Griffiths Lott
3 years ago
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DAILY.MANUAL.INVOICE |
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CONTRACTS THAT WERE NOT INVOICED |
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PAGE 04-26-22 1 |
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CHRG BUSINESS |
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CONTRACT.NO........ UATB.OIC.DUE RENTAL DUE......... UATB.IDS.OIC.PAYME TYPEM..... OUTSTANDING BALANCE.... SEGMENT. BOOKING.DATE BRANCH |
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100-2453558-003 05/09/2022 183.71 0.00 MISC 201.16 001.000 03/09/2018 9 |
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100-2453558-003 04/09/2022 183.71 180.67 MISC 20.49 001.000 03/09/2018 9 |
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100-2453558-003 03/09/2022 183.71 183.71 MISC 17.45 001.000 03/09/2018 9 |
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100-1665517-003 05/15/2022 412.97 0.00 MISC 438.78 001.000 10/31/2014 9 |
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100-4850431-001 05/10/2022 411.80 0.00 MISC 441.21 001.000 12/10/2018 3 |
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100-4462739-001 04/18/2022 157.08 0.00 RENT 157.08 001.000 06/18/2018 9 |
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100-4850431-001 04/10/2022 411.80 0.00 MISC 441.21 001.000 12/10/2018 3 |
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100-3500858-001 05/12/2022 262.37 0.00 MISC 279.42 001.000 04/13/2016 9 |
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100-3725849-003 05/13/2022 559.32 0.00 MISC 612.45 001.000 10/19/2017 9 |
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100-3500858-001 04/12/2022 262.37 0.00 MISC 279.42 001.000 04/13/2016 9 |
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047-2580598-001 04/05/2022 0.00 0.00 MISC 72.53 001.000 03/06/2014 9 |
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100-3725849-003 03/13/2022 559.32 559.32 MISC 53.13 001.000 10/19/2017 9 |
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100-4566489-001 04/14/2022 0.00 354.04 MISC 2.25 001.000 06/14/2018 9 |
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100-4566489-001 05/14/2022 0.00 328.00 MISC 28.29 001.000 06/14/2018 9 |
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100-5382471-001 04/15/2022 1,128.00 0.00 MISC 1,228.11 001.000 10/09/2019 9 |
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100-5382471-001 05/15/2022 1,128.00 0.00 MISC 1,228.11 001.000 10/09/2019 9 |
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100-9723689-001 04/20/2022 0.00 0.00 RENT 571.58 001.000 04/20/2022 10 |
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100-9723689-001 04/20/2022 0.00 0.00 RENT 571.58 001.000 04/20/2022 10 |
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100-7219911-001 04/20/2022 0.00 0.00 RENT 813.08 001.000 04/20/2022 9 |
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100-1354567-002 05/25/2022 170.00 0.00 RENT 170.00 001.000 05/31/2016 9 |
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100-6651721-001 07/14/2021 0.00 761.00 MISC 53.27 001.000 07/14/2021 9 |
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100-2081987-008 05/25/2022 407.15 0.00 MISC 439.73 001.000 10/02/2017 9 |
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100-2139037-002 05/20/2022 105.00 0.00 MISC 111.67 001.000 03/20/2017 9 |
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100-3725849-003 04/13/2022 559.32 440.68 MISC 171.77 001.000 10/19/2017 9 |
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100-3344078-002 05/01/2022 -28.53 18.47 RENT 63.00 001.000 10/01/2020 9 |
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100-2081987-008 03/25/2022 407.15 0.00 MISC 439.73 001.000 10/02/2017 9 |
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100-1354567-002 04/25/2022 170.00 0.00 RENT 170.00 001.000 05/31/2016 9 |
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100-2081987-008 04/25/2022 407.15 0.00 MISC 439.73 001.000 10/02/2017 9 |
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100-2081987-008 02/25/2022 407.15 407.15 MISC 32.58 001.000 10/02/2017 9 |
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100-3876959-007 04/21/2022 61.07 0.00 RENT 61.07 001.000 06/21/2018 9 |
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100-1637209-005 05/20/2022 2,023.20 0.00 RENT 2,023.20 001.000 04/25/2022 9 |
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100-7146771-001 04/20/2022 183.28 167.00 RENT 16.28 001.000 04/25/2022 3 |
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100-7146771-001 05/20/2022 183.28 0.00 RENT 183.28 001.000 04/25/2022 3 |
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100-7045691-001 05/20/2022 244.57 0.00 RENT 244.57 001.000 04/25/2022 3 |
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100-7059671-001 05/20/2022 60.00 0.00 MISC 64.20 001.000 04/25/2022 3 |
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100-7237601-001 04/20/2022 0.00 0.00 RENT 34,192.91 001.000 04/25/2022 3 |
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100-7242461-001 05/20/2022 57.00 0.00 MISC 60.99 001.000 04/25/2022 9 |
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100-7178461-001 05/20/2022 197.45 0.00 MISC 209.30 001.000 04/25/2022 3 |
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100-2611389-007 05/20/2022 171.76 0.00 RENT 171.76 001.000 04/25/2022 3 |
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100-7037791-001 05/01/2022 444.00 0.00 MISC 478.41 001.000 04/25/2022 9 |
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100-7203371-001 05/20/2022 1,566.40 0.00 RENT 1,566.40 001.000 04/25/2022 3 |
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100-6630017-005 05/01/2022 0.00 178.55 MISC 0.01 001.000 04/25/2022 3 |
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100-6738611-001 04/20/2022 0.00 0.00 RENT 4,545.94 001.000 04/25/2022 3 |
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100-6738611-001 04/25/2022 0.00 0.00 RENT 4,545.94 001.000 04/25/2022 3 |
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100-7052571-001 05/14/2022 255.87 0.00 MISC 278.90 001.000 04/25/2022 9 |
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100-1011756-004 05/20/2022 1,001.64 0.00 MISC 1,081.77 001.000 04/25/2022 9 |
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100-6849836-001 05/20/2022 1,077.47 0.00 RENT 1,077.47 001.000 04/25/2022 3 |
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100-3492758-003 05/15/2022 312.41 0.00 RENT 312.41 001.000 04/25/2022 9 |
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100-7156851-001 05/20/2022 150.00 0.00 MISC 159.00 001.000 04/25/2022 3 |
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100-7232561-001 05/20/2022 113.60 0.00 MISC 122.12 001.000 04/25/2022 9 |
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100-3876959-007 05/21/2022 61.07 0.00 RENT 61.07 001.000 06/21/2018 9 |
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100-5382931-003 05/20/2022 146.69 0.00 RENT 146.69 001.000 04/26/2022 3 |
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100-5722341-003 05/20/2022 170.00 0.00 MISC 181.90 001.000 04/26/2022 3 |
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100-7150721-001 04/20/2022 174.96 0.00 RENT 174.96 001.000 04/26/2022 3 |
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100-7150721-001 05/20/2022 174.96 0.00 RENT 174.96 001.000 04/26/2022 3 |
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100-7165521-001 05/20/2022 1,417.88 0.00 RENT 1,417.88 001.000 04/26/2022 3 |
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100-7227921-001 05/20/2022 64.00 0.00 MISC 69.28 001.000 04/26/2022 3 |
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100-4858739-002 05/15/2022 208.00 0.00 MISC 225.16 001.000 04/26/2022 3 |
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100-7100621-001 05/13/2022 880.10 0.00 MISC 954.90 001.000 04/26/2022 9 |
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100-9725556-001 04/25/2022 0.00 0.00 RENT 600.77 001.000 04/26/2022 10 |
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100-9725556-001 04/26/2022 0.00 0.00 RENT 600.77 001.000 04/26/2022 10 |
