lending club 贷款数据 2018年第二季度的贷款数据 "id","member_id","loan_amnt","funded_amnt","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"
2022-05-20 10:45:12
100.99MB
1
该程序采用STL的sort排序函数，对CSV中的数据进行排序（排序时不包括首行（第0行）。可以按字符、数值和IP排序。排序方式还包括普通排序（整行作为一个参数进行比较）、将整行数据从指定列开始作为字符串起始按字符编码大小排序以及单独按某列列值作为排序标准进行排序。其中，按数值和按IP排序的排序标准只能在仅以某一列作为标准排序（常见的excel选择某列排序同时复选上扩展选定区域的排序方式）时才能使用该功能。
2022-05-20 09:32:49
11.58MB
1
今天遇到的问题是，要将一份csv数据读入dataframe，但某些列中含有NA值。对于这些列来说，NA应该作为一个有意义的level，而不是缺失值，但read_csv函数会自动将类似的缺失值理解为缺失值并变为NaN。
看pandas文档中read_csv函数中这两个参数的描述，默认会将’-1.#IND’, ‘1.#QNAN’, ‘1.#IND’, ‘-1.#QNAN’, ‘#N/A N/A’,’#N/A’, ‘N/A’, ‘NA’, ‘#NA’, ‘NULL’, ‘NaN’, ‘-NaN’, ‘nan’, ‘-nan’, ”转换为NaN，且na_values参数还支持定义另外的应处理为缺失值的
2022-05-19 00:15:20
35KB
1