	1	1	4	9	1	2	3	1	2	1	1	1	0	3.0	1.0	NaN	NaN	NaN	NaN	1	2.0	NaN	1.0	1.0	49.0	10.0	13.0	7.0	12.0	1.0	12.0	1.0	12.0	1.0	5.0	1.0	1.0	1.0	NaN	1999.0	3.0	1999.0	12.0	2000.0	4.0	NaN	2002.0	9.0	NaN	NaN	NaN	NaN	1.0	1	NaN	NaN	1.0	210.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	2.0	2.0	0.0	0.0	0.0	2.0	2.0	2.0	2.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN	NaN	1.0	17.583334	NaN	NaN	1999.208374	1999.958374	21.0	0.750000	2000.291626	0.333252	1.0	0.0	17.0	16.0	13.0	14.0	NaN	0.0	2.0	1.0	30.0	9.0	1	0.0	479.0	470.0	1.500	0.00	0.0	20170624.0	2.0	1.0	1.0	1.0	4.0	2.0	1.0	1.0	0.0	20170907.0	1.0	2.0	4.0	20171216.0	0.0	1.0	15.0	20180822.0	2.0	1.0	1.0	1.0	4.0	2.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	20190612.0	3.0	3.0	4.0	20190420.0	0.0	1.0	15.0	20191104.0	2.0	1.0	1.0	0.0	1.0	4.0	2.0	1.0	1.0	0.0	20200517.0	1.0	2.0	4.0	20200421.0	4.0	4.0	3.0	4.0	2.0	4.0	3.0	5.0	4.0	3.0	20190713.0	0.0	1.0	20201222.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	1.0	1.0	0.0	20200910.0	1.0	1.0	0.0	1.0	4.0	2.0	20210429.0
3	121759	NaN	NaN	NaN	NaN	0.9177	NaN	1.041622	0.793093	0.933440	64790	1	2020	1.0	2.0	9	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	728.0	1.0	1.0	1.0	2.0	22.600000	57.0	5.0	3.0	9.0	2.0	1.0	1.0	1.0	2.0	1.0	16.0	112500.0	11.630709	1.0	1.0	2.0	1.0	0.0	0.0	0.0	0.0	2.0	1.0	1	2.0	2.0	3.0	NaN	2.0	2.0	1	1.0	1.0	1.0	NaN	0.0	0.0	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.00	NaN	NaN	NaN	NaN	NaN	1990.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.0	0.625	31.0	6.0	NaN	1.0	3.0	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	6.0	1.0	1.0	2.0	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	3.0	NaN	NaN	NaN	NaN	3.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	25	1	2.0	1	1	1	1.0	2.0	54	4	3	9	2	1	1	0	1	0	1	1	4	1	16	3	1	1	1	2	1	0	4	1	2	1	1	1	0	4.0	1.0	NaN	NaN	NaN	NaN	1	2.0	NaN	1.0	4.0	59.0	13.0	1.0	4.0	14.0	1.0	33.0	1.0	33.0	1.0	3.0	1.0	1.0	1.0	NaN	1989.0	10.0	1990.0	2.0	1991.0	4.0	NaN	1991.0	3.0	NaN	NaN	NaN	NaN	1.0	1	NaN	NaN	3.0	423.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	3.0	3.0	0.0	0.0	1.0	2.0	NaN	2.0	2.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	NaN	NaN	NaN	NaN	1.0	27.416666	NaN	NaN	1989.791626	1990.125000	26.0	0.333374	1991.291626	1.166626	1.0	1.0	0.0	20.0	17.0	12.0	NaN	0.0	2.0	1.0	30.0	-1.0	1	1.0	361.0	357.0	0.625	0.00	0.0	20170814.0	2.0	1.0	1.0	11.0	2.0	1.0	0.0	0.0	1.0	20170914.0	1.0	4.0	5.0	20171212.0	1.0	1.0	18.0	20180819.0	2.0	1.0	1.0	11.0	2.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	20190613.0	3.0	4.0	5.0	20190330.0	1.0	1.0	16.0	20191103.0	2.0	1.0	1.0	0.0	11.0	2.0	1.0	0.0	0.0	1.0	20200517.0	5.0	4.0	5.0	20200308.0	5.0	2.0	2.0	5.0	4.0	4.0	4.0	5.0	2.0	5.0	20190721.0	1.0	1.0	20210114.0	2.0	4.0	5.0	20210224.0	4.0	3.0	1.0	4.0	4.0	4.0	2.0	4.0	1.0	5.0	20201017.0	1.0	1.0	0.0	0.0	0.0	20200917.0	1.0	1.0	0.0	11.0	2.0	1.0	20210507.0
4	158083	0.8810	NaN	0.986809	0.655467	0.8697	NaN	0.987140	0.735473	0.931291	64641	3	2020	NaN	NaN	9	0	5.34	1.0	53.0	4.0	3.0	10.0	3.0	1.0	1.0	1.0	3.0	1.0	18.0	162500.0	11.940274	3.0	1.0	4.0	8.0	2.0	0.0	0.0	0.0	0.0	0.0	3.0	1.0	1	6.0	3	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	NaN	NaN	5.0	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	NaN	4.0	2.0	NaN	2.00	1.0	1.0	1.0	2.0	NaN	NaN	0.0	1.0	NaN	NaN	2.0	NaN	NaN	NaN	NaN	NaN	3.0	3.0	1.0	NaN	NaN	1.0	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.0	2.0	2.0	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	746.0	NaN	NaN	NaN	728.0	3.0	4.0	NaN	2.0	3.083333	52.0	4.0	3.0	10.0	3.0	1.0	1.0	1.0	3.0	1.0	18.0	162500.0	11.998433	1.0	4.0	8.0	2.0	0.0	0.0	0.0	0.0	3.0	1.0	1	NaN	NaN	3.0	2.0	NaN	NaN	3	0.0	4.0	0.0	NaN	NaN	NaN	NaN	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN	1.0	NaN	2.0	0.0	2.0	0.75	2.0	NaN	NaN	NaN	NaN	NaN	NaN	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	NaN	NaN	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	NaN	NaN	NaN	NaN	3.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12	1	3.0	1	1	1	-2.0	5.0	48	4	3	10	3	1	1	0	1	0	1	1	1	1	14	3	3	1	4	8	1	0	1	1	2	2	3	4	0	NaN	NaN	NaN	NaN	NaN	NaN	1	2.0	NaN	1.0	1.0	34.0	11.0	11.0	2.0	9.0	1.0	29.0	1.0	29.0	1.0	0.0	3.0	1.0	NaN	1.0	2011.0	8.0	2011.0	8.0	2012.0	1.0	NaN	2013.0	6.0	2014.0	6.0	NaN	NaN	1.0	1	NaN	NaN	1.0	186.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	0.0	1.0	1.0	2.0	2.0	2.0	1.0	1.0	1.0	0.0	0.0	1.0	NaN	NaN	1.0	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	2011.625000	2011.625000	42.0	0.000000	2012.041626	0.416626	1.0	0.0	14.0	12.0	14.0	13.0	NaN	1.0	2.0	1.0	30.0	21.0	1	NaN	619.0	619.0	NaN	NaN	0.0	20170914.0	2.0	1.0	1.0	13.0	5.0	NaN	0.0	0.0	1.0	20171013.0	3.0	4.0	4.0	20180127.0	0.0	3.0	18.0	20180831.0	4.0	1.0	1.0	13.0	6.0	NaN	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	20190611.0	1.0	4.0	4.0	20190217.0	0.0	3.0	18.0	20191101.0	2.0	1.0	1.0	0.0	13.0	6.0	NaN	0.0	0.0	1.0	20200529.0	1.0	4.0	4.0	20200124.0	5.0	2.0	4.0	4.0	1.0	1.0	4.0	4.0	2.0	5.0	20190712.0	0.0	3.0	20201215.0	3.0	4.0	4.0	20210201.0	5.0	1.0	4.0	4.0	1.0	1.0	2.0	4.0	1.0	5.0	20201104.0	0.0	0.0	0.0	0.0	1.0	20200916.0	1.0	1.0	0.0	13.0	6.0	NaN	20210602.0

df.shape

(3510, 725)

Full List of Variables

The next cell displays the full list of variables in the original data set. We are not going to use everything…

We’ll focus on variables of interest for specific inquiries.

In the following links, we can find the encoding of each variable.

Data source: https://data.stanford.edu/hcmst2017

Variable List per year (Note w1 = 2017, w2 = 2020, w3 = 2022): https://stacks.stanford.edu/file/druid:tq903pj6286/HCMST%202017-%202022%20user%27s%20guide%20v2.3.pdf

Detailed Info on variables: https://stacks.stanford.edu/file/druid:hg921sg6829/HCMST%202017%20to%202022%20v2.2%20codebook.pdf

#df.columns.tolist()

Variables of Interest

The following lists are not actually used in the rest of the notebook for data cleaning purposes.

Still, they display the selected variables of interest (and how their names vary from year to year), so, we are leaving them in the notebook for documentation.