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100-7209051-001 05/20/2022 1,652.01 0.00 RENT 1,652.01 001.000 04/26/2022 3 |
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100-9660710-001 05/09/2022 174.75 0.00 RENT 174.75 001.000 06/09/2021 10 |
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100-5329301-002 04/20/2022 0.00 0.00 RENT 263.44 001.000 04/26/2022 3 |
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100-7087121-001 05/16/2022 3,294.46 1,125.58 RENT 3,294.46 001.000 02/16/2022 12 |
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100-6602681-003 04/25/2022 0.00 0.00 RENT 478.00 001.000 04/26/2022 3 |
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100-6602681-003 04/25/2022 0.00 0.00 RENT 478.00 001.000 04/26/2022 3 |
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100-6754131-001 05/20/2022 747.75 0.00 RENT 747.75 001.000 04/26/2022 3 |
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100-7214111-001 05/21/2022 542.97 0.00 RENT 542.97 001.000 04/26/2022 9 |
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101-6898811-001 04/20/2022 0.00 0.00 RENT 15,035.55 001.000 04/26/2022 3 |
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100-2406418-003 05/20/2022 200.00 0.00 MISC 219.00 001.000 04/26/2022 9 |
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100-6943901-002 05/16/2022 236.40 0.00 MISC 257.67 001.000 04/26/2022 9 |
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100-1623380-901 05/15/2022 1,769.11 0.00 RENT 1,769.11 001.000 04/26/2022 10 |
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100-7107941-001 05/20/2022 1,038.95 0.00 RENT 1,038.95 001.000 02/23/2022 3 |
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100-7031531-001 05/20/2022 120.00 0.00 MISC 130.92 001.000 04/26/2022 3 |
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100-3630389-005 05/20/2022 168.00 0.00 MISC 181.86 001.000 04/26/2022 3 |
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100-7174941-002 05/20/2022 1,667.38 0.00 MISC 1,804.93 001.000 04/26/2022 9 |
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100-5204521-002 05/25/2022 3,222.20 0.00 RENT 3,222.20 001.000 04/26/2022 12 |
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100-7241571-001 05/20/2022 55.00 0.00 MISC 59.54 001.000 04/26/2022 3 |
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100-7182731-001 04/20/2022 0.00 0.00 RENT 1,025.37 001.000 04/26/2022 3 |
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100-7182731-001 04/26/2022 0.00 0.00 RENT 1,025.37 001.000 04/26/2022 3 |
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100-9726258-001 04/25/2022 0.00 0.00 RENT 255.97 001.000 04/26/2022 10 |
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100-9726258-001 04/26/2022 0.00 0.00 RENT 255.97 001.000 04/26/2022 10 |
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100-7220301-001 04/20/2022 0.00 0.00 RENT 1,238.00 001.000 04/26/2022 3 |
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100-7151521-001 05/15/2022 94.00 0.00 MISC 102.46 001.000 04/26/2022 9 |
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100-7237751-001 05/25/2022 2.00 101.65 MISC 2.14 001.000 04/26/2022 9 |
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100-3876959-005 03/25/2022 0.00 0.00 RENT 60.74 001.000 09/27/2017 9 |
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100-3910629-001 03/25/2022 0.00 0.00 RENT 245.81 001.000 03/30/2017 9 |
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100-3876959-005 04/25/2022 0.00 0.00 RENT 60.74 001.000 09/27/2017 9 |
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100-3910629-001 04/25/2022 0.00 0.00 RENT 245.81 001.000 03/30/2017 9 |
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104-4687809-001 04/25/2022 -2,161.94 140.00 MISC 9.80 001.000 08/29/2018 9 |
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100-3964329-001 04/28/2022 318.13 0.00 MISC 340.40 001.000 03/31/2017 9 |
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100-3964329-001 03/28/2022 318.13 0.00 MISC 340.40 001.000 03/31/2017 9 |
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100-1670517-003 04/16/2022 0.00 0.00 RENT 165.00 001.000 09/16/2021 3 |
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100-4945021-001 05/15/2022 0.00 0.00 RENT 1,357.77 001.000 02/15/2019 3 |
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100-3694757-001 05/01/2022 298.00 0.00 MISC 324.45 001.000 09/02/2016 9 |
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100-3694757-001 04/01/2022 298.00 0.00 MISC 324.45 001.000 09/02/2016 9 |
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100-6651721-002 07/25/2021 0.00 761.00 MISC 53.27 001.000 07/27/2021 9 |
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100-6814061-001 04/06/2022 0.00 169.00 RENT 15.63 001.000 04/06/2022 3 |
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100-7170651-001 04/07/2022 0.00 99.00 RENT 9.50 001.000 04/07/2022 3 |
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100-2446458-002 04/06/2022 865.00 859.26 MISC 66.29 001.000 12/06/2016 9 |
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100-2446458-002 05/06/2022 865.00 0.00 MISC 925.55 001.000 12/06/2016 9 |
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102 records listed |
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@ -1,42 +0,0 @@ |
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DAILY.MANUAL.INVOICE |
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CONTRACTS THAT WERE NOT INVOICED |
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PAGE 06-09-22 1 |
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CHRG BUSINESS |
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CONTRACT.NO........ UATB.OIC.DUE RENTAL DUE......... UATB.IDS.OIC.PAYME TYPEM..... OUTSTANDING BALANCE.... SEGMENT. BOOKING.DATE BRANCH |
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100-0553300-001 04/01/2022 413.81 0.00 RENT 413.81 001.000 11/01/2020 4.3 |
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047-4725409-001 04/14/2022 351.30 0.00 MISC 380.28 001.000 01/14/2019 3 |
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100-1864627-002 05/14/2022 146.71 156.07 MISC 9.36 001.000 07/14/2017 9 |
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100-6651721-001 07/14/2021 0.00 761.00 MISC 53.27 001.000 07/14/2021 9 |
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100-0553300-001 05/01/2022 413.81 0.00 RENT 413.81 001.000 11/01/2020 4.3 |
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100-6651721-002 07/25/2021 0.00 761.00 MISC 53.27 001.000 07/27/2021 9 |
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100-0553300-001 01/01/2022 413.81 319.43 RENT 94.38 001.000 11/01/2020 4.3 |
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047-4725409-001 05/14/2022 351.30 31.54 MISC 348.74 001.000 01/14/2019 3 |
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100-5047921-001 06/15/2022 94.69 0.00 RENT 94.69 001.000 04/11/2019 3 |
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100-3285968-004 05/25/2022 2,027.75 0.00 MISC 2,139.28 001.000 02/27/2019 9 |
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100-0553300-001 06/01/2022 413.81 0.00 RENT 413.81 001.000 11/01/2020 4.3 |
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100-0553300-001 02/01/2022 413.81 0.00 RENT 413.81 001.000 11/01/2020 4.3 |
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100-7218171-001 05/19/2022 0.00 157.00 MISC 10.60 001.000 05/19/2022 3 |
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047-4725409-001 06/14/2022 351.30 0.00 MISC 380.28 001.000 01/14/2019 3 |
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100-5189011-002 07/01/2022 130.58 275.67 RENT 130.58 001.000 07/16/2019 12 |
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100-2772548-003 06/25/2022 -830.51 0.00 MISC 601.48 001.000 05/30/2017 9 |
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100-3285968-004 06/25/2022 2,027.75 0.00 MISC 2,139.28 001.000 02/27/2019 9 |
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047-2903398-005 05/25/2022 0.00 0.00 MISC 58.25 001.000 04/25/2017 9 |
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100-2911448-002 05/25/2022 0.00 1,895.04 RENT 30.43 001.000 04/29/2022 9 |
