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Using Lending Club dataset I have a dataframe with characteristics of loans of some borrowers. Here is their distribution of the sub-grades:

>>>plt.figure(figsize=(16, 6))
>>>sns.countplot(x="sub_grade", data=result)

enter image description here

I wonder if I can easily segment people based on prime and sub prime loans (and are "prime" and "sub prime" synonyms of "grades" and "sub-grades" ?)

Here is an excerpt of the dataset :

funded_amnt_inv term    int_rate    installment grade   sub_grade   emp_title   emp_length  home_ownership  annual_inc  verification_status issue_d loan_status pymnt_plan  url desc    purpose title   zip_code    addr_state  dti delinq_2yrs earliest_cr_line    inq_last_6mths  mths_since_last_delinq  mths_since_last_record  open_acc    pub_rec revol_bal   revol_util  total_acc   initial_list_status out_prncp   out_prncp_inv   total_pymnt total_pymnt_inv total_rec_prncp total_rec_int   total_rec_late_fee  recoveries  collection_recovery_fee last_pymnt_d    last_pymnt_amnt next_pymnt_d    last_credit_pull_d  collections_12_mths_ex_med  mths_since_last_major_derog policy_code application_type    annual_inc_joint    dti_joint   verification_status_joint   acc_now_delinq  tot_coll_amt    tot_cur_bal open_acc_6m open_act_il open_il_12m open_il_24m mths_since_rcnt_il  total_bal_il    il_util open_rv_12m open_rv_24m max_bal_bc  all_util    total_rev_hi_lim    inq_fi  total_cu_tl inq_last_12m    acc_open_past_24mths    avg_cur_bal bc_open_to_buy  bc_util chargeoff_within_12_mths    delinq_amnt mo_sin_old_il_acct  mo_sin_old_rev_tl_op    mo_sin_rcnt_rev_tl_op   mo_sin_rcnt_tl  mort_acc    mths_since_recent_bc    mths_since_recent_bc_dlq    mths_since_recent_inq   mths_since_recent_revol_delinq  num_accts_ever_120_pd   num_actv_bc_tl  num_actv_rev_tl num_bc_sats num_bc_tl   num_il_tl   num_op_rev_tl   num_rev_accts   num_rev_tl_bal_gt_0 num_sats    num_tl_120dpd_2m    num_tl_30dpd    num_tl_90g_dpd_24m  num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75    pub_rec_bankruptcies    tax_liens   tot_hi_cred_lim total_bal_ex_mort   total_bc_limit  total_il_high_credit_limit  revol_bal_joint sec_app_earliest_cr_line    sec_app_inq_last_6mths  sec_app_mort_acc    sec_app_open_acc    sec_app_revol_util  sec_app_open_act_il sec_app_num_rev_accts   sec_app_chargeoff_within_12_mths    sec_app_collections_12_mths_ex_med  sec_app_mths_since_last_major_derog hardship_flag   hardship_type   hardship_reason hardship_status deferral_term   hardship_amount hardship_start_date hardship_end_date   payment_plan_start_date hardship_length hardship_dpd    hardship_loan_status    orig_projected_additional_accrued_interest  hardship_payoff_balance_amount  hardship_last_payment_amount    disbursement_method debt_settlement_flag    debt_settlement_flag_date   settlement_status   settlement_date settlement_amount   settlement_percentage   settlement_term
749995  7605    NaN NaN 15000   15000   15000.0 36 months   13.56   509.47  C   C1  Driver  4 years RENT    55000.0 Not Verified    2018-12-01  Current n   NaN NaN debt_consolidation  Debt consolidation  171xx   PA  17.15   0.0 Oct-2012    0.0 NaN NaN 9.0 0.0 7277    40.0    14.0    w   14316.22    14316.22    1001.99 1001.99 683.78  318.21  0.0 0.0 0.0 Feb-2019    509.47  Mar-2019    Feb-2019    0.0 NaN 1   Individual  NaN NaN NaN 0.0 0.0 21229.0 0.0 2.0 1.0 3.0 8.0 13952.0 85.0    1.0 1.0 4778.0  61.0    18200.0 1.0 0.0 2.0 4.0 2359.0  2457.0  69.7    0.0 0.0 74.0    54.0    12.0    8.0 0.0 35.0    NaN 8.0 NaN 0.0 2.0 5.0 2.0 2.0 4.0 7.0 10.0    5.0 9.0 0.0 0.0 0.0 2.0 100.0   50.0    0.0 0.0 34632.0 21229.0 8100.0  16432.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Cash    N   NaN NaN NaN NaN NaN NaN
749996  35399   NaN NaN 8000    8000    8000.0  36 months   6.46    245.05  A   A1  Realtor 2 years MORTGAGE    100000.0    Not Verified    2018-12-01  Current n   NaN NaN debt_consolidation  Debt consolidation  231xx   VA  9.90    1.0 Sep-2001    0.0 21.0    NaN 13.0    0.0 17646   51.7    29.0    w   7389.00 7389.00 692.18  692.18  611.00  81.18   0.0 0.0 0.0 Feb-2019    245.05  Mar-2019    Feb-2019    0.0 NaN 1   Individual  NaN NaN NaN 0.0 3323.0  326631.0    1.0 2.0 0.0 0.0 31.0    13286.0 57.0    2.0 3.0 2173.0  54.0    34100.0 0.0 3.0 0.0 3.0 27219.0 12434.0 26.0    0.0 0.0 82.0    206.0   2.0 2.0 2.0 2.0 63.0    17.0    21.0    0.0 4.0 5.0 5.0 11.0    3.0 10.0    24.0    5.0 13.0    0.0 0.0 0.0 2.0 82.8    40.0    0.0 0.0 381832.0    30932.0 16800.0 23370.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN DirectPay   N   NaN NaN NaN NaN NaN NaN
749997  8452    NaN NaN 40000   40000   40000.0 60 months   16.14   975.71  C   C4  Director of Operations  3 years MORTGAGE    157630.0    Verified    2018-12-01  Current n   NaN NaN credit_card Credit card refinancing 972xx   OR  16.61   1.0 May-2004    0.0 18.0    NaN 11.0    0.0 15761   56.7    21.0    w   39562.28    39562.28    1155.05 1155.05 437.72  717.33  0.0 0.0 0.0 Feb-2019    1208.85 Mar-2019    Feb-2019    0.0 NaN 1   Individual  NaN NaN NaN 0.0 0.0 474781.0    1.0 4.0 1.0 1.0 5.0 59310.0 38.0    0.0 0.0 9029.0  43.0    27800.0 0.0 5.0 0.0 1.0 43162.0 9453.0  59.8    0.0 0.0 71.0    175.0   27.0    5.0 2.0 27.0    NaN NaN NaN 0.0 3.0 4.0 3.0 4.0 6.0 6.0 13.0    4.0 11.0    0.0 0.0 0.0 1.0 95.2    66.7    0.0 0.0 542479.0    75071.0 23500.0 100389.0    NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN N   NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Cash    N   NaN NaN NaN NaN NaN NaN
...

I was wondering if I should use a k-mean on some features with the same number of sub-grades and see if it returns the same sub_grade classes. Yet I only know how to do k-means on one dimension.

I use python3

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