# ppage = Age
# ppeduc = Education (Highest Degree Received)
# ppgender = Gender
# ppethm =  Race / Ethnicity 
# ppincimp = Household Income
# ppwork = Current Employment Status 

subject_demographics_2017 = [
    'w1_ppage', 'w1_ppeduc', 'w1_ppgender', 'w1_ppethm', 'w1_ppincimp', 'w1_ppwork'
]
subject_demographics_2020 = [
    'w2_ppage', 'w2_ppeduc', 'w2_ppgender', 'w2_ppethm', 'w2_ppincimp', 'w2_ppwork'
]
subject_demographics_2022 = [
    'w3_ppage', 'w3_ppeduc', 'w3_ppgender', 'w3_ppethm', 'w3_ppincimp', 'w3_ppwork'
]

# q4 = Partner Gender
# q9 = Partner Age
# q6b = Partner's Race
# q10 = Partner's Education (Highest Degree Received)

partner_demographics_2017 = [
    'w1_q4', 'w1_q9', 'w1_q6b', 'w1_q10'
]
partner_demographics_2020 = [
    'w2_Q4', 'w2_Q9', 'w2_Q6B', 'w2_Q10'
]
partner_demographics_2022 = [
    'w3_Q4', 'w3_Q9', 'w3_Q6B', 'w3_Q10'
]

# same_sex_couple = same-sex couple
# married = Married
# q34/rel_qual_combo/rel_qual = Relationship quality 1 (Excellent) to 5 (Very Poor) - Potential Target

relationship_status_2017 = [
    'w1_same_sex_couple', 'w1_married', 'w1_q34'
]
relationship_status_2020 = [
    'w2_same_sex_couple', 'w2_married', 'w2_rel_qual_combo'
]
relationship_status_2022 = [
    'w3_same_sex_couple', 'w3_married', 'w3_rel_qual'
]

# q21b - Year current relationship started
# q21d - Year of marriage
# q21e - Year relationship ended
# relate/relationship duration = Duration of relationship in years

relationship_time_2017 = [
    'w1_q21b_year', 'w1_q21d_year', 'w1_q21e_year', 'w1_relate_duration_in2017_years'
]
relationship_time_2020 = [
    'w2_q21b_year', 'w2_q21d_year', 'w2_q21e_year', 'w2_relationship_duration'
]
relationship_time_2022 = [
    'w3_Q21B_year', 'w3_Q21D_year', 'w3_Q21E_year', 'w3_relationship_duration_yrs'
]

# PPT01 = # of children in the household ages 0-1
# PPT25 = # of children in the household ages 2-5
# PPT612 = # of children in the household ages 6-12
# PPT1317 = # of children in the household ages 13-17
# PPT18OV = # of children in the household ages 18-Over

children_info_2017 = [
    'w1_PPT01', 'w1_PPT25', 'w1_PPT612', 'w1_PPT1317', 'w1_PPT18OV'
]
children_info_2020 = [
    'w2_PPT01', 'w2_PPT25', 'w2_PPT612', 'w2_PPT1317', 'w2_PPT18OV'
]
children_info_2022 = [
    'w3_PPT01', 'w3_PPT25', 'w3_PPT612', 'w3_PPT1317', 'w3_PPT18OV'
]

# Only available in 2022 in full (Post-COVID)

# coronavirus_effect_combo = Is relationship better or worse during pandemic
# pandemic_income = has income gone up or down during pandemic
# subject/partner_had_COVID = has been sick with COVID
# corona/partner_vaccine = has been vaccinated
# COVID_agreement = subject and partner agree on approach to pandemic

covid_vars_2022 = [
    'w3_coronavirus_effect_combo', 'w3_pandemic_income', 'w3_subject_had_COVID', 'w3_partner_had_COVID', 'w3_corona_vaccine',
    'w3_partner_corona_vaccine', 'w3_COVID_agreement'
]

# Only some variables available per year

# sex_frequency = Frequency of sex
# flirt = how often flirt
# fight = how often fight
# monogamy = subject's commitment to monogamy
# p_monogamy = expected commitment to monogamy from partner

relationship_quality_2017 = [
    'w1_sex_frequency'
]
relationship_quality_2020 = [
    'w2_sex_frequency', 'w2_flirt', 'w2_fight'
]
relationship_quality_2022 = [
    'w3_sex_frequency', 'w3_flirt', 'w3_fight'
]

Rename Columns

Some variables contain the same information but vary in name year to year. So, we’ll need 3 renaming dictionaries (1 per year)

First, the 3 renaming maps (one per year):

column_renames_2017 = {
    #subject_demographics
    'w1_ppage': 'subject_age',
    'w1_ppeduc': 'subject_education',
    'w1_ppgender': 'subject_sex',
    'w1_ppethm': 'subject_ethnicity',
    'w1_ppincimp': 'subject_income_category',
    'w1_ppwork': 'subject_employment_status',
    #partner_demographics
    'w1_q4': 'partner_sex',
    'w1_q9': 'partner_age',
    'w1_q6b': 'partner_ethnicity',
    'w1_q10': 'partner_education',
    #relationship_status
    'w1_same_sex_couple': 'same_sex_couple',
    'w1_married': 'married',
    'w1_q34': 'relationship_quality',
    #relationship_time
    'w1_q21b_year': 'relationship_start_year',
    'w1_q21d_year': 'marriage_year',
    'w1_q21e_year': 'break_up_year',
    'w1_relate_duration_in2017_years': 'relationship_duration',
    #children_info
    'w1_PPT01': 'kids_0_1',
    'w1_PPT25': 'kids_2_5',
    'w1_PPT612': 'kids_6_12',
    'w1_PPT1317': 'kids_13_17',
    'w1_PPT18OV': 'kids_18_plus',
    #covidvars
    #'w1_coronavirus_effect_combo': 'rel_change_during_pandemic',
    #'w1_pandemic_income': 'inc_change_during_pandemic',
    #'w1_subject_had_COVID': 'subject_had_covid', 
    #'w1_partner_had_COVID': 'partner_had_covid', 
    #'w1_corona_vaccine': 'subject_vaccinated',
    #'w1_partner_corona_vaccine': 'partner_vaccinated', 
    #'w1_COVID_agreement': 'agree_covid_approach',
    #relationship_quality
    'w1_sex_frequency': 'sex_frequency',
    #'w3_flirt': 'flirts_with_partner',
    #'w3_fight': 'fights_with_partner',
}

column_renames_2020 = {
    #subject_demographics
    'w2_ppage': 'subject_age',
    'w2_ppeduc': 'subject_education',
    'w2_ppgender': 'subject_sex',
    'w2_ppethm': 'subject_ethnicity',
    'w2_ppincimp': 'subject_income_category',
    'w2_ppwork': 'subject_employment_status',
    #partner_demographics
    'w2_Q4': 'partner_sex',
    'w2_Q9': 'partner_age',
    'w2_Q6B': 'partner_ethnicity',
    'w2_Q10': 'partner_education',
    #relationship_status
    'w2_same_sex_couple': 'same_sex_couple',
    'w2_married': 'married',
    'w2_rel_qual_combo': 'relationship_quality',
    #relationship_time
    'w2_q21b_year': 'relationship_start_year',
    'w2_q21d_year': 'marriage_year',
    'w2_q21e_year': 'break_up_year',
    'w2_relationship_duration': 'relationship_duration',
    #children_info
    'w2_PPT01': 'kids_0_1',
    'w2_PPT25': 'kids_2_5',
    'w2_PPT612': 'kids_6_12',
    'w2_PPT1317': 'kids_13_17',
    'w2_PPT18OV': 'kids_18_plus',
    #covidvars
    #'w2_coronavirus_effect_combo': 'rel_change_during_pandemic',
    #'w2_pandemic_income': 'inc_change_during_pandemic',
    #'w2_subject_had_COVID': 'subject_had_covid', 
    #'w2_partner_had_COVID': 'partner_had_covid', 
    #'w2_corona_vaccine': 'subject_vaccinated',
    #'w2_partner_corona_vaccine': 'partner_vaccinated', 
    #'w2_COVID_agreement': 'agree_covid_approach',
    #relationship_quality
    'w2_sex_frequency': 'sex_frequency',
    'w2_flirt': 'flirts_with_partner',
    'w2_fight': 'fights_with_partner',
}

column_renames_2022 = {
    #subject_demographics
    'w3_ppage': 'subject_age',
    'w3_ppeduc': 'subject_education',
    'w3_ppgender': 'subject_sex',
    'w3_ppethm': 'subject_ethnicity',
    'w3_ppincimp': 'subject_income_category',
    'w3_ppwork': 'subject_employment_status',
    #partner_demographics
    'w3_Q4': 'partner_sex',
    'w3_Q9': 'partner_age',
    'w3_Q6B': 'partner_ethnicity',
    'w3_Q10': 'partner_education',
    #relationship_status
    'w3_same_sex_couple': 'same_sex_couple',
    'w3_married': 'married',
    'w3_rel_qual': 'relationship_quality',
    #relationship_time
    'w3_Q21B_year': 'relationship_start_year',
    'w3_Q21D_year': 'marriage_year',
    'w3_Q21E_year': 'break_up_year',
    'w3_relationship_duration_yrs': 'relationship_duration',
    #children_info
    'w3_PPT01': 'kids_0_1',
    'w3_PPT25': 'kids_2_5',
    'w3_PPT612': 'kids_6_12',
    'w3_PPT1317': 'kids_13_17',
    'w3_PPT18OV': 'kids_18_plus',
    #covidvars
    'w3_coronavirus_effect_combo': 'rel_change_during_pandemic',
    'w3_pandemic_income': 'inc_change_during_pandemic',
    'w3_subject_had_COVID': 'subject_had_covid', 
    'w3_partner_had_COVID': 'partner_had_covid', 
    'w3_corona_vaccine': 'subject_vaccinated',
    'w3_partner_corona_vaccine': 'partner_vaccinated', 
    'w3_COVID_agreement': 'agree_covid_approach',
    #relationship_quality
    'w3_sex_frequency': 'sex_frequency',
    'w3_flirt': 'flirts_with_partner',
    'w3_fight': 'fights_with_partner',
}

Dataframe per year

We need to create dataframes per year while renaming variables in each dictionary.

We will also create a column named “Wave” that contains the year of the survey.