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100-0553300-001 07/01/2022 413.81 0.00 RENT 413.81 001.000 11/01/2020 4.3 |
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100-0553300-001 03/01/2022 413.81 0.00 RENT 413.81 001.000 11/01/2020 4.3 |
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100-7220451-001 06/06/2022 0.00 171.00 RENT 19.45 001.000 06/06/2022 3 |
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100-7268381-001 06/06/2022 0.00 151.00 RENT 11.33 001.000 06/06/2022 3 |
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100-7314881-001 06/06/2022 0.00 119.00 RENT 9.88 001.000 06/06/2022 3 |
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100-7317551-001 06/25/2022 2,916.00 0.00 RENT 2,916.00 001.000 05/27/2022 12 |
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100-7289411-001 07/03/2022 556.33 0.00 RENT 556.33 001.000 06/03/2022 3 |
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100-7304881-002 07/01/2022 60.90 0.00 MISC 66.27 001.000 06/08/2022 9 |
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100-6943321-002 06/15/2022 8,012.77 0.03 RENT 8,012.77 001.000 04/12/2022 9 |
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100-1477460-002 07/08/2022 34,915.99 0.00 RENT 34,915.99 001.000 06/08/2022 12 |
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100-1516251-002 06/20/2022 1,485.99 0.00 RENT 1,485.99 001.000 09/24/2019 10 |
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100-7352541-001 06/09/2022 1,422.30 0.00 RENT 711.15 001.000 06/09/2022 3 |
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100-7352541-001 06/09/2022 1,422.30 0.00 RENT 711.15 001.000 06/09/2022 3 |
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100-9677665-001 06/25/2022 242.11 0.00 RENT 242.11 001.000 08/30/2021 10 |
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33 records listed |
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@ -1,51 +0,0 @@ |
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DAILY.MANUAL.INVOICE |
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CONTRACTS THAT WERE NOT INVOICED |
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PAGE 06-10-22 1 |
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CHRG BUSINESS |
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CONTRACT.NO........ UATB.OIC.DUE RENTAL DUE......... UATB.IDS.OIC.PAYME TYPEM..... OUTSTANDING BALANCE.... SEGMENT. BOOKING.DATE BRANCH |
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100-1049364-003 03/15/2022 79.56 0.00 RENT 79.56 001.000 10/01/2021 4.3 |
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100-1049364-003 04/15/2022 79.56 0.00 RENT 79.56 001.000 10/01/2021 4.3 |
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100-6651721-001 07/14/2021 0.00 761.00 MISC 53.27 001.000 07/14/2021 9 |
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100-1049364-003 12/15/2021 79.56 68.40 RENT 11.16 001.000 10/01/2021 4.3 |
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100-6651721-002 07/25/2021 0.00 761.00 MISC 53.27 001.000 07/27/2021 9 |
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100-1049364-003 05/15/2022 79.56 0.00 RENT 79.56 001.000 10/01/2021 4.3 |
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100-1049364-003 01/15/2022 79.56 0.00 RENT 79.56 001.000 10/01/2021 4.3 |
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100-2170587-001 05/21/2022 484.62 0.00 RENT 484.62 001.000 03/26/2013 9 |
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100-7218171-001 05/19/2022 0.00 157.00 MISC 10.60 001.000 05/19/2022 3 |
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100-1049364-003 06/15/2022 79.56 0.00 RENT 79.56 001.000 10/01/2021 4.3 |
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100-1049364-003 02/15/2022 79.56 0.00 RENT 79.56 001.000 10/01/2021 4.3 |
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100-2170587-001 06/21/2022 484.62 0.00 RENT 484.62 001.000 03/26/2013 9 |
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100-2772548-003 06/25/2022 -830.51 0.00 MISC 601.48 001.000 05/30/2017 9 |
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047-2903398-005 05/25/2022 0.00 0.00 MISC 58.25 001.000 04/25/2017 9 |
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100-2911448-002 05/25/2022 0.00 1,895.04 RENT 30.43 001.000 04/29/2022 9 |
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100-7220451-001 06/06/2022 0.00 171.00 RENT 19.45 001.000 06/06/2022 3 |
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100-7268381-001 06/06/2022 0.00 151.00 RENT 11.33 001.000 06/06/2022 3 |
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100-7314881-001 06/06/2022 0.00 119.00 RENT 9.88 001.000 06/06/2022 3 |
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100-7352541-001 06/09/2022 0.00 0.00 RENT 711.15 001.000 06/09/2022 3 |
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100-7352541-001 06/09/2022 0.00 0.00 RENT 711.15 001.000 06/09/2022 3 |
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100-9678164-001 06/25/2022 242.11 0.00 RENT 242.11 001.000 08/31/2021 10 |
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100-1570785-001 06/24/2022 765.50 0.00 RENT 765.50 001.000 02/24/2020 10 |
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100-4693949-001 06/17/2022 326.11 0.00 RENT 326.11 001.000 10/17/2018 3 |
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100-1500564-011 07/04/2022 1,239.97 0.00 RENT 1,239.97 001.000 06/09/2022 12 |
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100-9704497-001 06/16/2022 2,127.68 0.00 RENT 2,127.68 001.000 02/16/2022 10 |
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100-7318371-001 07/01/2022 257.76 0.00 RENT 257.76 001.000 06/09/2022 9 |
|
||||||
100-1697447-005 06/20/2022 184.11 0.00 RENT 184.11 001.000 08/23/2019 3 |
|
||||||
100-1697447-004 06/20/2022 287.93 0.00 RENT 287.93 001.000 07/30/2019 3 |
|
||||||
100-4714407-008 07/01/2022 671.57 0.00 MISC 714.21 001.000 06/09/2022 9 |
|
||||||
100-6051107-001 06/20/2022 30.15 39.99 RENT 30.15 001.000 09/08/2020 9 |
|
||||||
100-3240348-006 06/14/2022 227.52 0.00 MISC 241.17 001.000 04/14/2022 9 |
|
||||||
100-6795737-003 07/10/2022 0.00 0.00 RENT 30,000.00 001.000 06/10/2022 12 |
|
||||||
100-9729335-001 06/10/2022 1,841.70 0.00 RENT 920.85 001.000 06/10/2022 10 |
|
||||||
100-9729335-001 06/10/2022 1,841.70 0.00 RENT 920.85 001.000 06/10/2022 10 |
|
||||||
100-7252451-001 06/10/2022 15.63 169.00 RENT 15.63 001.000 06/10/2022 3 |
|
||||||
100-6238218-001 06/19/2022 74.99 0.00 RENT 74.99 001.000 11/19/2021 9 |
|
||||||
100-7337851-001 06/10/2022 216.36 0.00 RENT 108.18 001.000 06/10/2022 3 |
|
||||||
100-7337851-001 06/10/2022 216.36 0.00 RENT 108.18 001.000 06/10/2022 3 |
|
||||||
100-7275051-001 06/10/2022 0.00 843.27 MISC 0.01 001.000 06/10/2022 3 |
|
||||||
100-1017678-003 07/01/2022 718.61 0.00 MISC 777.89 001.000 05/16/2019 12 |
|
||||||
100-4266879-003 07/03/2022 339.42 0.00 MISC 368.95 001.000 06/10/2022 9 |
|
||||||
41 records listed |
|
||||||
@ -1,33 +0,0 @@ |
|||||||
|
|
||||||
DAILY.MANUAL.INVOICE |
|
||||||
CONTRACTS THAT WERE NOT INVOICED |
|
||||||
PAGE 06-13-22 1 |
|
||||||
|
|
||||||
CHRG BUSINESS |
|
||||||
CONTRACT.NO........ UATB.OIC.DUE RENTAL DUE......... UATB.IDS.OIC.PAYME TYPEM..... OUTSTANDING BALANCE.... SEGMENT. BOOKING.DATE BRANCH |
|
||||||
|
|
||||||
100-6651721-001 07/14/2021 0.00 761.00 MISC 53.27 001.000 07/14/2021 9 |
|
||||||
100-6550098-001 05/20/2022 619.26 108.80 RENT 510.46 001.000 05/25/2021 3 |
|
||||||
100-6651721-002 07/25/2021 0.00 761.00 MISC 53.27 001.000 07/27/2021 9 |
|
||||||
100-7218171-001 05/19/2022 0.00 157.00 MISC 10.60 001.000 05/19/2022 3 |
|
||||||
100-2772548-003 06/25/2022 -830.51 0.00 MISC 601.48 001.000 05/30/2017 9 |
|
||||||
047-2903398-005 05/25/2022 0.00 0.00 MISC 58.25 001.000 04/25/2017 9 |
|
||||||
100-2911448-002 05/25/2022 0.00 1,895.04 RENT 30.43 001.000 04/29/2022 9 |
|
||||||
100-6795737-003 07/10/2022 30,000.00 0.00 RENT 30,000.00 001.000 06/10/2022 12 |
|
||||||
100-7252451-001 06/10/2022 0.00 169.00 RENT 15.63 001.000 06/10/2022 3 |
|
||||||
100-6238218-001 06/19/2022 74.99 0.00 RENT 74.99 001.000 11/19/2021 9 |
|
||||||
100-7337851-001 06/10/2022 0.00 0.00 RENT 108.18 001.000 06/10/2022 3 |
|
||||||
100-7337851-001 06/10/2022 0.00 0.00 RENT 108.18 001.000 06/10/2022 3 |
|
||||||
100-7275051-001 06/10/2022 0.00 843.27 MISC 0.01 001.000 06/10/2022 3 |
|
||||||
100-4266879-003 07/03/2022 339.42 0.00 MISC 368.95 001.000 06/10/2022 9 |
|
||||||
100-7203501-002 07/01/2022 40.00 0.00 MISC 42.50 001.000 06/10/2022 9 |
|
||||||
100-7282331-001 06/10/2022 0.00 0.00 RENT 1,432.54 001.000 06/10/2022 3 |
|
||||||
100-9735444-001 06/10/2022 0.00 0.00 RENT 214.90 001.000 06/10/2022 10 |
|
||||||
100-9735444-001 06/10/2022 0.00 0.00 RENT 214.90 001.000 06/10/2022 10 |
|
||||||
100-6930421-001 06/10/2022 0.00 0.00 RENT 1,982.24 001.000 06/10/2022 3 |
|
||||||
100-6930421-001 06/10/2022 0.00 0.00 RENT 1,982.24 001.000 06/10/2022 3 |
|
||||||
100-6721551-003 06/20/2022 1,210.36 0.00 RENT 1,210.36 001.000 09/24/2021 3 |
|
||||||
100-6721551-002 06/20/2022 613.92 0.00 RENT 613.92 001.000 09/23/2021 3 |
|
||||||
100-7136271-001 07/20/2022 176.54 0.00 RENT 176.54 001.000 03/22/2022 3 |
|
||||||
100-7171001-001 06/17/2022 68.92 0.00 RENT 68.92 001.000 03/17/2022 9 |
|
||||||
24 records listed |
|
||||||
@ -1,40 +0,0 @@ |
|||||||
|
|
||||||
|
|
||||||
DAILY.MANUAL.INVOICE |
|
||||||
CONTRACTS THAT WERE NOT INVOICED |
|
||||||
PAGE 06-14-22 1 |
|
||||||
|
|
||||||
CHRG BUSINESS |
|
||||||
CONTRACT.NO........ UATB.OIC.DUE RENTAL DUE......... UATB.IDS.OIC.PAYME TYPEM..... OUTSTANDING BALANCE.... SEGMENT. BOOKING.DATE BRANCH |
|
||||||
|
|
||||||
100-7269201-001 06/14/2022 17.11 172.00 RENT 17.11 001.000 06/14/2022 3 |
|
||||||
100-3654949-003 06/24/2022 1,086.99 0.00 RENT 1,086.99 001.000 09/24/2019 9 |
|
||||||
100-3654949-002 06/20/2022 1,789.52 0.00 RENT 1,789.52 001.000 09/19/2019 9 |
|
||||||
100-7199137-001 06/20/2022 1,068.97 0.00 RENT 1,068.97 001.000 04/29/2022 3 |
|
||||||
100-7284391-001 07/08/2022 91.25 0.00 RENT 91.25 001.000 06/08/2022 9 |
|
||||||
100-7209951-001 06/14/2022 0.00 160.00 MISC 11.20 001.000 06/14/2022 3 |
|
||||||
100-7334181-001 07/01/2022 338.41 0.00 MISC 366.32 001.000 06/14/2022 9 |
|
||||||
100-9570778-001 06/24/2022 637.91 0.00 RENT 637.91 001.000 02/24/2020 10 |
|
||||||
100-7234311-001 06/14/2022 15.81 204.00 RENT 15.81 001.000 06/14/2022 3 |
|
||||||
100-7294191-001 06/14/2022 14.80 160.00 RENT 14.80 001.000 06/14/2022 3 |
|
||||||
100-0849508-002 04/15/2022 2,392.50 0.00 RENT 2,392.50 001.000 10/01/2021 4.3 |
|
||||||
100-6651721-001 07/14/2021 0.00 761.00 MISC 53.27 001.000 07/14/2021 9 |
|
||||||
100-6651721-002 07/25/2021 0.00 761.00 MISC 53.27 001.000 07/27/2021 9 |
|
||||||
100-0849508-002 05/15/2022 2,392.50 0.00 RENT 2,392.50 001.000 10/01/2021 4.3 |
|
||||||
100-0849508-002 06/15/2022 2,392.50 0.00 RENT 2,392.50 001.000 10/01/2021 4.3 |
|
||||||
100-2772548-003 06/25/2022 -830.51 0.00 MISC 601.48 001.000 05/30/2017 9 |
|
||||||
100-3618019-001 06/25/2022 428.34 0.00 MISC 466.36 001.000 09/28/2016 9 |
|
||||||
047-2903398-005 05/25/2022 0.00 0.00 MISC 58.25 001.000 04/25/2017 9 |
|
||||||
100-2911448-002 05/25/2022 0.00 1,895.04 RENT 30.43 001.000 04/29/2022 9 |
|
||||||
100-7252451-001 06/10/2022 0.00 169.00 RENT 15.63 001.000 06/10/2022 3 |
|
||||||
100-7275051-001 06/10/2022 0.00 843.27 MISC 0.01 001.000 06/10/2022 3 |
|
||||||
100-7113121-001 07/13/2022 2,500.39 0.00 RENT 2,500.39 001.000 06/13/2022 3 |
|
||||||
100-7342981-001 07/08/2022 103.56 0.00 MISC 109.77 001.000 06/08/2022 9 |
|
||||||
100-7113391-002 07/01/2022 2,275.30 0.00 RENT 2,275.30 001.000 06/13/2022 3 |
|
||||||
100-5398561-002 07/01/2022 786.33 0.00 MISC 847.27 001.000 06/13/2022 9 |
|
||||||
100-4457819-001 06/20/2022 5,785.12 0.00 RENT 5,785.12 001.000 05/31/2018 3 |
|
||||||
100-3744149-012 07/08/2022 50.00 0.00 MISC 53.00 001.000 06/13/2022 9 |
|
||||||
100-3154768-008 07/01/2022 897.26 0.00 MISC 980.27 001.000 06/13/2022 9 |
|
||||||
100-7318021-001 07/03/2022 295.00 0.00 MISC 320.08 001.000 06/13/2022 9 |
|
||||||
100-2146226-002 07/01/2022 558.96 0.00 RENT 558.96 001.000 06/13/2022 9 |
|
||||||
30 records listed |
|
||||||
@ -1,32 +0,0 @@ |
|||||||
|
|
||||||
|
|
||||||
DAILY.MANUAL.INVOICE |
|
||||||
CONTRACTS THAT WERE NOT INVOICED |
|
||||||
PAGE 06-15-22 1 |
|
||||||
|
|
||||||
CHRG BUSINESS |
|
||||||
CONTRACT.NO........ UATB.OIC.DUE RENTAL DUE......... UATB.IDS.OIC.PAYME TYPEM..... OUTSTANDING BALANCE.... SEGMENT. BOOKING.DATE BRANCH |
|
||||||
|
|
||||||
100-7269201-001 06/14/2022 0.00 172.00 RENT 17.11 001.000 06/14/2022 3 |
|
||||||
100-7209951-001 06/14/2022 0.00 160.00 MISC 11.20 001.000 06/14/2022 3 |
|
||||||
100-7334181-001 07/01/2022 338.41 0.00 MISC 366.32 001.000 06/14/2022 9 |
|
||||||
100-7234311-001 06/14/2022 0.00 204.00 RENT 15.81 001.000 06/14/2022 3 |
|
||||||
100-7294191-001 06/14/2022 0.00 160.00 RENT 14.80 001.000 06/14/2022 3 |
|
||||||
100-9570729-001 06/24/2022 637.91 0.00 RENT 637.91 001.000 02/24/2020 10 |
|
||||||
100-7080901-001 06/14/2022 0.00 0.00 MISC 177.62 001.000 06/14/2022 3 |
|
||||||
100-7313341-001 07/09/2022 217.00 0.00 RENT 217.00 001.000 06/14/2022 9 |
|
||||||
100-7363251-001 06/14/2022 0.00 0.00 RENT 13,168.80 001.000 06/14/2022 3 |
|
||||||
100-1564449-001 07/03/2022 430.01 0.00 RENT 430.01 001.000 02/03/2020 10 |
|
||||||
100-7233611-001 06/15/2022 17.36 179.00 RENT 17.36 001.000 06/15/2022 3 |
|
||||||
100-7151111-001 06/15/2022 480.40 0.00 RENT 480.40 001.000 06/15/2022 9 |
|
||||||
100-9737145-001 06/15/2022 590.20 0.00 RENT 295.10 001.000 06/15/2022 10 |
|
||||||
100-9737145-001 06/15/2022 590.20 0.00 RENT 295.10 001.000 06/15/2022 10 |
|
||||||
100-7303761-001 08/14/2022 0.00 0.00 RENT 240,632.43 001.000 06/14/2022 12 |
|
||||||
100-6651721-001 07/14/2021 0.00 761.00 MISC 53.27 001.000 07/14/2021 9 |
|
||||||
100-6651721-002 07/25/2021 0.00 761.00 MISC 53.27 001.000 07/27/2021 9 |
|
||||||
100-6726271-001 06/15/2022 1,588.04 0.00 RENT 1,588.04 001.000 08/31/2021 12 |
|
||||||
100-2772548-003 06/25/2022 -830.51 0.00 MISC 601.48 001.000 05/30/2017 9 |
|
||||||
047-2903398-005 05/25/2022 0.00 0.00 MISC 58.25 001.000 04/25/2017 9 |
|
||||||
100-2911448-002 05/25/2022 0.00 1,895.04 RENT 30.43 001.000 04/29/2022 9 |
|
||||||
100-5864201-501 07/07/2022 143.00 0.00 MISC 153.01 001.000 09/01/2020 9 |
|
||||||
22 records listed |
|
||||||
@ -1,142 +0,0 @@ |
|||||||
import os |
|
||||||
import pandas as pd |
|
||||||
from datetime import datetime as dt, timedelta |
|
||||||
import re |
|
||||||
from pathlib import Path |
|
||||||
import time |
|
||||||
import numpy as np |
|
||||||
from pprint import pprint as prt |
|
||||||
|
|
||||||
|
|
||||||
def pfd(df: pd.DataFrame): |
|
||||||
with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also |
|
||||||
print(df) |
|
||||||
|
|
||||||
|
|
||||||
def create_line_divider(breakage_list: list): |
|
||||||
""" |
|
||||||
This allows for the creation of a custom data extractor |
|
||||||
Breakage list defines the split points that will be used for the line |
|
||||||
Example |
|
||||||
Given breakage_list [10, 20, 30] |
|
||||||
using slot_num 0 in the resulting extract_line_slot will yield |
|
||||||
characters 0 - 10 from the string. |
|
||||||
Slot 1 would give characters 10 - 20 |
|
||||||
""" |
|
||||||
def extract_line_slot(slot_num : int, line_string: str, debug : bool = False): |
|
||||||
""" |
|
||||||
Pulls data from a line/string using break points defined by the |
|
||||||
parent function. |
|
||||||
ONLY USE THIS FUNCTION THROUGH CREATION USING 'create_line_extractor' |
|
||||||
Will automatically convert numbers to floats |
|
||||||
""" |
|
||||||
# We can't have a slot number higher than the number of slots |
|
||||||
assert(slot_num < len(breakage_list)+1) |
|
||||||
low_range = 0 if slot_num == 0 else breakage_list[slot_num-1] |
|
||||||
high_range = len(line_string) if slot_num == len(breakage_list) else breakage_list[slot_num] |
|
||||||
# In order to create a float we need to remove the , from the string |
|
||||||
data = line_string[low_range:high_range].strip().replace(",", "") |
|
||||||
try: data = float(data) |
|
||||||
except: pass |
|
||||||
if debug: |
|
||||||
print(f"Slot num: {slot_num} | Low: {low_range} | High: {high_range} | Data: {data}") |
|
||||||
return data |
|
||||||
return extract_line_slot |
|
||||||
|
|
||||||
|
|
||||||
def minv(report: str, save_name: str): |
|
||||||
lines = report.splitlines() |
|
||||||
data_extractor = create_line_divider([15,32,52,71,83,107,116,128]) |