First, 2017:

df_2017 = df[['caseid_new'] + list(column_renames_2017.keys())].rename(columns=column_renames_2017)
df_2017['wave'] = '2017'
df_2017.head()

	caseid_new	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	married	relationship_quality	relationship_start_year	marriage_year	break_up_year	relationship_duration	kids_6_12	kids_18_plus	sex_frequency	wave
0	53001	48	9	2	5	13	1	1.0	46.0	1.0	11.0	1.0	1.0	2013.0	2014.0	NaN	3.583333	0	1	3.0	2017
1	71609	68	10	2	1	12	1	1.0	71.0	1.0	10.0	1.0	1.0	1964.0	1969.0	NaN	52.750000	0	2	5.0	2017
2	106983	39	11	1	1	15	1	2.0	49.0	1.0	10.0	1.0	1.0	1999.0	2002.0	NaN	17.583334	2	3	3.0	2017
3	121759	54	9	1	1	16	1	2.0	59.0	4.0	13.0	1.0	1.0	1990.0	1991.0	NaN	27.416666	0	4	4.0	2017
4	158083	48	10	1	1	14	1	2.0	34.0	1.0	11.0	0.0	NaN	2011.0	2013.0	2014.0	NaN	0	1	NaN	2017

df_2017.shape

(3510, 25)

Then, 2020:

df_2020 = df[['caseid_new'] + list(column_renames_2020.keys())].rename(columns=column_renames_2020)
df_2020['wave'] = '2020'
df_2020.head()

	caseid_new	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	same_sex_couple	married	relationship_quality	relationship_start_year	marriage_year	break_up_year	relationship_duration	kids_6_12	kids_13_17	kids_18_plus	sex_frequency	flirts_with_partner	fights_with_partner	wave
0	53001	51.0	9.0	2.0	1.0	10.0	1.0	1.0	51.0	1.0	13.0	0.0	0.0	2.0	2020.0	NaN	NaN	0.0	0.0	0.0	2.0	5.0	6.0	1.0	2020
1	71609	71.0	10.0	2.0	1.0	13.0	1.0	NaN	NaN	NaN	NaN	0.0	1.0	1.0	NaN	NaN	NaN	56.0	0.0	0.0	1.0	5.0	6.0	4.0	2020
2	106983	42.0	11.0	1.0	1.0	15.0	1.0	NaN	NaN	NaN	NaN	0.0	1.0	1.0	NaN	NaN	NaN	21.0	1.0	1.0	3.0	3.0	2.0	3.0	2020
3	121759	57.0	9.0	1.0	1.0	16.0	1.0	NaN	NaN	NaN	NaN	0.0	1.0	2.0	NaN	NaN	NaN	31.0	0.0	0.0	2.0	4.0	6.0	1.0	2020
4	158083	52.0	10.0	1.0	1.0	18.0	1.0	NaN	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	NaN	NaN	0.0	0.0	3.0	NaN	NaN	NaN	2020

df_2020.shape

(3510, 27)

Then, 2022:

df_2022 = df[['caseid_new'] + list(column_renames_2022.keys())].rename(columns=column_renames_2022)
df_2022['wave'] = '2022'
df_2022.head()

	caseid_new	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	same_sex_couple	married	relationship_quality	relationship_start_year	marriage_year	break_up_year	relationship_duration	kids_0_1	kids_2_5	kids_6_12	kids_13_17	kids_18_plus	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach	sex_frequency	flirts_with_partner	fights_with_partner	wave
0	53001	53.0	9.0	2.0	1.0	10.0	1.0	NaN	NaN	NaN	NaN	0.0	0.0	1.0	NaN	NaN	NaN	1.500000	0.0	0.0	0.0	0.0	2.0	1.0	3.0	0.0	1.0	4.0	4.0	1.0	3.0	2.0	1.0	2022
1	71609	72.0	10.0	2.0	1.0	14.0	1.0	NaN	NaN	NaN	NaN	0.0	1.0	2.0	NaN	NaN	NaN	57.416668	0.0	0.0	0.0	0.0	1.0	2.0	2.0	0.0	0.0	1.0	1.0	2.0	5.0	6.0	8.0	2022
2	106983	43.0	11.0	1.0	1.0	14.0	1.0	NaN	NaN	NaN	NaN	0.0	1.0	1.0	NaN	NaN	NaN	22.333334	0.0	0.0	0.0	2.0	3.0	2.0	2.0	0.0	0.0	1.0	1.0	1.0	3.0	2.0	3.0	2022
3	121759	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2022
4	158083	53.0	10.0	1.0	1.0	18.0	1.0	NaN	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	3.0	NaN	3.0	0.0	NaN	1.0	NaN	NaN	NaN	NaN	NaN	2022

df_2022.shape

(3510, 34)

A single Dataframe

Now, we can concatenate the 3 dataframes since they have the same variables. We’ll have the “wave” column to differentiate by year.

df_cleaned = pd.concat([df_2017, df_2020, df_2022], ignore_index=True)

df_cleaned.shape

(10530, 34)

df_cleaned.head()

	caseid_new	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	married	relationship_quality	relationship_start_year	marriage_year	break_up_year	relationship_duration	kids_6_12	kids_18_plus	sex_frequency	wave	flirts_with_partner	fights_with_partner	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach
0	53001	48.0	9.0	2.0	5.0	13.0	1.0	1.0	46.0	1.0	11.0	1.0	1.0	2013.0	2014.0	NaN	3.583333	0.0	1.0	3.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	71609	68.0	10.0	2.0	1.0	12.0	1.0	1.0	71.0	1.0	10.0	1.0	1.0	1964.0	1969.0	NaN	52.750000	0.0	2.0	5.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	106983	39.0	11.0	1.0	1.0	15.0	1.0	2.0	49.0	1.0	10.0	1.0	1.0	1999.0	2002.0	NaN	17.583334	2.0	3.0	3.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	121759	54.0	9.0	1.0	1.0	16.0	1.0	2.0	59.0	4.0	13.0	1.0	1.0	1990.0	1991.0	NaN	27.416666	0.0	4.0	4.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	158083	48.0	10.0	1.0	1.0	14.0	1.0	2.0	34.0	1.0	11.0	0.0	NaN	2011.0	2013.0	2014.0	NaN	0.0	1.0	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

We are just going to rename the ‘caseid_new’ to something more simple:

df_cleaned = df_cleaned.rename(columns={'caseid_new': 'id'})

Selected variables

df_cleaned.columns.tolist()

['id',
 'subject_age',
 'subject_education',
 'subject_sex',
 'subject_ethnicity',
 'subject_income_category',
 'subject_employment_status',
 'partner_sex',
 'partner_age',
 'partner_ethnicity',
 'partner_education',
 'same_sex_couple',
 'married',
 'relationship_quality',
 'relationship_start_year',
 'marriage_year',
 'break_up_year',
 'relationship_duration',
 'kids_0_1',
 'kids_2_5',
 'kids_6_12',
 'kids_13_17',
 'kids_18_plus',
 'sex_frequency',
 'wave',
 'flirts_with_partner',
 'fights_with_partner',
 'rel_change_during_pandemic',
 'inc_change_during_pandemic',
 'subject_had_covid',
 'partner_had_covid',
 'subject_vaccinated',
 'partner_vaccinated',
 'agree_covid_approach']

Merging kids of all ages to a single feature:

df_cleaned['children'] = df_cleaned['kids_0_1'] + df_cleaned['kids_2_5'] + df_cleaned['kids_6_12'] + df_cleaned['kids_13_17'] + df_cleaned['kids_18_plus']

df_cleaned.drop(columns=['kids_0_1', 'kids_2_5', 'kids_6_12', 'kids_13_17', 'kids_18_plus'], inplace=True)

df_cleaned.head()

	id	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	married	relationship_quality	relationship_start_year	marriage_year	break_up_year	relationship_duration	sex_frequency	wave	flirts_with_partner	fights_with_partner	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach	children
0	53001	48.0	9.0	2.0	5.0	13.0	1.0	1.0	46.0	1.0	11.0	1.0	1.0	2013.0	2014.0	NaN	3.583333	3.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
1	71609	68.0	10.0	2.0	1.0	12.0	1.0	1.0	71.0	1.0	10.0	1.0	1.0	1964.0	1969.0	NaN	52.750000	5.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0
2	106983	39.0	11.0	1.0	1.0	15.0	1.0	2.0	49.0	1.0	10.0	1.0	1.0	1999.0	2002.0	NaN	17.583334	3.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.0
3	121759	54.0	9.0	1.0	1.0	16.0	1.0	2.0	59.0	4.0	13.0	1.0	1.0	1990.0	1991.0	NaN	27.416666	4.0	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.0
4	158083	48.0	10.0	1.0	1.0	14.0	1.0	2.0	34.0	1.0	11.0	0.0	NaN	2011.0	2013.0	2014.0	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0

Data Type Exploration

We can see what data type each column has. We can note that all columns are encoded with numerical values.

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print(df_cleaned.dtypes)

id                              int64
subject_age                   float64
subject_education             float64
subject_sex                   float64
subject_ethnicity             float64
subject_income_category       float64
subject_employment_status     float64
partner_sex                   float64
partner_age                   float64
partner_ethnicity             float64
partner_education             float64
same_sex_couple               float64
married                       float64
relationship_quality          float64
relationship_start_year       float64
marriage_year                 float64
break_up_year                 float64
relationship_duration         float64
sex_frequency                 float64
wave                           object
flirts_with_partner           float64
fights_with_partner           float64
rel_change_during_pandemic    float64
inc_change_during_pandemic    float64
subject_had_covid             float64
partner_had_covid             float64
subject_vaccinated            float64
partner_vaccinated            float64
agree_covid_approach          float64
children                      float64
dtype: object

Decoding Variables

We’ll need to re-map column content to something more interpretable.

Going column by column…

First, education level:

edu_mapping = {
    1: 'no_education',
    2: '1st_4th_grade',
    3: '5th_6th_grade',
    4: '7th_8th_grade',
    5: '9th',
    6: '10th',
    7: '11th',
    8: '12th_nodiploma',
    9: 'high_school_grad',
    10: 'some_college',
    11: 'associate_degree',
    12: 'bach_degree',
    13: 'masters_degree',
    14: 'prof_doct_degree'
}

df_cleaned['subject_education'] = df_cleaned['subject_education'].map(edu_mapping)
df_cleaned['partner_education'] = df_cleaned['partner_education'].map(edu_mapping)

ordered_levels = [
    'no_education',
    '1st_4th_grade',
    '5th_6th_grade',
    '7th_8th_grade',
    '9th',
    '10th',
    '11th',
    '12th_nodiploma',
    'high_school_grad',
    'some_college',
    'associate_degree',
    'bach_degree',
    'masters_degree',
    'prof_doct_degree'
]

# Convert to ordered categorical
df_cleaned['subject_education'] = pd.Categorical(
    df_cleaned['subject_education'],
    categories=ordered_levels,
    ordered=True
)

df_cleaned['partner_education'] = pd.Categorical(
    df_cleaned['partner_education'],
    categories=ordered_levels,
    ordered=True
)

Then gender:

gender_mapping = {
    1: 'male',
    2: 'female',
    3: 'other'
}

df_cleaned['subject_sex'] = df_cleaned['subject_sex'].map(gender_mapping)
df_cleaned['partner_sex'] = df_cleaned['partner_sex'].map(gender_mapping)

Then, ethniticy/race:

eth_sub_mapping = {
    1: 'white',
    2: 'black',
    3: 'other',
    4: 'hispanic',
    5: '2_plus_eth'
}

df_cleaned['subject_ethnicity'] = df_cleaned['subject_ethnicity'].map(eth_sub_mapping)

eth_part_mapping = {
    1: 'white',
    2: 'black',
    3: 'american_indian',
    4: 'asian',
    5: 'other'
}

df_cleaned['partner_ethnicity'] = df_cleaned['partner_ethnicity'].map(eth_part_mapping)

Then, income level:

income_mapping = {
    1: 'under_5k',
    2: '5k_7k',
    3: '7k_10k',
    4: '10k_12k',
    5: '12k_15k',
    6: '15k_20k',
    7: '20k_25k',
    8: '25k_30k',
    9: '30k_35k',
    10: '35k_40k',
    11: '40k_50k',
    12: '50k_60k',
    13: '60k_75k',
    14: '75k_85k',
    15: '85k_100k',
    16: '100k_125k',
    17: '125k_150k',
    18: '150k_175k',
    19: '175k_200k',
    20: '200k_250k',
    21: 'over_250k'
}

df_cleaned['subject_income_category'] = df_cleaned['subject_income_category'].map(income_mapping)

ordered_income_levels = [
    'under_5k', '5k_7k', '7k_10k', '10k_12k', '12k_15k', '15k_20k',
    '20k_25k', '25k_30k', '30k_35k', '35k_40k', '40k_50k', '50k_60k',
    '60k_75k', '75k_85k', '85k_100k', '100k_125k', '125k_150k',
    '150k_175k', '175k_200k', '200k_250k', 'over_250k'
]

df_cleaned['subject_income_category'] = pd.Categorical(
    df_cleaned['subject_income_category'],
    categories=ordered_income_levels,
    ordered=True
)

Then, employment status:

employment_mapping = {
    1: 'working_paid_employee',
    2: 'working_self_employed',
    3: 'not_working_temp_layoff',
    4: 'not_working_looking',
    5: 'not_working_retired',
    6: 'not_working_disabled',
    7: 'not_working_other'
}

df_cleaned['subject_employment_status'] = df_cleaned['subject_employment_status'].map(employment_mapping)

Then, same-sex couple encoding:

same_sex_couple_map = {
    0: 'no',
    1: 'yes'
}

df_cleaned['same_sex_couple'] = df_cleaned['same_sex_couple'].map(same_sex_couple_map)

Then, marital status:

married_mapping = {
    0: 'not_married',
    1: 'married'
}

df_cleaned['married'] = df_cleaned['married'].map(married_mapping)

Then, relationship quality (THIS COULD BE A TARGET VARIABLE FOR MANY STUDIES):

rel_qual_mapping = {
    1: 'excellent',
    2: 'good',
    3: 'fair',
    4: 'poor',
    5: 'very_poor'
}

df_cleaned['relationship_quality'] = df_cleaned['relationship_quality'].map(rel_qual_mapping)

Then, the effect of COVID on the relationship. Note that all COVID variables only appear in the 2022 survey.

covid_effect_mapping = {
    1: 'better_than_before',
    2: 'no_change',
    3: 'worse_than_before'
}

df_cleaned['rel_change_during_pandemic'] = df_cleaned['rel_change_during_pandemic'].map(covid_effect_mapping)

Then, the effect of COVID on income level.

covid_income_mapping = {
    1: 'much_worse',
    2: 'worse',
    3: 'no_change',
    4: 'better',
    5: 'much_better'
}

df_cleaned['inc_change_during_pandemic'] = df_cleaned['inc_change_during_pandemic'].map(covid_income_mapping)

Then, if subject or partner had COVID

had_covid_mapping = {
    0: 'no',
    1: 'yes'
}

df_cleaned['subject_had_covid'] = df_cleaned['subject_had_covid'].map(had_covid_mapping)
df_cleaned['partner_had_covid'] = df_cleaned['partner_had_covid'].map(had_covid_mapping)

Then, status of COVID vaccination:

corona_vaccine_mapping = {
    1: 'fully_vaccinated_and_booster',
    2: 'fully_vaccinated_no_booster',
    3: 'partially_vaccinated',
    4: 'not_vaccinated'
}

df_cleaned['subject_vaccinated'] = df_cleaned['subject_vaccinated'].map(corona_vaccine_mapping)
df_cleaned['partner_vaccinated'] = df_cleaned['partner_vaccinated'].map(corona_vaccine_mapping)

Then, if the couple agrees on COVID approach:

covid_agreement_mapping = {
    1: 'completely_agree',
    2: 'mostly_agree',
    3: 'mostly_disagree',
    4: 'completely_disagree'
}

df_cleaned['agree_covid_approach'] = df_cleaned['agree_covid_approach'].map(covid_agreement_mapping)

Then, sex frequency:

sex_frequency_mapping = {
    1: 'once_or_more_a_day',
    2: '3_to_6_times_a_week',
    3: 'once_or_twice_a_week',
    4: '2_to_3_times_a_month',
    5: 'once_a_month_or_less'
}

df_cleaned['sex_frequency'] = df_cleaned['sex_frequency'].map(sex_frequency_mapping)

Then, how often the subject flirts with partner:

flirt_mapping = {
    1: 'every_day',
    2: 'a_few_times_a_week',
    3: 'once_a_week',
    4: '1_to_3_times_a_month',
    5: 'less_than_once_a_month',
    6: 'never'
}

df_cleaned['flirts_with_partner'] = df_cleaned['flirts_with_partner'].map(flirt_mapping)

ordered_flirt_frequency_levels = [
    'every_day',
    'a_few_times_a_week',
    'once_a_week',
    '1_to_3_times_a_month',
    'less_than_once_a_month',
    'never'
]

df_cleaned['flirts_with_partner'] = pd.Categorical(
    df_cleaned['flirts_with_partner'],
    categories=ordered_flirt_frequency_levels,
    ordered=True
)

Then, how often the subject fights with partner:

fight_mapping = {
    1: '0_times',
    2: '1_time',
    3: '2_times',
    4: '3_times',
    5: '4_times',
    6: '5_times',
    7: '6_times',
    8: '7_or_more_times'
}

df_cleaned['fights_with_partner'] = df_cleaned['fights_with_partner'].map(fight_mapping)

ordered_fight_frequency_levels = [
    '0_times',
    '1_time',
    '2_times',
    '3_times',
    '4_times',
    '5_times',
    '6_times',
    '7_or_more_times'
]

df_cleaned['fights_with_partner'] = pd.Categorical(
    df_cleaned['fights_with_partner'],
    categories=ordered_fight_frequency_levels,
    ordered=True
)

Now, we can see the updated data types for each variable.

We can also see a sample of how the data looks after the recoding process.

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print(df_cleaned.dtypes)

id                               int64
subject_age                    float64
subject_education             category
subject_sex                     object
subject_ethnicity               object
subject_income_category       category
subject_employment_status       object
partner_sex                     object
partner_age                    float64
partner_ethnicity               object
partner_education             category
same_sex_couple                 object
married                         object
relationship_quality            object
relationship_start_year        float64
marriage_year                  float64
break_up_year                  float64
relationship_duration          float64
sex_frequency                   object
wave                            object
flirts_with_partner           category
fights_with_partner           category
rel_change_during_pandemic      object
inc_change_during_pandemic      object
subject_had_covid               object
partner_had_covid               object
subject_vaccinated              object
partner_vaccinated              object
agree_covid_approach            object
children                       float64
dtype: object

df_cleaned.sample(20)

	id	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	same_sex_couple	married	relationship_quality	relationship_start_year	marriage_year	break_up_year	relationship_duration	sex_frequency	wave	flirts_with_partner	fights_with_partner	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach	children
5795	2586225	57.0	masters_degree	female	white	100k_125k	working_paid_employee	male	58.0	white	prof_doct_degree	no	not_married	excellent	2019.0	NaN	NaN	1.000000	NaN	2020	a_few_times_a_week	0_times	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
5371	2330205	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2020	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4116	1570547	61.0	some_college	male	white	100k_125k	working_paid_employee	NaN	NaN	NaN	NaN	no	married	excellent	NaN	NaN	NaN	38.000000	once_or_twice_a_week	2020	less_than_once_a_month	0_times	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0
1270	2011087	29.0	high_school_grad	male	white	7k_10k	working_paid_employee	female	23.0	white	high_school_grad	no	not_married	NaN	2016.0	NaN	2017.0	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0
7626	1570547	62.0	some_college	male	white	100k_125k	working_paid_employee	NaN	NaN	NaN	NaN	no	married	excellent	NaN	NaN	NaN	40.166668	2_to_3_times_a_month	2022	less_than_once_a_month	0_times	no_change	no_change	no	no	fully_vaccinated_and_booster	fully_vaccinated_and_booster	completely_agree	2.0
3719	924575	56.0	prof_doct_degree	female	other	125k_150k	working_paid_employee	NaN	NaN	NaN	NaN	no	not_married	good	NaN	NaN	NaN	19.000000	NaN	2020	1_to_3_times_a_month	0_times	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0
2076	2449593	71.0	associate_degree	male	white	over_250k	working_self_employed	female	68.0	white	prof_doct_degree	no	married	fair	1972.0	1973.0	NaN	44.750000	once_a_month_or_less	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0
6036	2699919	42.0	high_school_grad	female	hispanic	100k_125k	not_working_looking	NaN	NaN	NaN	NaN	no	married	excellent	NaN	NaN	NaN	25.000000	3_to_6_times_a_week	2020	a_few_times_a_week	0_times	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.0
1634	2219759	23.0	some_college	female	white	125k_150k	working_paid_employee	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0
9503	2682019	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1958	2401785	41.0	bach_degree	male	hispanic	25k_30k	working_paid_employee	male	48.0	white	some_college	yes	married	good	2012.0	2016.0	NaN	4.750000	2_to_3_times_a_month	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0
1612	2212021	50.0	high_school_grad	female	white	75k_85k	working_paid_employee	male	52.0	white	high_school_grad	no	married	excellent	1983.0	1985.0	NaN	34.500000	once_or_twice_a_week	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.0
6392	2844081	30.0	associate_degree	male	white	35k_40k	working_paid_employee	NaN	NaN	NaN	NaN	NaN	not_married	NaN	NaN	NaN	NaN	NaN	NaN	2020	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0
1092	1928049	54.0	associate_degree	female	hispanic	75k_85k	working_paid_employee	male	48.0	other	some_college	no	not_married	excellent	2002.0	NaN	NaN	15.500000	once_a_month_or_less	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
6791	2932429	35.0	bach_degree	male	white	125k_150k	working_paid_employee	NaN	NaN	NaN	NaN	no	married	good	NaN	NaN	NaN	14.000000	once_a_month_or_less	2020	never	1_time	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0
4025	1420887	78.0	bach_degree	female	white	175k_200k	not_working_other	NaN	NaN	NaN	NaN	no	married	excellent	NaN	NaN	NaN	55.000000	once_a_month_or_less	2020	never	0_times	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
1023	1905539	57.0	high_school_grad	male	white	100k_125k	working_self_employed	female	54.0	white	high_school_grad	no	not_married	NaN	1986.0	1988.0	2006.0	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
5290	2296263	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2020	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9679	2765923	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
464	1359305	63.0	high_school_grad	male	white	150k_175k	working_self_employed	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0

NaN Percentage

Below we can note the % of NaN values in each column.