|
||||||
extracted_data_dict = { |
|
||||||
"ContractNumber" : [], |
|
||||||
"UTAB_OIC_DUE" : [], |
|
||||||
"RentalDue" : [], |
|
||||||
"UTAB_OIC_PYMT" : [], |
|
||||||
"ChargeType" : [], |
|
||||||
"OutstandBalance" : [], |
|
||||||
"BizSegment" : [], |
|
||||||
"BookingDate" : [], |
|
||||||
"Branch" : [], |
|
||||||
} |
|
||||||
columns = list(extracted_data_dict.keys()) |
|
||||||
for line in enumerate(lines): |
|
||||||
if re.search(contract_number_regex, line[1]) != None: |
|
||||||
[extracted_data_dict[columns[c]].append(data_extractor(c,line[1],debug=False)) for c in range(0,len(columns))] |
|
||||||
#All the list lengths need to be the same so if anything was missed it will fail to build |
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
# ( bookdate != today & rent = 0 ) OR (outstanding > 100 & rent = 0) |
|
||||||
# dt.today().strftime("%m/%m/%Y") |
|
||||||
filtered = dataframe[ |
|
||||||
((dataframe["BookingDate"] != '04/26/2022') & (dataframe["RentalDue"] == 0)) |\ |
|
||||||
((dataframe["RentalDue"] == 0 ) & (dataframe["OutstandBalance"] > 100))] |
|
||||||
filtered.to_excel(save_name, index=False) |
|
||||||
return filtered |
|
||||||
|
|
||||||
current_output = [ |
|
||||||
'100-1011756-004', |
|
||||||
'100-1354567-002', |
|
||||||
'100-1637209-005', |
|
||||||
'100-1665517-003', |
|
||||||
'100-1670517-003', |
|
||||||
'100-2081987-008', |
|
||||||
'100-2139037-002', |
|
||||||
'100-2446458-002', |
|
||||||
'100-2453558-003', |
|
||||||
'100-2611389-007', |
|
||||||
'100-3492758-003', |
|
||||||
'100-3500858-001', |
|
||||||
'100-3694757-001', |
|
||||||
'100-3725849-003', |
|
||||||
'100-3876959-007', |
|
||||||
'100-3910629-001', |
|
||||||
'100-3964329-001', |
|
||||||
'100-4462739-001', |
|
||||||
'100-4850431-001', |
|
||||||
'100-4945021-001', |
|
||||||
'100-5382471-001', |
|
||||||
'100-6738611-001', |
|
||||||
'100-6849836-001', |
|
||||||
'100-7037791-001', |
|
||||||
'100-7045691-001', |
|
||||||
'100-7052571-001', |
|
||||||
'100-7059671-001', |
|
||||||
'100-7087121-001', |
|
||||||
'100-7107941-001', |
|
||||||
'100-7146771-001', |
|
||||||
'100-7156851-001', |
|
||||||
'100-7178461-001', |
|
||||||
'100-7203371-001', |
|
||||||
'100-7219911-001', |
|
||||||
'100-7232561-001', |
|
||||||
'100-7237601-001', |
|
||||||
'100-7242461-001', |
|
||||||
'100-9660710-001', |
|
||||||
'100-9723689-001', |
|
||||||
] |
|
||||||
|
|
||||||
contract_number_regex = "\d{3}-\d{7}-\d{3}" |
|
||||||
|
|
||||||
with open("2022.05.04_MINV_C", errors="replace") as ifile: |
|
||||||
report = ifile.read() |
|
||||||
|
|
||||||
fin_df = minv(report, "man_inv_test.xlsx") |
|
||||||
pfd(fin_df) |
|
||||||
il_contracts = fin_df.ContractNumber.to_list() |
|
||||||
prt(il_contracts) |
|
||||||
|
|
||||||
extra_contracts = [] |
|
||||||
not_included = [] |
|
||||||
for c in il_contracts: |
|
||||||
if c not in current_output: |
|
||||||
extra_contracts.append(c) |
|
||||||
for c in current_output: |
|
||||||
if c not in il_contracts: |
|
||||||
not_included.append(c) |
|
||||||
|
|
||||||
print("\nExtra Contracts:") |
|
||||||
prt(extra_contracts) |
|
||||||
print("Not Included Contracts:") |
|
||||||
prt(not_included) |
|
||||||
print(f"MATCHING CONTRACTS: {il_contracts == current_output}") |
|
||||||
print(f"Current # contract {len(current_output)} | ILE Processed Contracts: {len(il_contracts)}") |
|
||||||
print(f"# Extra contracts included: {len(extra_contracts)} | # Contracts not included: {len(not_included)}") |
|
||||||
@ -1,152 +0,0 @@ |
|||||||
import os |
|
||||||
import pandas as pd |
|
||||||
from datetime import datetime as dt, timedelta |
|
||||||
import sys, getopt |
|
||||||
import re |
|
||||||
from pathlib import Path |
|
||||||
import time |
|
||||||
from pprint import pprint as prt |
|
||||||
import numpy as np |
|
||||||
|
|
||||||
contract_number_regex = "\d{3}-\d{7}-\d{3}" |
|
||||||
|
|
||||||
def create_line_divider(breakage_list: list): |
|
||||||
""" |
|
||||||
This allows for the creation of a custom data extractor |
|
||||||
Breakage list defines the split points that will be used for the line |
|
||||||
Example |
|
||||||
Given breakage_list [10, 20, 30] |
|
||||||
using slot_num 0 in the resulting extract_line_slot will yield |
|
||||||
characters 0 - 10 from the string. |
|
||||||
Slot 1 would give characters 10 - 20 |
|
||||||
""" |
|
||||||
def extract_line_slot(slot_num : int, line_string: str, debug : bool = False): |
|
||||||
""" |
|
||||||
Pulls data from a line/string using break points defined by the |
|
||||||
parent function. |
|
||||||
ONLY USE THIS FUNCTION THROUGH CREATION USING 'create_line_extractor' |
|
||||||
Will automatically convert numbers to floats |
|
||||||
""" |
|
||||||
assert(slot_num < len(breakage_list)+1) |
|
||||||
low_range = 0 if slot_num == 0 else breakage_list[slot_num-1] |
|
||||||
high_range = len(line_string) if slot_num == len(breakage_list) else breakage_list[slot_num] |
|
||||||
data = line_string[low_range:high_range].strip().replace(",", "") |
|
||||||
try: data = float(data) |
|
||||||
except: pass |
|
||||||
if debug: |
|
||||||
print(f"Slot num: {slot_num} | Low: {low_range} | High: {high_range} | Data: {data}") |
|
||||||
return data |
|
||||||
return extract_line_slot |
|
||||||
|
|
||||||
|
|
||||||
def net_invest_trial_balance(report: str, save_name: str): |
|
||||||
lines = report.splitlines() |
|
||||||
extracted_data_dict = { |
|
||||||
'CUSTOMER NAME' : [], |
|
||||||
'CURR INT RCVB' : [], |
|
||||||
'UNEARNED BLENDED' : [], |
|
||||||
'BLEND NET INV' : [], |
|
||||||
'LEASE NUMBER' : [], |
|
||||||
'GROSS CONTRACT' : [], |
|
||||||
'CURR RENT RCVB' : [], |
|
||||||
'UNEARN FIN' : [], |
|
||||||
'END DEPOSIT' : [], |
|
||||||
'SEC DEPOSIT' : [], |
|
||||||
'LEASE PYMTS' : [], |
|
||||||
'TOTAL' : [], |
|
||||||
'CONTRACT STAT' : [], |
|
||||||
'PAYMENTS RCVD' : [], |
|
||||||
'REM RENT RCVB' : [], |
|
||||||
'UNEARN RESID' : [], |
|
||||||
'PROV LOSS' : [], |
|
||||||
'NET RESERVE' : [], |
|
||||||
'UNEARN INC' : [], |
|
||||||
'BAL REMAINING' : [], |
|
||||||
'RESIDUAL' : [], |
|
||||||
'UNPAID INT' : [], |
|
||||||
'NET INV' : [], |
|
||||||
'UNEARNED IDC' : [], |
|
||||||
"LESSOR": [] |
|
||||||
} |
|
||||||
lessors = [] |
|
||||||
columns = list(extracted_data_dict.keys()) |
|
||||||
line0 = list(zip(columns[0:4], [0,3,4,5])) |
|
||||||
line1 = list(zip(columns[4:12], [i for i in range(0,8)])) |
|
||||||
line2 = list(zip(columns[12:19], [i for i in range(0,7)])) |
|
||||||
line3 = list(zip(columns[19:-1], [i for i in range(1,6)])) |
|
||||||
|
|
||||||
for l in [line0,line1,line2,line3]: |
|
||||||
print(f"\n{l}") |
|
||||||
|
|
||||||
data_extractor = create_line_divider([18,32,50,66,84,100,117]) |
|
||||||
for line in enumerate(lines): |
|
||||||
slot1 = data_extractor(0,line[1],False) |
|
||||||
if type(slot1) != str : continue |
|
||||||
if re.search(contract_number_regex, slot1) != None: |
|
||||||
data_section = lines[line[0]-1:line[0]+3] |
|
||||||
|
|
||||||
if data_section[0].find(".") == -1: |
|
||||||
data_section[0] = lines[line[0]-2] |
|
||||||
for ds in enumerate(data_section): |
|
||||||
if ds[1].find(".") == -1: |
|
||||||
if ds[0] < len(data_section) -1: |
|
||||||
for i in range(ds[0], len(data_section)-1): |
|
||||||
#print(f"{i}: { data_section[i]}") |
|
||||||
data_section[i] = data_section[i+1] |
|
||||||
#print(f"DELTA| {i}: { data_section[i]}") |
|
||||||
data_section[3] = lines[line[0]+3] |
|
||||||
else: |
|
||||||
data_section[3] = lines[line[0]+3] |
|
||||||
|
|
||||||
|
|
||||||
# [print(f"\n{d[0]}: {d[1]}") for d in enumerate(data_section)] |
|
||||||
# print('\n') |
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[0], False)) for c in line0] |
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[1], False)) for c in line1] |
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[2], False)) for c in line2] |
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[3], False)) for c in line3] |
|
||||||
extracted_data_dict["LESSOR"].append(extracted_data_dict["LEASE NUMBER"][-1][0:3]) |
|
||||||
if extracted_data_dict["LESSOR"][-1] not in lessors: |
|
||||||
print(extracted_data_dict["LESSOR"][-1]) |
|
||||||
lessors.append(extracted_data_dict["LESSOR"][-1]) |
|
||||||
print(lessors) |
|
||||||
for c in columns: |