We can note that most columns still has a major % of NaN values.

These high percentages are mostly dependent on the year of the survey.

For example:

2017 is missing primarily COVID related info.
2020 is missing primarily partner info, chronological info and COVID related info.
2022 is missing primarily partner info and chronological info.

This makes sense as the documentation notes that:

Variables in survey changed from year to year slightly.
“Response rate was 3510/6753=52% in 2017, 2107/2431= 87% in 2020, and 1722/2073=83% in 2022. The Denominators in 2020 and 2022 include only subjects who remained in the KnowledgePanel, as they were the only subjects eligible to be contacted.”

df_cleaned.shape

(10530, 30)

nan_percentage_per_column = df_cleaned.isna().mean() * 100
print(nan_percentage_per_column)

id                             0.000000
subject_age                   30.303894
subject_education             30.303894
subject_sex                   30.303894
subject_ethnicity             30.303894
subject_income_category       30.303894
subject_employment_status     30.303894
partner_sex                   65.536562
partner_age                   65.707502
partner_ethnicity             65.660019
partner_education             65.622032
same_sex_couple               39.563153
married                       31.405508
relationship_quality          44.586895
relationship_start_year       66.666667
marriage_year                 79.116809
break_up_year                 96.077873
relationship_duration         46.087369
sex_frequency                 47.853751
wave                           0.000000
flirts_with_partner           70.940171
fights_with_partner           71.975309
rel_change_during_pandemic    87.388414
inc_change_during_pandemic    83.741690
subject_had_covid             83.732194
partner_had_covid             86.904084
subject_vaccinated            83.817664
partner_vaccinated            87.245964
agree_covid_approach          87.435897
children                      30.303894
dtype: float64

nan_percentage_per_column_by_wave = (
    df_cleaned
    .groupby('wave')
    .apply(lambda g: g.isna().mean() * 100)
    .transpose()
)

print(nan_percentage_per_column_by_wave)

wave                              2017        2020       2022
id                            0.000000    0.000000   0.000000
subject_age                   0.000000   39.971510  50.940171
subject_education             0.000000   39.971510  50.940171
subject_sex                   0.000000   39.971510  50.940171
subject_ethnicity             0.000000   39.971510  50.940171
subject_income_category       0.000000   39.971510  50.940171
subject_employment_status     0.000000   39.971510  50.940171
partner_sex                   3.390313   95.868946  97.350427
partner_age                   3.874644   95.868946  97.378917
partner_ethnicity             3.732194   95.897436  97.350427
partner_education             3.618234   95.897436  97.350427
same_sex_couple               3.304843   53.475783  61.908832
married                       3.304843   39.971510  50.940171
relationship_quality         18.888889   52.962963  61.908832
relationship_start_year       6.666667   95.925926  97.407407
marriage_year                38.205128   99.658120  99.487179
break_up_year                88.319088   99.943020  99.971510
relationship_duration        21.225071   54.045584  62.991453
sex_frequency                23.475783   55.897436  64.188034
wave                          0.000000    0.000000   0.000000
flirts_with_partner         100.000000   51.225071  61.595442
fights_with_partner         100.000000   53.589744  62.336182
rel_change_during_pandemic  100.000000  100.000000  62.165242
inc_change_during_pandemic  100.000000  100.000000  51.225071
subject_had_covid           100.000000  100.000000  51.196581
partner_had_covid           100.000000  100.000000  60.712251
subject_vaccinated          100.000000  100.000000  51.452991
partner_vaccinated          100.000000  100.000000  61.737892
agree_covid_approach        100.000000  100.000000  62.307692
children                      0.000000   39.971510  50.940171

C:\Users\Paco\AppData\Local\Temp\ipykernel_21604\437808906.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  .apply(lambda g: g.isna().mean() * 100)

We can remove some of the rows that are fully empty (even if they have values in “id” and “wave”)

cols_to_check = df_cleaned.columns.difference(['id', 'wave'])
df_cleaned = df_cleaned.dropna(subset=cols_to_check, how='all')

df_cleaned.shape

(7339, 30)

nan_percentage_per_column_by_wave = (
    df_cleaned
    .groupby('wave')
    .apply(lambda g: g.isna().mean() * 100)
    .transpose()
)

print(nan_percentage_per_column_by_wave)

wave                              2017        2020       2022
id                            0.000000    0.000000   0.000000
subject_age                   0.000000    0.000000   0.000000
subject_education             0.000000    0.000000   0.000000
subject_sex                   0.000000    0.000000   0.000000
subject_ethnicity             0.000000    0.000000   0.000000
subject_income_category       0.000000    0.000000   0.000000
subject_employment_status     0.000000    0.000000   0.000000
partner_sex                   3.390313   93.118178  94.599303
partner_age                   3.874644   93.118178  94.657375
partner_ethnicity             3.732194   93.165638  94.599303
partner_education             3.618234   93.165638  94.599303
same_sex_couple               3.304843   22.496440  22.357724
married                       3.304843    0.000000   0.000000
relationship_quality         18.888889   21.642145  22.357724
relationship_start_year       6.666667   93.213099  94.715447
marriage_year                38.205128   99.430470  98.954704
break_up_year                88.319088   99.905078  99.941928
relationship_duration        21.225071   23.445657  24.564460
sex_frequency                23.475783   26.530612  27.003484
wave                          0.000000    0.000000   0.000000
flirts_with_partner         100.000000   18.747034  21.718931
fights_with_partner         100.000000   22.686284  23.228804
rel_change_during_pandemic  100.000000  100.000000  22.880372
inc_change_during_pandemic  100.000000  100.000000   0.580720
subject_had_covid           100.000000  100.000000   0.522648
partner_had_covid           100.000000  100.000000  19.918699
subject_vaccinated          100.000000  100.000000   1.045296
partner_vaccinated          100.000000  100.000000  22.009292
agree_covid_approach        100.000000  100.000000  23.170732
children                      0.000000    0.000000   0.000000

C:\Users\Paco\AppData\Local\Temp\ipykernel_21604\437808906.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  .apply(lambda g: g.isna().mean() * 100)

Final Reorganization of Columns

Giving amount of missing data, “Partner variables” and “Chronological variables” that have significant %NaN rates in 2020 and 2022 could be removed for specific inquiries. However, I would propose to keep them in the overall dataset.

Therefore, the final data set would be:

df_cleaned.columns

Index(['id', 'subject_age', 'subject_education', 'subject_sex',
       'subject_ethnicity', 'subject_income_category',
       'subject_employment_status', 'partner_sex', 'partner_age',
       'partner_ethnicity', 'partner_education', 'same_sex_couple', 'married',
       'relationship_quality', 'relationship_start_year', 'marriage_year',
       'break_up_year', 'relationship_duration', 'sex_frequency', 'wave',
       'flirts_with_partner', 'fights_with_partner',
       'rel_change_during_pandemic', 'inc_change_during_pandemic',
       'subject_had_covid', 'partner_had_covid', 'subject_vaccinated',
       'partner_vaccinated', 'agree_covid_approach', 'children'],
      dtype='object')

data = df_cleaned[['id', 'wave',  #Identifiers
                   
                   'subject_age', 'subject_education', 'subject_sex',  #Subject variables
                   'subject_ethnicity', 'subject_income_category', 'subject_employment_status',
                   
                   'partner_sex', 'partner_age', 'partner_ethnicity', 'partner_education',  #Partner variables
                   
                   'same_sex_couple', 'married', 'sex_frequency', 'flirts_with_partner', 'fights_with_partner', #Couple Habits
                   
                   'relationship_start_year', 'marriage_year', 'break_up_year', #Chronology
                   'relationship_duration', 
                   
                   'children',  #Kids Info
                   
                   'rel_change_during_pandemic', 'inc_change_during_pandemic', #Pandemic Vars
                   'subject_had_covid', 'partner_had_covid', 'subject_vaccinated',
                   'partner_vaccinated', 'agree_covid_approach', 
                   
                   'relationship_quality' #Outcome
       ]]

data.head()

	id	wave	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	same_sex_couple	married	sex_frequency	flirts_with_partner	fights_with_partner	relationship_start_year	marriage_year	break_up_year	relationship_duration	children	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach	relationship_quality
0	53001	2017	48.0	high_school_grad	female	2_plus_eth	60k_75k	working_paid_employee	male	46.0	white	associate_degree	no	married	once_or_twice_a_week	NaN	NaN	2013.0	2014.0	NaN	3.583333	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
1	71609	2017	68.0	some_college	female	white	50k_60k	working_paid_employee	male	71.0	white	some_college	no	married	once_a_month_or_less	NaN	NaN	1964.0	1969.0	NaN	52.750000	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
2	106983	2017	39.0	associate_degree	male	white	85k_100k	working_paid_employee	female	49.0	white	some_college	no	married	once_or_twice_a_week	NaN	NaN	1999.0	2002.0	NaN	17.583334	5.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
3	121759	2017	54.0	high_school_grad	male	white	100k_125k	working_paid_employee	female	59.0	asian	masters_degree	no	married	2_to_3_times_a_month	NaN	NaN	1990.0	1991.0	NaN	27.416666	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
4	158083	2017	48.0	some_college	male	white	75k_85k	working_paid_employee	female	34.0	white	associate_degree	no	not_married	NaN	NaN	NaN	2011.0	2013.0	2014.0	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Final Data Review

for column in df_cleaned.columns:
    print(f"Column: {column}")
    print(f"Data type: {df_cleaned[column].dtype}")
    print(f"Unique values: {df_cleaned[column].unique()}\n")

Column: id
Data type: int64
Unique values: [  53001   71609  106983 ... 2968971 2969933 2972135]

Column: subject_age
Data type: float64
Unique values: [48. 68. 39. 54. 59. 72. 55. 73. 46. 43. 57. 50. 61. 79. 58. 64. 81. 70.
 80. 53. 51. 74. 56. 40. 36. 22. 47. 78. 67. 25. 65. 38. 24. 66. 35. 26.
 60. 71. 27. 29. 34. 76. 21. 41. 28. 19. 49. 86. 20. 23. 44. 84. 62. 63.
 45. 52. 77. 75. 42. 82. 69. 92. 85. 32. 37. 33. 30. 31. 90. 83. 18. 87.
 93. 89. 91. 88. 95. 97. 98.]

Column: subject_education
Data type: category
Unique values: ['high_school_grad', 'some_college', 'associate_degree', 'bach_degree', 'masters_degree', ..., '11th', '7th_8th_grade', '1st_4th_grade', 'no_education', '5th_6th_grade']
Length: 14
Categories (14, object): ['no_education' < '1st_4th_grade' < '5th_6th_grade' < '7th_8th_grade' ... 'associate_degree' < 'bach_degree' < 'masters_degree' < 'prof_doct_degree']

Column: subject_sex
Data type: object
Unique values: ['female' 'male']

Column: subject_ethnicity
Data type: object
Unique values: ['2_plus_eth' 'white' 'black' 'other' 'hispanic']

Column: subject_income_category
Data type: category
Unique values: ['60k_75k', '50k_60k', '85k_100k', '100k_125k', '75k_85k', ..., '15k_20k', '20k_25k', 'under_5k', '7k_10k', '5k_7k']
Length: 21
Categories (21, object): ['under_5k' < '5k_7k' < '7k_10k' < '10k_12k' ... '150k_175k' < '175k_200k' < '200k_250k' < 'over_250k']

Column: subject_employment_status
Data type: object
Unique values: ['working_paid_employee' 'not_working_disabled' 'not_working_retired'
 'not_working_looking' 'working_self_employed' 'not_working_other'
 'not_working_temp_layoff']

Column: partner_sex
Data type: object
Unique values: ['male' 'female' nan 'other']

Column: partner_age
Data type: float64
Unique values: [46. 71. 49. 59. 34. 52. 75. 55. 79. 37. 63. 51. 61. 62. 74. 50. 65. 58.
 83. 47. 70. 77. 42. 69. 24. 53. 56. 54. 57. 32. 44. 72. 28. 64. 30. nan
 33. 60. 43. 25. 27. 31. 80. 21. 78. 67. 29. 40. 39. 76. 23. 41. 45. 82.
 26. 66. 68. 86. 36. 84. 18. 20. 38. 73. 19. 81. 22. 91. 85. 48. -1. 87.
 89. 35. 10. 90. 94. 95. 17. 14. 16. 11.]

Column: partner_ethnicity
Data type: object
Unique values: ['white' 'asian' 'black' 'american_indian' 'other' nan]

Column: partner_education
Data type: category
Unique values: ['associate_degree', 'some_college', 'masters_degree', 'bach_degree', 'high_school_grad', ..., '7th_8th_grade', '10th', 'no_education', '5th_6th_grade', '1st_4th_grade']
Length: 15
Categories (14, object): ['no_education' < '1st_4th_grade' < '5th_6th_grade' < '7th_8th_grade' ... 'associate_degree' < 'bach_degree' < 'masters_degree' < 'prof_doct_degree']

Column: same_sex_couple
Data type: object
Unique values: ['no' nan 'yes']

Column: married
Data type: object
Unique values: ['married' 'not_married' nan]

Column: relationship_quality
Data type: object
Unique values: ['excellent' nan 'good' 'fair' 'very_poor' 'poor']

Column: relationship_start_year
Data type: float64
Unique values: [2013. 1964. 1999. 1990. 2011. 1993. 1981. 1983. 1996. 2001. 2014. 1984.
 1987. 1988. 1962. 1989. 1954. 1970. 2007.   nan 1960. 2015. 1978. 2006.
 2016. 1991. 1986. 1997. 2010. 2017. 1968. 1998. 1980. 2004. 1982. 1972.
 2002. 1977. 2008. 1974. 1985. 1994. 1979. 2009. 1959. 1965. 2012. 1971.
 1976. 1961. 1958. 1995. 1969. 1953. 1973. 2003. 1992. 2000. 1967. 1966.
 1963. 2005. 1975. 1955. 1952. 1947. 1957. 1951. 1950. 1956. 1948. 1949.
 1942. 2020. 2019. 2018. 1920. 2021. 2022.]

Column: marriage_year
Data type: float64
Unique values: [2014. 1969. 2002. 1991. 2013. 1997.   nan 1983. 2017. 2001. 1990. 2007.
 1964. 1985. 1984. 1956. 1995. 1972. 2008. 1960. 1986. 2000. 2009. 1987.
 1965. 1978. 1970. 2011. 1973. 2006. 1975. 1981. 1977. 1963. 1988. 1980.
 1976. 1999. 1992. 1996. 1961. 1974. 1994. 1955. 2016. 2003. 1982. 1998.
 1971. 1989. 1979. 1966. 2005. 1962. 1967. 1968. 2010. 2015. 2004. 1993.
 1959. 2012. 1951. 1952. 1957. 1958. 1950. 1944. 1948. 1953. 2019. 2018.
 2020. 2022. 2021.]

Column: break_up_year
Data type: float64
Unique values: [  nan 2014. 1989. 1985. 1984. 2016. 2017. 2012. 2013. 2015. 2007. 2006.
 2003. 2009. 1996. 2000. 1999. 2010. 1987. 1990. 1976. 2001. 1994. 1997.
 1978. 2004. 1992. 1975. 2011. 2002. 2008. 1986. 1993. 1980. 1995. 1988.
 1998. 1977. 2005. 1983. 1991. 2018. 2020. 2022.]