|
||||||
print(f"C: {c} | {len(extracted_data_dict[c])}") |
|
||||||
print(lessors) |
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
|
|
||||||
summary_series = [] |
|
||||||
for lessor in lessors: |
|
||||||
reduced_df = dataframe.loc[dataframe["LESSOR"] == lessor] |
|
||||||
del reduced_df["CUSTOMER NAME"] |
|
||||||
del reduced_df["LEASE NUMBER"] |
|
||||||
del reduced_df["CONTRACT STAT"] |
|
||||||
reduced_df = reduced_df.replace("", np.NaN) |
|
||||||
reduced_df = reduced_df.replace("REVOLV", np.NaN) |
|
||||||
reduced_df = reduced_df.replace("ING ACCOUNT", np.NaN) |
|
||||||
summation = reduced_df.sum(skipna=True, axis=0) |
|
||||||
summation["LESSOR"] = lessor |
|
||||||
summation["CONTRACT COUNT"] = len(reduced_df.index) |
|
||||||
summary_series.append(summation) |
|
||||||
summary_df = pd.concat(summary_series, axis=1).transpose().set_index("LESSOR") |
|
||||||
prt(summary_df) |
|
||||||
with pd.ExcelWriter(save_name) as writer: |
|
||||||
dataframe.to_excel(writer, index=False, sheet_name="data") |
|
||||||
pd.DataFrame(summary_df).to_excel(writer, index=True, sheet_name="Summary") |
|
||||||
return dataframe |
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
with open("/config/workspace/LEAF/IL Extract SRC/2022.05.20 Net Investment", errors="replace") as rep_file: |
|
||||||
report = rep_file.read() |
|
||||||
|
|
||||||
prt(net_invest_trial_balance(report, "520_NI_TEST.xlsx")) |
|
||||||
@ -1,97 +0,0 @@ |
|||||||
import os |
|
||||||
import pandas as pd |
|
||||||
from datetime import datetime as dt, timedelta |
|
||||||
import sys, getopt |
|
||||||
import re |
|
||||||
from pathlib import Path |
|
||||||
import time |
|
||||||
from pprint import pprint as prt |
|
||||||
import numpy as np |
|
||||||
|
|
||||||
contract_number_regex = "\d{3}-\d{7}-\d{3}" |
|
||||||
|
|
||||||
def create_line_divider(breakage_list: list): |
|
||||||
""" |
|
||||||
This allows for the creation of a custom data extractor |
|
||||||
Breakage list defines the split points that will be used for the line |
|
||||||
Example |
|
||||||
Given breakage_list [10, 20, 30] |
|
||||||
using slot_num 0 in the resulting extract_line_slot will yield |
|
||||||
characters 0 - 10 from the string. |
|
||||||
Slot 1 would give characters 10 - 20 |
|
||||||
""" |
|
||||||
def extract_line_slot(slot_num : int, line_string: str, debug : bool = False): |
|
||||||
""" |
|
||||||
Pulls data from a line/string using break points defined by the |
|
||||||
parent function. |
|
||||||
ONLY USE THIS FUNCTION THROUGH CREATION USING 'create_line_extractor' |
|
||||||
Will automatically convert numbers to floats |
|
||||||
""" |
|
||||||
assert(slot_num < len(breakage_list)+1) |
|
||||||
low_range = 0 if slot_num == 0 else breakage_list[slot_num-1] |
|
||||||
high_range = len(line_string) if slot_num == len(breakage_list) else breakage_list[slot_num] |
|
||||||
data = line_string[low_range:high_range].strip().replace(",", "") |
|
||||||
try: data = float(data) |
|
||||||
except: pass |
|
||||||
if debug: |
|
||||||
print(f"Slot num: {slot_num} | Low: {low_range} | High: {high_range} | Data: {data}") |
|
||||||
return data |
|
||||||
return extract_line_slot |
|
||||||
|
|
||||||
def renewal_net_invest_trial_balance(report: str, save_name: str): |
|
||||||
lines = report.splitlines() |
|
||||||
data_extractor = create_line_divider([21,29,43,58,71,88,99,113]) |
|
||||||
extracted_data_dict = { |
|
||||||
'CUSTOMER NAME' : [], |
|
||||||
'TYPE' : [], |
|
||||||
'GROSS RENEWAL' : [], |
|
||||||
'REMAINING BAL' : [], |
|
||||||
'FINANCED RES' : [], |
|
||||||
'REMAINING RES' : [], |
|
||||||
'LEASE PYMTS' : [], |
|
||||||
'CONTRACT NUMBER' : [], |
|
||||||
'RENEWAL' : [], |
|
||||||
'PAYMENTS RCVD' : [], |
|
||||||
'CUR RENT RCVB' : [], |
|
||||||
'UNEARNED RIN' : [], |
|
||||||
'SECURITY DEP' : [], |
|
||||||
'NET INVEST' : [], |
|
||||||
'UNEARN INCOME' : [], |
|
||||||
'TOTAL' : [], |
|
||||||
'REM RENT RCVB' : [], |
|
||||||
'UNPAID RES' : [], |
|
||||||
} |
|
||||||
columns = list(extracted_data_dict.keys()) |
|
||||||
line0 = list(zip(columns[0:7], [0,1,2,3,4,5,7])) |
|
||||||
line1 = list(zip(columns[7:16], [i for i in range(0,9)])) |
|
||||||
line2 = list(zip(columns[16:], [3,4])) |
|
||||||
|
|
||||||
for line in enumerate(lines): |
|
||||||
slot1 = data_extractor(0,line[1],False) |
|
||||||
if type(slot1) != str : continue |
|
||||||
if re.search(contract_number_regex, slot1) != None: |
|
||||||
data_section = lines[line[0]-1:line[0]+2] |
|
||||||
|
|
||||||
if data_section[0].find(".") == -1: |
|
||||||
data_section[0] = lines[line[0]-2] |
|
||||||
for ds in enumerate(data_section): |
|
||||||
if ds[1].find(".") == -1: |
|
||||||
if ds[0] < len(data_section) -1: |
|
||||||
for i in range(ds[0], len(data_section)-1): |
|
||||||
data_section[i] = data_section[i+1] |
|
||||||
data_section[2] = lines[line[0]+2] |
|
||||||
else: |
|
||||||
data_section[2] = lines[line[0]+2] |
|
||||||
|
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[0])) for c in line0] |
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[1])) for c in line1] |
|
||||||
[extracted_data_dict[c[0]].append(data_extractor(c[1], data_section[2])) for c in line2] |
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
dataframe.to_excel(save_name, index=False) |
|
||||||
return dataframe |
|
||||||
|
|
||||||
|
|
||||||
with open("/config/workspace/LEAF/IL Extract SRC/2022.05.20 Renewal Net Investment", errors="replace") as rep_file: |
|
||||||
report = rep_file.read() |
|
||||||
|
|
||||||
prt(renewal_net_invest_trial_balance(report, "RN_TEST_0606.xlsx")) |
|
||||||
@ -1,110 +0,0 @@ |
|||||||
import os |
|
||||||
import pandas as pd |
|
||||||
from datetime import datetime as dt, timedelta |
|
||||||
import sys, getopt |
|
||||||
import re |
|
||||||
from pathlib import Path |
|
||||||
import time |
|
||||||
import numpy as np |
|
||||||
|
|
||||||
|
|
||||||
contract_number_regex = "\d{3}-\d{7}-\d{3}" |
|
||||||
|
|
||||||
|
|
||||||
def create_line_divider(breakage_list: list): |
|
||||||
""" |
|
||||||
This allows for the creation of a custom data extractor |
|
||||||
Breakage list defines the split points that will be used for the line |
|
||||||
Example |
|
||||||
Given breakage_list [10, 20, 30] |
|
||||||
using slot_num 0 in the resulting extract_line_slot will yield |
|
||||||
characters 0 - 10 from the string. |
|
||||||
Slot 1 would give characters 10 - 20 |
|
||||||
""" |
|
||||||
def extract_line_slot(slot_num : int, line_string: str, debug : bool = False): |
|
||||||
""" |
|
||||||
Pulls data from a line/string using break points defined by the |
|
||||||
parent function. |
|
||||||
ONLY USE THIS FUNCTION THROUGH CREATION USING 'create_line_extractor' |
|
||||||
Will automatically convert numbers to floats |
|
||||||
""" |
|
||||||
# We can't have a slot number higher than the number of slots |
|
||||||
assert(slot_num < len(breakage_list)+1) |
|
||||||
low_range = 0 if slot_num == 0 else breakage_list[slot_num-1] |
|
||||||
high_range = len(line_string) if slot_num == len(breakage_list) else breakage_list[slot_num] |
|
||||||
# In order to create a float we need to remove the , from the string |
|
||||||
data = line_string[low_range:high_range].strip().replace(",", "") |
|
||||||
try: data = float(data) |
|
||||||
except: pass |
|
||||||
if debug: |
|
||||||
print(f"Slot num: {slot_num} | Low: {low_range} | High: {high_range} | Data: {data}") |
|
||||||
return data |
|
||||||
return extract_line_slot |
|
||||||
|
|
||||||
def ach(report: str, save_name: str): |
|
||||||
lines = report.splitlines() |
|
||||||
extracted_data_dict = { |
|
||||||
"ContractNumber" : [], |
|
||||||
"CustomerName" : [], |
|
||||||
"BankCode" : [], |
|
||||||
"BankNumber": [], |
|
||||||
"AccountNumber" : [], |
|
||||||
"Payment" : [], |
|
||||||
"Batch": [], |
|
||||||
"Lessor": [], |
|
||||||
"PaymentDate": [], |
|
||||||
} |
|
||||||
columns = list(extracted_data_dict.keys()) |
|
||||||
batches = { |
|
||||||
"batch_num": [], |
|
||||||
"payment_date": [], |
|
||||||
"lessor": [], |
|
||||||
#"count": [], |
|
||||||
"total": [] |
|
||||||
} |
|
||||||
|
|
||||||
data_extractor = create_line_divider([19,57,67,82,104]) |
|
||||||
bank_number_regex = "\d{9}" |
|
||||||
batch_num_regex = "BATCH \d{4} TOTAL" |