Column: relationship_duration
Data type: float64
Unique values: [ 3.58333325e+00  5.27500000e+01  1.75833340e+01  2.74166660e+01
             nan  2.35833340e+01  3.42500000e+01  2.36666660e+01
  2.10000000e+01  1.64166660e+01  3.41666675e+00  3.34166679e+01
  2.98333340e+01  2.89166660e+01  2.67500000e+01  2.80000000e+01
  3.59166679e+01  6.29166679e+01  2.45000000e+01  2.44166660e+01
  4.70833321e+01  1.03333330e+01  1.77500000e+01  1.66666663e+00
  3.89166679e+01  1.80833340e+01  1.09166670e+01  3.58333321e+01
  2.40833340e+01  2.88333340e+01  2.80833340e+01  2.65000000e+01
  3.43333321e+01  3.11666660e+01  2.75000000e+00  5.83333349e+00
  5.66666651e+00  1.95833340e+01  5.26666679e+01  3.95000000e+01
  4.90833321e+01  6.41666651e+00  1.91666660e+01  3.65833321e+01
  1.33333330e+01  3.47500000e+01  4.53333321e+01  1.46666670e+01
  3.99166679e+01  9.16666698e+00  4.25833321e+01  3.18333340e+01
  2.30000000e+01  3.81666679e+01  4.02500000e+01  1.15000000e+01
  3.90833321e+01  4.93333321e+01  8.16666698e+00  3.02500000e+01
  5.78333321e+01  5.15833321e+01  5.16666651e+00  3.10833340e+01
  3.67500000e+01  3.88333321e+01  4.56666679e+01  3.30000000e+01
  3.32500000e+01  4.61666679e+01  2.78333340e+01  1.33333337e+00
  1.97500000e+01  3.80833321e+01  3.16666660e+01  3.97500000e+01
  2.02500000e+01  5.95000000e+01  2.24166660e+01  3.40833321e+01
  4.10000000e+01  6.66666687e-01  4.13333321e+01  4.12500000e+01
  1.00000000e+01  2.08333325e+00  7.83333349e+00  3.22500000e+01
  2.03333340e+01  9.41666698e+00  5.65833321e+01  3.75000000e+00
  1.00833330e+01  2.58333325e+00  4.31666679e+01  4.87500000e+01
  3.66666675e+00  2.77500000e+01  4.79166679e+01  2.69166660e+01
  6.37500000e+01  2.16666675e+00  4.58333349e+00  3.51666679e+01
  4.40000000e+01  1.26666670e+01  1.50000000e+01  2.20833340e+01
  1.41666670e+01  3.52500000e+01  5.48333321e+01  2.30833340e+01
  4.50833321e+01  3.72500000e+01  3.24166679e+01  5.28333321e+01
  6.50000000e+00  2.96666660e+01  2.71666660e+01  5.90833321e+01
  4.05000000e+01  1.93333340e+01  2.49166660e+01  1.65833340e+01
  2.91666675e+00  5.19166679e+01  1.66666672e-01  1.40000000e+01
  2.63333340e+01  4.98333321e+01  5.07500000e+01  2.90833340e+01
  2.50000000e-01  2.56666660e+01  2.68333340e+01  4.26666679e+01
  4.41666651e+00  1.04166670e+01  5.37500000e+01  0.00000000e+00
  3.55833321e+01  4.91666651e+00  3.04166660e+01  1.78333340e+01
  3.00833340e+01  5.46666679e+01  9.58333302e+00  2.12500000e+01
  4.94166679e+01  3.57500000e+01  1.91666663e+00  2.94166660e+01
  1.89166660e+01  1.54166670e+01  1.05833330e+01  1.94166660e+01
  3.30833321e+01  1.25000000e+01  1.61666660e+01  4.24166679e+01
  1.68333340e+01  4.60833321e+01  8.50000000e+00  3.91666675e+00
  4.71666679e+01  5.00000000e-01  4.28333321e+01  4.15000000e+01
  2.29166660e+01  3.84166679e+01  2.19166660e+01  1.57500000e+01
  6.83333349e+00  3.96666679e+01  1.55000000e+01  6.16666679e+01
  2.70833340e+01  4.16666679e+01  5.21666679e+01  2.00833340e+01
  3.50000000e+00  2.82500000e+01  1.36666670e+01  6.50833359e+01
  2.87500000e+01  2.52500000e+01  5.13333321e+01  5.25000000e+01
  2.51666660e+01  3.68333321e+01  2.91666660e+01  3.54166679e+01
  4.75833321e+01  2.06666660e+01  5.72500000e+01  3.41666679e+01
  1.10000000e+01  4.92500000e+01  5.10833321e+01  3.20000000e+01
  2.79166660e+01  2.33333340e+01  2.64166660e+01  2.76666660e+01
  4.46666679e+01  5.66666679e+01  3.20833321e+01  5.55833321e+01
  5.79166679e+01  2.48333340e+01  5.30000000e+01  5.55000000e+01
  1.58333337e+00  1.08333337e+00  3.39166679e+01  1.24166670e+01
  7.91666651e+00  9.00000000e+00  4.00000000e+01  5.08333321e+01
  2.00000000e+01  3.35000000e+01  5.36666679e+01  4.33333349e+00
  3.37500000e+01  1.58333330e+01  4.39166679e+01  4.83333321e+01
  4.60000000e+01  8.33333358e-02  5.00000000e+01  4.10833321e+01
  2.04166660e+01  2.99166660e+01  2.50000000e+00  4.50000000e+01
  5.00000000e+00  1.20000000e+01  1.90833340e+01  1.88333340e+01
  7.50000000e+00  2.38333340e+01  1.62500000e+01  8.91666698e+00
  8.41666698e+00  7.00000000e+01  2.33333325e+00  2.14166660e+01
  4.85000000e+01  1.80000000e+01  5.25833321e+01  4.89166679e+01
  2.55000000e+01  2.09166660e+01  6.00000000e+00  2.34166660e+01
  2.83333340e+01  4.01666679e+01  5.99166679e+01  5.60000000e+01
  3.77500000e+01  2.57500000e+01  3.25000000e+00  9.25000000e+00
  3.63333321e+01  1.59166670e+01  1.32500000e+01  1.25833330e+01
  1.85833340e+01  7.50000000e-01  4.38333321e+01  3.86666679e+01
  3.66666679e+01  5.83333313e-01  2.92500000e+01  2.16666660e+01
  6.60000000e+01  5.50833321e+01  1.16666663e+00  5.20000000e+01
  5.44166679e+01  3.65000000e+01  3.23333321e+01  8.83333302e+00
  4.04166679e+01  4.69166679e+01  2.17500000e+01  3.21666679e+01
  5.03333321e+01  1.39166670e+01  4.57500000e+01  1.75000000e+01
  7.75000000e+00  2.73333340e+01  5.58333321e+01  2.46666660e+01
  2.62500000e+01  1.30000000e+01  9.91666698e+00  2.28333340e+01
  1.83333337e+00  2.58333340e+01  2.15833340e+01  2.95833340e+01
  2.26666660e+01  3.76666679e+01  4.45833321e+01  9.75000000e+00
  3.25833321e+01  5.11666679e+01  3.17500000e+01  2.01666660e+01
  2.75833340e+01  5.47500000e+01  1.87500000e+01  4.80833321e+01
  4.97500000e+01  4.90000000e+01  3.45000000e+01  5.91666651e+00
  3.13333340e+01  3.64166679e+01  3.70000000e+01  5.09166679e+01
  4.81666679e+01  6.50000000e+01  4.03333321e+01  3.93333321e+01
  4.76666679e+01  4.08333349e+00  4.48333321e+01  1.35000000e+01
  4.07500000e+01  4.16666651e+00  3.75833321e+01  4.23333321e+01
  5.50000000e+00  4.64166679e+01  1.11666670e+01  1.41666663e+00
  2.50833340e+01  1.44166670e+01  2.11666660e+01  5.49166679e+01
  4.20833321e+01  2.60000000e+01  4.30833321e+01  1.83333340e+01
  3.28333321e+01  1.69166660e+01  1.82500000e+01  3.90000000e+01
  3.50000000e+01  1.63333340e+01  1.74166660e+01  1.23333330e+01
  1.37500000e+01  5.41666651e+00  2.23333340e+01  5.70833321e+01
  8.33333302e+00  3.55000000e+01  4.59166679e+01  2.95000000e+01
  3.53333321e+01  3.74166679e+01  5.39166679e+01  6.30000000e+01
  6.91666651e+00  1.19166670e+01  8.66666698e+00  4.34166679e+01
  2.41666675e+00  6.58333349e+00  4.83333349e+00  5.69166679e+01
  8.00000000e+00  1.47500000e+01  2.22500000e+01  3.61666679e+01
  4.00833321e+01  1.52500000e+01  3.19166660e+01  3.06666660e+01
  5.24166679e+01  5.77500000e+01  3.08333340e+01  5.42500000e+01
  4.30000000e+01  5.60833321e+01  1.72500000e+01  3.08333325e+00
  2.47500000e+01  4.88333321e+01  4.18333321e+01  2.90000000e+01
  3.91666679e+01  3.25000000e+01  1.13333330e+01  3.38333321e+01
  2.42500000e+01  4.17500000e+01  2.05000000e+01  4.49166679e+01
  4.73333321e+01  3.83333325e+00  3.83333321e+01  1.45000000e+01
  6.75000000e+00  4.55833321e+01  4.95833321e+01  2.75000000e+01
  2.70000000e+01  1.05000000e+01  8.33333313e-01  2.05833340e+01
  3.00000000e+00  9.66666698e+00  1.76666660e+01  4.84166679e+01
  2.53333340e+01  1.67500000e+01  6.33333349e+00  3.50833321e+01
  2.81666660e+01  1.18333330e+01  2.25000000e+01  5.02500000e+01
  4.35833321e+01  5.06666679e+01  3.33333343e-01  1.60833340e+01
  4.70000000e+01  6.25000000e+00  1.21666670e+01  4.82500000e+01
  1.16666670e+01  9.83333302e+00  3.85000000e+01  5.63333321e+01
  1.50000000e+00  4.75000000e+01  3.95833321e+01  6.58333359e+01
 -3.33333343e-01  2.41666660e+01  1.50833330e+01  8.58333302e+00
  6.67500000e+01  4.50000000e+00  5.58333349e+00  1.84166660e+01
  2.08333340e+01  4.06666679e+01  5.53333321e+01  7.41666651e+00
  4.75000000e+00  2.86666660e+01  2.15000000e+01  1.45833330e+01
  5.17500000e+01  2.59166660e+01  1.55833330e+01  4.36666679e+01
  1.25000000e+00  1.06666670e+01  4.19166679e+01  1.73333340e+01
  1.10833330e+01  5.91666679e+01  1.70833340e+01  3.40000000e+01
  1.66666660e+01  2.43333340e+01  4.20000000e+01  1.70000000e+01
  7.16666651e+00  5.57500000e+01  2.40000000e+01  6.08333349e+00
  1.92500000e+01  4.08333321e+01  1.01666670e+01  2.93333340e+01
  2.66666660e+01  5.75000000e+00  6.26666679e+01  4.22500000e+01
  1.95000000e+01  1.90000000e+01  3.60000000e+01  4.68333321e+01
  1.29166670e+01  1.75000000e+00  1.27500000e+01  9.16666687e-01
  6.21666679e+01  4.54166679e+01  9.08333302e+00  8.08333302e+00
  5.40833321e+01  4.78333321e+01  7.08333349e+00  4.11666679e+01
  3.15000000e+01  7.25000000e+00  4.14166679e+01  1.99166660e+01
  6.08333321e+01  2.25000000e+00  1.30833330e+01  1.56666670e+01
  1.15833330e+01  8.25000000e+00  7.00000000e+00  4.00000000e+00
  3.33333325e+00  4.65833321e+01  6.16666651e+00  1.02500000e+01
  6.00000000e+01  4.33333321e+01  4.65000000e+01  2.60833340e+01
  1.31666670e+01  1.17500000e+01  2.55833340e+01  4.42500000e+01
  1.51666670e+01  3.09166660e+01  2.65833340e+01  1.48333330e+01
  5.38333321e+01  9.33333302e+00  2.66666675e+00  2.25833340e+01
  6.28333321e+01  4.72500000e+01  2.50000000e+01  1.42500000e+01
  1.38333330e+01  4.16666657e-01  2.13333340e+01  2.37500000e+01
  3.98333321e+01  5.22500000e+01  1.53333330e+01  3.85833321e+01
  3.00000000e+01  7.58333349e+00  4.25000000e+00  3.78333321e+01
  3.87500000e+01  5.50000000e+01  4.85833321e+01  2.21666660e+01
  6.47500000e+01  8.75000000e+00  3.16666675e+00  1.20833330e+01
  6.66666651e+00  3.75000000e+01  1.43333330e+01  3.56666679e+01
  1.40833330e+01  3.82500000e+01  4.58333321e+01  1.35833330e+01
  5.33333349e+00  4.66666651e+00  1.22500000e+01  5.20833321e+01
  1.08333330e+01  3.71666679e+01  2.83333325e+00  4.41666679e+01
  6.90833359e+01  3.92500000e+01  3.80000000e+01  4.21666679e+01
  3.46666679e+01  5.76666679e+01  1.07500000e+01  5.25000000e+00
  6.07500000e+01  4.51666679e+01  5.10000000e+01  2.00000000e+00
  1.28333330e+01  1.34166670e+01  6.40833359e+01  5.68333321e+01
  2.07500000e+01  3.49166679e+01  4.09166679e+01  5.45000000e+01
  5.35833321e+01  1.60000000e+01  3.60833321e+01  6.80000000e+01
  2.61666660e+01  5.54166679e+01  4.77500000e+01  1.00000000e+00
  3.33333321e+01  1.12500000e+01  6.00833321e+01  1.86666660e+01
  1.79166660e+01  3.03333340e+01  3.73333321e+01  5.43333321e+01
  5.15000000e+01  3.05833340e+01  4.55000000e+01  1.49166670e+01
  4.15833321e+01  3.10000000e+01  3.05000000e+01  6.06666679e+01
  3.48333321e+01  3.94166679e+01  4.47500000e+01  2.72500000e+01
  3.15833340e+01  3.44166679e+01  2.84166660e+01  4.67500000e+01
  2.35000000e+01  3.14166660e+01  4.37500000e+01  3.69166679e+01
  2.45833340e+01  5.01666679e+01  6.12500000e+01  1.14166670e+01
  2.20000000e+01  7.51666641e+01  6.45833359e+01  7.33333349e+00
  6.69166641e+01  3.29166679e+01  5.12500000e+01  1.71666660e+01
  5.45833321e+01  2.27500000e+01  5.62500000e+01  5.90000000e+01
  5.08333349e+00  4.63333321e+01  2.32500000e+01  6.11666679e+01
  4.74166679e+01  2.10833340e+01  9.50000000e+00  5.30833321e+01
  1.81666660e+01  2.85833340e+01  3.36666679e+01  4.25000000e+01
  4.66666679e+01  3.70833321e+01  6.73333359e+01  4.05833321e+01
  5.65000000e+01  1.96666660e+01  2.18333340e+01  2.85000000e+01
  4.44166679e+01  3.12500000e+01  4.45000000e+01  6.23333321e+01
  4.40833321e+01  5.00833321e+01  6.10833321e+01  1.98333340e+01
  4.99166679e+01  5.34166679e+01  2.97500000e+01  5.35000000e+01
  5.88333321e+01  6.19166679e+01  4.29166679e+01  5.95833321e+01
  5.40000000e+01  5.05000000e+01  1.65000000e+01  2.54166660e+01
  5.86666679e+01  3.79166679e+01  3.45833321e+01  7.66666651e+00
  2.31666660e+01  6.10000000e+01  6.70000000e+01  5.80000000e+01
  5.70000000e+01  4.80000000e+01  7.40000000e+01  6.90000000e+01
  7.10000000e+01  6.20000000e+01  6.40000000e+01  7.20000000e+01
  5.74166679e+01  3.35833321e+01  6.75833359e+01  5.73333321e+01
  6.25000000e+01  4.35000000e+01  4.27500000e+01  6.41666641e+01
  5.18333321e+01  6.97500000e+01  5.71666679e+01  6.02500000e+01
  5.83333321e+01  7.46666641e+01  5.31666679e+01  3.01666660e+01
  5.97500000e+01  6.05000000e+01  5.23333321e+01  5.05833321e+01
  3.26666679e+01  5.89166679e+01  5.16666679e+01  5.29166679e+01
  7.14166641e+01  3.27500000e+01  5.64166679e+01  3.07500000e+01
  5.85000000e+01  4.32500000e+01  2.39166660e+01  5.32500000e+01
  6.04166679e+01  4.62500000e+01  6.54166641e+01  5.56666679e+01
  5.04166679e+01  6.15000000e+01  5.82500000e+01  7.26666641e+01
  5.85833321e+01  5.61666679e+01  6.55000000e+01  5.67500000e+01
  6.53333359e+01  3.62500000e+01  5.14166679e+01  5.87500000e+01
  1.85000000e+01  7.15833359e+01  6.09166679e+01  6.13333321e+01
  3.31666679e+01  4.52500000e+01  4.96666679e+01  5.94166679e+01
  1.01750000e+02]