|
||||||
for line in enumerate(lines): |
|
||||||
# Check for a contract number and a bank number in the line |
|
||||||
if (re.search(contract_number_regex, line[1]) != None) & (re.search(bank_number_regex, line[1]) != None): |
|
||||||
# Iterates through the columns list and adds the corresponding slot number to the dictonary for the column |
|
||||||
# Here the order of the columns (keys in dictonary) matter since they need to be in the same order as |
|
||||||
# the slot numbers |
|
||||||
[extracted_data_dict[columns[c]].append(data_extractor(c, line[1])) for c in range(0, len(columns)-3)] |
|
||||||
# This searches for a statement that looks like a batch number |
|
||||||
# This sums the contracts by thier lessor code. A feature requested by cash apps |
|
||||||
if re.search(batch_num_regex, line[1]) != None: |
|
||||||
# Batch number is always in characters 96 to 101 |
|
||||||
batches["batch_num"].append(line[1][96:101]) |
|
||||||
# Payment date will be 2 lines below that between charactes 114 and 125 |
|
||||||
batches["payment_date"].append(lines[line[0]+2][114:125]) |
|
||||||
# Lessor is just the first three number sof the contract number |
|
||||||
batches["lessor"].append(extracted_data_dict["ContractNumber"][-1][0:3]) |
|
||||||
# Total is a number given by the report for that batch. ',' is removed so that it can be transformed into a float |
|
||||||
batches["total"].append(float(line[1][107:125].strip().replace(",", ""))) |
|
||||||
#print(f"{line[0]+6} | {lines[line[0]+6][107:125]}\n{lines[line[0]+6]}") |
|
||||||
#batches["count"].append(float(lines[line[0]+6][107:125].strip().replace(",", ""))) |
|
||||||
# Any time there's a new batch we need to add this data to the dictionary up up to the currrent place |
|
||||||
# So we iterate over the number of contracts and add in the newest value for each that don't have one of these values already |
|
||||||
[extracted_data_dict["Batch"].append(batches["batch_num"][-1]) for _ in range(0, (len(extracted_data_dict["BankCode"]) - len(extracted_data_dict["Batch"])))] |
|
||||||
[extracted_data_dict["Lessor"].append(batches["lessor"][-1]) for _ in range(0, (len(extracted_data_dict["BankCode"]) - len(extracted_data_dict["Lessor"])))] |
|
||||||
[extracted_data_dict["PaymentDate"].append(batches["payment_date"][-1]) for _ in range(0, (len(extracted_data_dict["BankCode"]) - len(extracted_data_dict["PaymentDate"])))] |
|
||||||
# Now the dictioanry lists should all be equal lengths and we can create a dataframe |
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
# We're creating two sheets: data & summary so we need to open and excel writer |
|
||||||
# This also helps with a bug caused by larger dataframes |
|
||||||
with pd.ExcelWriter(save_name) as writer: |
|
||||||
dataframe.to_excel(writer, index=False, sheet_name="data") |
|
||||||
# The batches dictioanry is converted to a dataframe and added as it's own sheet |
|
||||||
pd.DataFrame(batches).to_excel(writer, index=False, sheet_name="Summary") |
|
||||||
return dataframe |
|
||||||
|
|
||||||
r1 = "/config/workspace/LEAF/IL Extract SRC/ach_errors/2022.05.27_ACH_C" |
|
||||||
r2 = "/config/workspace/LEAF/IL Extract SRC/ach_errors/2022.06.03_ACH_C" |
|
||||||
|
|
||||||
with open(r2, errors="replace") as ifile: |
|
||||||
report = ifile.read() |
|
||||||
|
|
||||||
ach(report, "test_ach_0613.xlsx") |
|
||||||
@ -1,87 +0,0 @@ |
|||||||
import os |
|
||||||
import pandas as pd |
|
||||||
from datetime import datetime as dt, timedelta |
|
||||||
import sys, getopt |
|
||||||
import re |
|
||||||
from pathlib import Path |
|
||||||
import time |
|
||||||
from pprint import pprint as prt |
|
||||||
|
|
||||||
contract_number_regex = "\d{3}-\d{7}-\d{3}" |
|
||||||
|
|
||||||
def create_line_divider(breakage_list: list): |
|
||||||
""" |
|
||||||
This allows for the creation of a custom data extractor |
|
||||||
Breakage list defines the split points that will be used for the line |
|
||||||
Example |
|
||||||
Given breakage_list [10, 20, 30] |
|
||||||
using slot_num 0 in the resulting extract_line_slot will yield |
|
||||||
characters 0 - 10 from the string. |
|
||||||
Slot 1 would give characters 10 - 20 |
|
||||||
""" |
|
||||||
def extract_line_slot(slot_num : int, line_string: str, debug : bool = False): |
|
||||||
""" |
|
||||||
Pulls data from a line/string using break points defined by the |
|
||||||
parent function. |
|
||||||
ONLY USE THIS FUNCTION THROUGH CREATION USING 'create_line_extractor' |
|
||||||
Will automatically convert numbers to floats |
|
||||||
""" |
|
||||||
assert(slot_num < len(breakage_list)+1) |
|
||||||
low_range = 0 if slot_num == 0 else breakage_list[slot_num-1] |
|
||||||
high_range = len(line_string) if slot_num == len(breakage_list) else breakage_list[slot_num] |
|
||||||
data = line_string[low_range:high_range].strip().replace(",", "") |
|
||||||
try: data = float(data) |
|
||||||
except: pass |
|
||||||
if debug: |
|
||||||
print(f"Slot num: {slot_num} | Low: {low_range} | High: {high_range} | Data: {data}") |
|
||||||
return data |
|
||||||
return extract_line_slot |
|
||||||
|
|
||||||
|
|
||||||
def ach(report: str, save_name: str): |
|
||||||
|
|
||||||
lines = report.splitlines() |
|
||||||
extracted_data_dict = { |
|
||||||
"ContractNumber" : [], |
|
||||||
"CustomerName" : [], |
|
||||||
"BankCode" : [], |
|
||||||
"BankNumber": [], |
|
||||||
"AccountNumber" : [], |
|
||||||
"Payment" : [], |
|
||||||
"Batch": [], |
|
||||||
"Lessor": [], |
|
||||||
"PaymentDate": [], |
|
||||||
} |
|
||||||
columns = list(extracted_data_dict.keys()) |
|
||||||
batches = { |
|
||||||
"batch_num": [], |
|
||||||
"payment_date": [], |
|
||||||
"lessor": [], |
|
||||||
"count": [], |
|
||||||
"total": [] |
|
||||||
} |
|
||||||
|
|
||||||
data_extractor = create_line_divider([19,57,67,82,104]) |
|
||||||
bank_number_regex = "\d{9}" |
|
||||||
batch_num_regex = "BATCH \d{4} TOTAL" |
|
||||||
for line in enumerate(lines): |
|
||||||
if (re.search(contract_number_regex, line[1]) != None) & (re.search(bank_number_regex, line[1]) != None): |
|
||||||
[extracted_data_dict[columns[c]].append(data_extractor(c, line[1])) for c in range(0, len(columns)-3)] |
|
||||||
if re.search(batch_num_regex, line[1]) != None: |
|
||||||
batches["batch_num"].append(line[1][96:101]) |
|
||||||
batches["payment_date"].append(lines[line[0]+2][114:125]) |
|
||||||
batches["lessor"].append(extracted_data_dict["ContractNumber"][-1][0:3]) |
|
||||||
batches["total"].append(float(line[1][107:125].strip().replace(",", ""))) |
|
||||||
batches["count"].append(float(lines[line[0]+6][107:125].strip().replace(",", ""))) |
|
||||||
[extracted_data_dict["Batch"].append(batches["batch_num"][-1]) for _ in range(0, (len(extracted_data_dict["BankCode"]) - len(extracted_data_dict["Batch"])))] |
|
||||||
[extracted_data_dict["Lessor"].append(batches["lessor"][-1]) for _ in range(0, (len(extracted_data_dict["BankCode"]) - len(extracted_data_dict["Lessor"])))] |
|
||||||
[extracted_data_dict["PaymentDate"].append(batches["payment_date"][-1]) for _ in range(0, (len(extracted_data_dict["BankCode"]) - len(extracted_data_dict["PaymentDate"])))] |
|
||||||
|
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
|
|
||||||
return dataframe |
|
||||||
|
|
||||||
with open("/config/workspace/LEAF/IL Extract SRC/2022.05.04_ACH_C") as rep_file: |
|
||||||
report = rep_file.read() |
|
||||||
|
|
||||||
prt(ach(report, "ACH_TESTING.xlsx")) |
|
||||||
|
Before Width: | Height: | Size: 6.9 KiB |
|
Before Width: | Height: | Size: 18 KiB |
|
Before Width: | Height: | Size: 512 B |
@ -1,168 +0,0 @@ |
|||||||
import os |
|
||||||
import pandas as pd |
|
||||||
from datetime import datetime as dt, timedelta |
|
||||||
import sys, getopt |
|
||||||
import re |
|
||||||
from pathlib import Path |
|
||||||
import time |
|
||||||
import numpy as np |
|
||||||
from pprint import pprint as prt |
|
||||||
|
|
||||||
|
|
||||||
contract_number_regex = "\d{3}-\d{7}-\d{3}" |
|
||||||
|
|
||||||
def dict_lens(dictionary): |
|
||||||
columns = list(dictionary.keys()) |
|
||||||
for c in columns: |
|
||||||
print(f"{c} : {len(dictionary[c])}") |
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def create_line_divider(breakage_list: list): |
|
||||||
""" |
|
||||||
This allows for the creation of a custom data extractor |
|
||||||
Breakage list defines the split points that will be used for the line |
|
||||||
Example |
|
||||||
Given breakage_list [10, 20, 30] |
|
||||||
using slot_num 0 in the resulting extract_line_slot will yield |
|
||||||
characters 0 - 10 from the string. |
|
||||||
Slot 1 would give characters 10 - 20 |
|
||||||
""" |
|
||||||
def extract_line_slot(slot_num : int, line_string: str, debug : bool = False): |
|
||||||
""" |
|
||||||
Pulls data from a line/string using break points defined by the |
|
||||||
parent function. |
|
||||||
ONLY USE THIS FUNCTION THROUGH CREATION USING 'create_line_extractor' |
|
||||||
Will automatically convert numbers to floats |
|
||||||
""" |
|
||||||
# We can't have a slot number higher than the number of slots |
|
||||||
assert(slot_num < len(breakage_list)+1) |
|
||||||
low_range = 0 if slot_num == 0 else breakage_list[slot_num-1] |
|
||||||
high_range = len(line_string) if slot_num == len(breakage_list) else breakage_list[slot_num] |
|
||||||
# In order to create a float we need to remove the , from the string |
|
||||||
data = line_string[low_range:high_range].strip().replace(",", "") |
|
||||||
try: data = float(data) |
|
||||||
except: pass |
|
||||||
if debug: |
|
||||||
print(f"Slot num: {slot_num} | Low: {low_range} | High: {high_range} | Data: {data}") |
|
||||||
return data |
|
||||||
return extract_line_slot |
|
||||||
|
|
||||||
def lockbox(report: str, save_name: str): |
|
||||||
lines = report.splitlines() |
|
||||||
extracted_data_dict = { |
|
||||||
"CustomerName" : [], |
|
||||||
"PaymentDate" : [], |
|
||||||
"InvoiceNumber" : [], |
|
||||||
"CheckNumber" : [], |
|
||||||
"InvoicePayment" : [], |
|
||||||
"ContractNumber" : [], |
|
||||||
"ContractPayment" : [], |
|
||||||
} |
|
||||||
# These are lists of the dictionary columns/keys and the data slots in which |
|
||||||
# that data can be found in the report. this way we can iterate through them |
|
||||||
# While extracting data |
|
||||||
bank_payment_records = [list(extracted_data_dict.keys())[1:5],[1,2,3,4]] |
|
||||||
infolease_payment_records = [list(extracted_data_dict.keys())[5:],[7,8]] |
|
||||||
|
|
||||||
# Below are the Regular Exppressions used to find relvant data lines |
|
||||||
full_line = "\d*\s{5}\d{2}/\d{2}/\d{4}\s{4}1" |
|
||||||
contract_only_line = "\s{90}\d.{7}1\d{2}-" |
|
||||||
cust_name_line = "\s{98}.{28}\D*" |
|
||||||
# The data extractor allows us to extract data from the report using slots |
|
||||||
# Slots are ranges of character denote by the list feed into the creation function |
|
||||||
data_extractor = create_line_divider([9,19,39,56,69,90,98,118]) |
|
||||||
for line in enumerate(lines): |
|
||||||
# We can skip empty lines |
|
||||||
if len(line[1]) == 0: continue |
|
||||||
# First we should check if there is a full line of data (defined by regex) |
|
||||||
if re.search(full_line, line[1]): |
|
||||||
# If this is true then we can iterate through the lists we created earlier and append the data to our dict |
|
||||||
for k in range(0,len(bank_payment_records[0])): |
|
||||||
extracted_data_dict[bank_payment_records[0][k]].append(data_extractor(bank_payment_records[1][k],line[1])) |
|
||||||
for k in range(0,len(infolease_payment_records[0])): |
|
||||||
extracted_data_dict[infolease_payment_records[0][k]].append(data_extractor(infolease_payment_records[1][k],line[1])) |
|
||||||
# Otherwise we should check if this is a line with only contract data |
|
||||||
elif re.search(contract_only_line,line[1]): |
|
||||||
# If that's the case we can use the 'bank payment data' from the previous entry since it should apply to his contract |
|
||||||
for k in range(0,len(bank_payment_records[0])): |
|
||||||
extracted_data_dict[bank_payment_records[0][k]].append(extracted_data_dict[bank_payment_records[0][k]][-1]) |
|
||||||
for k in range(0,len(infolease_payment_records[0])): |
|
||||||
extracted_data_dict[infolease_payment_records[0][k]].append(data_extractor(infolease_payment_records[1][k],line[1])) |
|
||||||
# If it doesn't hit either of these critera then continue since it's irelevant data |
|
||||||
else: continue |
|
||||||
i = 1 |
|
||||||
# used to track how many lines below the current line we're looking for the customer name |
|
||||||
# keep moving down a line and checking for a customer name |
|
||||||
# Customer name typically happens 1 line under data but can be 13 lines if cut off by page end |
|
||||||
while re.search(cust_name_line,lines[line[0]+i]) == None: |
|
||||||
i += 1 |
|
||||||
# Once it hits, add the name to the dict |
|
||||||
extracted_data_dict["CustomerName"].append(data_extractor(7,lines[line[0]+i])) |
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
dataframe.to_excel(save_name, index=False) |
|
||||||
return dataframe |
|
||||||
|
|
||||||
|
|
||||||
def lb2(report:str, save_name:str): |
|
||||||
lines = report.splitlines() |
|
||||||
extracted_data_dict = { |
|
||||||
"SEQ" : [], |
|
||||||
"PYMT DATE" : [], |
|
||||||
"INV NUM" : [], |
|
||||||
"CHECK NUMBER" : [], |
|
||||||
"PAYMENT AMOUNT" : [], |
|
||||||
"NOTE" : [], |
|
||||||
"IL SEQ" : [], |
|
||||||
"CONTRACT NUM" : [], |
|
||||||
"IL PAYMENT AMOUNT" : [], |
|
||||||
"CUST NAME" : [], |
|
||||||
} |
|
||||||
columns = list(extracted_data_dict.keys()) |
|
||||||
data_extractor = create_line_divider([9,19,39,56,69,89,98,118]) |
|
||||||
for line in enumerate(lines): |
|
||||||
match = False |
|
||||||
# Try to find the first SEQ # & a contract payment date e.i. ' 197 05/10/2022' |
|
||||||
if re.match("(\s|\d){3}\d{1}\s{5}\d{2}/\d{2}/\d{4}", line[1]): |
|
||||||
match = True |
|
||||||
# Add all of the data points except customer name |
|
||||||
[extracted_data_dict[columns[c]].append(data_extractor(c,line[1],debug=False)) for c in range(0,len(columns)-1)] |
|
||||||
# Check to see if this line contains only an infolease payment |
|
||||||
# Some times there are multiple infolease payments for a single bank record |
|
||||||
elif re.search(contract_number_regex, line[1]) != None: |
|
||||||
match = True |
|
||||||
# If there is then we can add the same data as the previous complete line |
|
||||||
[extracted_data_dict[columns[c]].append(extracted_data_dict[columns[c]][-1]) for c in range(0,6)] |
|
||||||
# Then add the new data for the infolease contract |
|
||||||
[extracted_data_dict[columns[c]].append(data_extractor(c,line[1],debug=False)) for c in range(6,len(columns)-1)] |
|
||||||
# If we had a match we need a customer name to associate with it |
|
||||||
# Sometimes these can appear on the next page hense the while loop searching for a match |
|
||||||
if match: |
|
||||||
# We can tell the cust name will be on the next page if the word "PAGE" appears three lines under the current line |
|
||||||
# And the next line is blank |
|
||||||
if (lines[line[0]+1].strip() == "") & (lines[line[0]+3].find("PAGE") != -1): |
|
||||||
i = 0 |
|
||||||
# Look for a bunch of whitespace then some writing |
|
||||||
while not re.match("\s{98}.{34}", lines[line[0]+i]): |
|
||||||
i +=1 |
|
||||||
# Once we find it add the cust name to the dict (it's the only thing on the line) |
|
||||||
extracted_data_dict["CUST NAME"].append(lines[line[0]+i].strip()) |
|
||||||
# if the condition above isnt met then the cust name is on the next line (even if that line is blank) |
|
||||||
else: |
|
||||||
extracted_data_dict["CUST NAME"].append(lines[line[0]+1].strip()) |
|
||||||
dataframe = pd.DataFrame(extracted_data_dict) |
|
||||||
dataframe.to_excel(save_name, index=False) |
|
||||||
return dataframe |
|
||||||
|
|
||||||
|
|
||||||
r1 = "/config/workspace/LEAF/IL Extract SRC/lb_errors/2022.05.10_LOCKBOX_094_C" |
|
||||||
r2 = "/config/workspace/LEAF/IL Extract SRC/lb_errors/2022.05.11_LOCKBOX_094_C" |
|
||||||
|
|
||||||
with open(r1, errors="replace") as ifile: |
|
||||||
report = ifile.read() |
|
||||||
|
|
||||||
lb2(report, "test_lb_0510.xlsx") |
|
||||||
|
|
||||||
with open(r2, errors="replace") as ifile: |
|
||||||
report = ifile.read() |
|
||||||
lb2(report, "test_lb_0511.xlsx") |
|
||||||
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|
Before Width: | Height: | Size: 1.2 KiB |
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Loading…
Reference in new issue