Column: sex_frequency
Data type: object
Unique values: ['once_or_twice_a_week' 'once_a_month_or_less' '2_to_3_times_a_month' nan
 '3_to_6_times_a_week' 'once_or_more_a_day']

Column: wave
Data type: object
Unique values: ['2017' '2020' '2022']

Column: flirts_with_partner
Data type: category
Unique values: [NaN, 'never', 'a_few_times_a_week', 'less_than_once_a_month', '1_to_3_times_a_month', 'every_day', 'once_a_week']
Categories (6, object): ['every_day' < 'a_few_times_a_week' < 'once_a_week' < '1_to_3_times_a_month' < 'less_than_once_a_month' < 'never']

Column: fights_with_partner
Data type: category
Unique values: [NaN, '0_times', '3_times', '2_times', '1_time', '7_or_more_times', '4_times', '5_times', '6_times']
Categories (8, object): ['0_times' < '1_time' < '2_times' < '3_times' < '4_times' < '5_times' < '6_times' < '7_or_more_times']

Column: rel_change_during_pandemic
Data type: object
Unique values: [nan 'better_than_before' 'no_change' 'worse_than_before']

Column: inc_change_during_pandemic
Data type: object
Unique values: [nan 'no_change' 'worse' 'much_worse' 'better' 'much_better']

Column: subject_had_covid
Data type: object
Unique values: [nan 'no' 'yes']

Column: partner_had_covid
Data type: object
Unique values: [nan 'yes' 'no']

Column: subject_vaccinated
Data type: object
Unique values: [nan 'not_vaccinated' 'fully_vaccinated_and_booster'
 'fully_vaccinated_no_booster' 'partially_vaccinated']

Column: partner_vaccinated
Data type: object
Unique values: [nan 'not_vaccinated' 'fully_vaccinated_and_booster'
 'partially_vaccinated' 'fully_vaccinated_no_booster']

Column: agree_covid_approach
Data type: object
Unique values: [nan 'completely_agree' 'mostly_agree' 'completely_disagree'
 'mostly_disagree']

Column: children
Data type: float64
Unique values: [ 1.  2.  5.  4.  6.  3.  8. 10.  7.  9. 12.]

Selecting only 2022 data set:

During the client meeting on May 21st, 2025. It was decided to only keep 2022 data, while removing mostly sparse columns, id and wave values.

clean = data

clean.head()

	id	wave	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	partner_sex	partner_age	partner_ethnicity	partner_education	same_sex_couple	married	sex_frequency	flirts_with_partner	fights_with_partner	relationship_start_year	marriage_year	break_up_year	relationship_duration	children	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach	relationship_quality
0	53001	2017	48.0	high_school_grad	female	2_plus_eth	60k_75k	working_paid_employee	male	46.0	white	associate_degree	no	married	once_or_twice_a_week	NaN	NaN	2013.0	2014.0	NaN	3.583333	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
1	71609	2017	68.0	some_college	female	white	50k_60k	working_paid_employee	male	71.0	white	some_college	no	married	once_a_month_or_less	NaN	NaN	1964.0	1969.0	NaN	52.750000	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
2	106983	2017	39.0	associate_degree	male	white	85k_100k	working_paid_employee	female	49.0	white	some_college	no	married	once_or_twice_a_week	NaN	NaN	1999.0	2002.0	NaN	17.583334	5.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
3	121759	2017	54.0	high_school_grad	male	white	100k_125k	working_paid_employee	female	59.0	asian	masters_degree	no	married	2_to_3_times_a_month	NaN	NaN	1990.0	1991.0	NaN	27.416666	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	excellent
4	158083	2017	48.0	some_college	male	white	75k_85k	working_paid_employee	female	34.0	white	associate_degree	no	not_married	NaN	NaN	NaN	2011.0	2013.0	2014.0	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

clean.shape

(7339, 30)

Selecting only 2022 and removing NA from selected columns.

data_2022 = clean[clean['wave'] == '2022'].dropna(subset=['rel_change_during_pandemic', 'relationship_quality'])

data_2022.shape

(1328, 30)

data_2022.columns.tolist()

['id',
 'wave',
 'subject_age',
 'subject_education',
 'subject_sex',
 'subject_ethnicity',
 'subject_income_category',
 'subject_employment_status',
 'partner_sex',
 'partner_age',
 'partner_ethnicity',
 'partner_education',
 'same_sex_couple',
 'married',
 'sex_frequency',
 'flirts_with_partner',
 'fights_with_partner',
 'relationship_start_year',
 'marriage_year',
 'break_up_year',
 'relationship_duration',
 'children',
 'rel_change_during_pandemic',
 'inc_change_during_pandemic',
 'subject_had_covid',
 'partner_had_covid',
 'subject_vaccinated',
 'partner_vaccinated',
 'agree_covid_approach',
 'relationship_quality']

Final selection of columns for clean dataframe.

data_2022 = data_2022[[
 'subject_age',
 'subject_education',
 'subject_sex',
 'subject_ethnicity',
 'subject_income_category',
 'subject_employment_status',
 'same_sex_couple',
 'married',
 'sex_frequency',
 'flirts_with_partner',
 'fights_with_partner',
 'relationship_duration',
 'children',
 'rel_change_during_pandemic',
 'inc_change_during_pandemic',
 'subject_had_covid',
 'partner_had_covid',
 'subject_vaccinated',
 'partner_vaccinated',
 'agree_covid_approach',
 'relationship_quality']]

data_2022.head()

	subject_age	subject_education	subject_sex	subject_ethnicity	subject_income_category	subject_employment_status	same_sex_couple	married	sex_frequency	flirts_with_partner	fights_with_partner	relationship_duration	children	rel_change_during_pandemic	inc_change_during_pandemic	subject_had_covid	partner_had_covid	subject_vaccinated	partner_vaccinated	agree_covid_approach	relationship_quality
7020	53.0	high_school_grad	female	white	35k_40k	working_paid_employee	no	not_married	once_or_twice_a_week	a_few_times_a_week	0_times	1.500000	2.0	better_than_before	no_change	no	yes	not_vaccinated	not_vaccinated	completely_agree	excellent
7021	72.0	some_college	female	white	75k_85k	working_paid_employee	no	married	once_a_month_or_less	never	7_or_more_times	57.416668	1.0	no_change	worse	no	no	fully_vaccinated_and_booster	fully_vaccinated_and_booster	mostly_agree	good
7022	43.0	associate_degree	male	white	75k_85k	working_paid_employee	no	married	once_or_twice_a_week	a_few_times_a_week	2_times	22.333334	5.0	no_change	worse	no	no	fully_vaccinated_and_booster	fully_vaccinated_and_booster	completely_agree	excellent
7025	64.0	some_college	male	white	75k_85k	working_paid_employee	no	married	once_or_twice_a_week	1_to_3_times_a_month	0_times	28.250000	2.0	no_change	no_change	no	no	fully_vaccinated_and_booster	fully_vaccinated_and_booster	completely_agree	good
7027	60.0	high_school_grad	female	black	75k_85k	working_paid_employee	no	married	once_or_twice_a_week	a_few_times_a_week	0_times	38.916668	3.0	better_than_before	no_change	no	no	not_vaccinated	partially_vaccinated	completely_agree	excellent

data_2022.shape

(1328, 21)

Saving CSV

data_2022.to_csv("../data/clean/hcmst.csv", index=False)