From 7908a0a77f25fac570ca2cf4993b52d7da4bf020 Mon Sep 17 00:00:00 2001 From: DTMhango <139768204+DTMhango@users.noreply.github.com> Date: Wed, 7 Feb 2024 16:53:48 +0200 Subject: [PATCH] Add files via upload --- Daniel_3.jpg | Bin 0 -> 266296 bytes Gralix Circle.ico | Bin 0 -> 64814 bytes Home.py | 120 +++++++++++++++++++++++++++++++++++ IMG_1.png | Bin 0 -> 150530 bytes IMG_8311.jpg | Bin 0 -> 558220 bytes cl_module.py | 84 ++++++++++++++++++++++++ data_validation.py | 155 +++++++++++++++++++++++++++++++++++++++++++++ requirements.txt | Bin 0 -> 3758 bytes rsz_1gralix3.png | Bin 0 -> 12030 bytes rsz_gralix2.ico | Bin 0 -> 96038 bytes rsz_gralix2.png | Bin 0 -> 12266 bytes run_app.py | 7 ++ 12 files changed, 366 insertions(+) create mode 100644 Daniel_3.jpg create mode 100644 Gralix Circle.ico create mode 100644 Home.py create mode 100644 IMG_1.png create mode 100644 IMG_8311.jpg create mode 100644 cl_module.py create mode 100644 data_validation.py create mode 100644 requirements.txt 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to prevent computation on every rerun + return data_frame.to_csv().encode('utf-8') + + +@st.cache_data +def read_data(data): + return pd.read_csv(data) + + +@st.cache_data +def find_index(file_list, match_string): + for i, file in enumerate(file_list): + file_name = file.name + if match_string in file_name: + return i + + +# Data to be uploaded - Add to session state +files = ['claims', 'case', 'premium'] + +for file in files: + if file not in st.session_state: + st.session_state[file] = None + +min_date = datetime(2000, 1, 1) +max_date = datetime(2030, 12, 31) + + +uploaded_files = st.sidebar.file_uploader(label='Upload Files', + help='Upload the separate files containing Paid/Incurred Claims Data, ' + 'Case Reserves Data and Premium Data. Files MUST be ".csv"', + accept_multiple_files=True, key='upload') + +if len(st.session_state['upload']) > 0: + + try: + claims = st.session_state['upload'][find_index(st.session_state['upload'], 'Claims Data')] + claims_df = read_data(claims) + clean_df, error_df = clean_dataframe(claims_df) + st.session_state['claims'] = clean_df + st.session_state['errors'] = error_df + except (TypeError, ValueError): + pass + + try: + case = st.session_state['upload'][find_index(st.session_state['upload'], 'Outstanding')] + case_res_df = read_data(case) + case_df, case_error = clean_os_dataframe(case_res_df) + for col in case_df.columns: + if 'Unnamed' in col: + case_df = case_df.drop(col, axis=1) + st.session_state['case'] = case_df + + except (TypeError, ValueError): + pass + + try: + premium = st.session_state['upload'][find_index(st.session_state['upload'], 'Premium')] + premium_df = read_data(premium) + premium_df = premium_df.dropna() + premium_df = premium_df.set_index(keys='Year', drop=True) + cols = premium_df.columns + + st.session_state['premium'] = premium_df + + except (TypeError, ValueError): + pass + +claims_data = st.session_state.get('claims') +errors_df = st.session_state.get('errors') +case_data = st.session_state.get('case') +premium_data = st.session_state.get('premium') + +if claims_data is not None: + st.markdown("**USABLE DATA: Clean File**") + st.dataframe(claims_data) + clean_csv = convert_df(claims_data) + download = st.sidebar.download_button(label="Download Clean File", + data=clean_csv, + file_name='Clean.csv', + mime='text/csv') + if errors_df is not None: + st.markdown("**UNUSABLE DATA: Error File**") + st.dataframe(errors_df) + errors_csv = convert_df(errors_df) + download = st.sidebar.download_button(label="Download Errors File", + data=errors_csv, + file_name='Errors.csv', + mime='text/csv') + +if case_data is not None: + st.markdown("**CASE RESERVES DATA**") + st.dataframe(case_data) + +if premium_data is not None: + 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+warnings.filterwarnings(action='ignore', category=UserWarning) + +def create_triangles(data, start_date, end_date): + dff = data[(data['LOSS DATE'] >= start_date) & (data['PAID DATE'] <= end_date)] + triangles = cl.Triangle( + data=dff, + origin='LOSS DATE', + development='PAID DATE', + columns=['GROSS AMOUNT', 'NET AMOUNT'], + index=['MAIN CLASS', 'ADDITIONAL SEGMENTATION'], + cumulative=False + ) + return triangles + + +def create_os_triangles(data, start_date, end_date): + dff = data[(data['LOSS DATE'] >= start_date) & (data['OUTSTANDING DATE'] <= end_date)] + triangles = cl.Triangle( + data=dff, + origin='LOSS DATE', + development='OUTSTANDING DATE', + columns=['GROSS AMOUNT', 'NET AMOUNT'], + index=['MAIN CLASS', 'ADDITIONAL SEGMENTATION'], + cumulative=False + ) + return triangles + + +def tri_size(triangle): + size = triangle.shape[2] + return size + + +def pure_ibnr(ibnr_triangle, os_triangle): + total = [] + dates = [] + zeroized = [] + if tri_size(ibnr_triangle) == tri_size(os_triangle): + for i in range(tri_size(ibnr_triangle)): + if ibnr_triangle.origin_grain == 'Y': + dates.append(ibnr_triangle.origin[i].strftime('%F')) + elif ibnr_triangle.origin_grain == 'Q': + dates.append( + ibnr_triangle.origin[i].strftime('%F-Q%q')) + elif ibnr_triangle.origin_grain == 'M': + dates.append(ibnr_triangle.origin[i].strftime('%F-%m')) + else: + dates.append( + ibnr_triangle.origin[i].strftime('%F-Q%q')) + + if os_triangle.iat[0, 0, i, 0] != os_triangle.iat[0, 0, i, 0]: + emerging = ibnr_triangle.iat[0, 0, + i, 0] + else: + emerging = ibnr_triangle.iat[0, 0, + i, 0] - os_triangle.iat[0, 0, i, 0] + + total.append(emerging) + if emerging >= 0: + zeroized.append(emerging) + elif emerging < 0: + zeroized.append(0) + elif emerging != emerging: + zeroized.append(0) + + pure_ibnr_dict = {'Pure IBNR': total} + pure_ibnr_df = (pd.DataFrame(pure_ibnr_dict, index=dates)) + + total_row_pure = pd.DataFrame( + {'Pure IBNR': [pure_ibnr_df['Pure IBNR'].sum()]}, index=['TOTAL']) + pure_ibnr_df = pd.concat([pure_ibnr_df, total_row_pure]) + + zeroized_dict = {'Zeroized Pure IBNR': zeroized} + zeroized_df = (pd.DataFrame(zeroized_dict, index=dates)) + + total_row_zeroized = pd.DataFrame( + {'Zeroized Pure IBNR': [zeroized_df['Zeroized Pure IBNR'].sum()]}, index=['TOTAL']) + zeroized_df = pd.concat([zeroized_df, total_row_zeroized]) + + return pure_ibnr_df, zeroized_df diff --git a/data_validation.py b/data_validation.py new file mode 100644 index 0000000..7bc08bb --- /dev/null +++ b/data_validation.py @@ -0,0 +1,155 @@ +import re +import io +import pandas as pd +import base64 +from datetime import datetime, timedelta +# import streamlit as st + + +def parse_content(contents, filename): + print("Received contents:", contents) + content_type, content_string = contents.split(',') + decoded = base64.b64decode(content_string) + if '.csv' in filename: + df = pd.read_csv(io.StringIO(decoded.decode('utf-8'))) + return df.to_dict('records') + elif '.xls' in filename: + df = pd.read_excel(io.BytesIO(decoded)) + return df.to_dict('records') + + +def decode_image(image_file): + encoded = base64.b64encode(open(image_file, 'rb').read()) + return f"data:image/png;base64,{encoded.decode()}" + + + +# def clean_dates(df: pd.DataFrame, old_column: str, new_column: str) -> pd.DataFrame: +# """Cleans a date column with mixed types and unifies format. Creates a new column for the dates""" +# df[new_column] = pd.to_datetime(df[old_column], errors="coerce", dayfirst=True, format="%d/%m/%Y") # try date coercion +# # Coerce date if given in day count format +# mask = pd.to_numeric(df[old_column], errors="coerce").notna() +# df.loc[mask, new_column] = pd.to_datetime(df[old_column][mask].astype(float), errors="coerce", unit="D", origin="1899-12-30") +# return df + +def clean_dates(date_str): + original_value = date_str + + day_first_formats = [ + # Day-First + '%d-%m-%Y', + '%d/%m/%Y', + '%d-%m-%Y %H:%M:%S', + '%d/%m/%Y %H:%M:%S', + '%d-%m-%y', + '%d-%m-%y %H:%M:%S', + '%d/%m/%y', + '%d/%m/%y %H:%M:%S' + ] + + month_first_formats = [ + '%m-%d-%Y', + '%m/%d/%Y', + '%m-%d-%y', + '%m-%d-%Y %H:%M:%S', + '%m/%d/%Y %H:%M:%S', + '%m-%d-%y %H:%M:%S', + ] + + other_formats = [ + '%Y-%m-%d', + '%Y-%m-%d %H:%M:%S', + '%d-%b-%y', + '%d-%b-%Y', + '%m/%d/%y', + '%m/%d/%Y', + '%B %d %Y', + '%B %d %Y %H:%M:%S' + ] + + try: + # try to parse an Excel style date + excel_date = float(date_str) + date = datetime(1899, 12, 30) + timedelta(days=excel_date) + return date.strftime('%Y-%m-%d') + except ValueError: + pass + + for date_format in day_first_formats: + try: + return datetime.strptime(date_str, date_format).strftime('%Y-%m-%d') + except ValueError: + pass + + for date_format in month_first_formats: + try: + return datetime.strptime(date_str, date_format).strftime('%Y-%m-%d') + except ValueError: + pass + + for date_format in other_formats: + try: + return datetime.strptime(date_str, date_format).strftime('%Y-%m-%d') + except ValueError: + return original_value + + +def clean_amounts(amount_str): + if amount_str == 'nan' or amount_str.strip() == '': + return 0.0 + try: + if re.match(r'^\(.+\d\)$', amount_str): # check amount enclosed in parentheses + return float('-' + re.sub(r'[^\d./-]', '', amount_str)) + else: + amount = re.sub(r'[^\d./-]', '', amount_str) # extract digits, periods and hyphens + try: + return float(amount) # convert amount to float + except ValueError: + return amount_str + except ValueError: + return amount_str + + +# @st.cache_data +def clean_dataframe(my_df): + for col in my_df.columns: + if 'Unnamed' in col: + my_df = my_df.drop(col, axis=1) + for col in my_df.columns: + my_df[col] = my_df[col].astype(str) + if 'DATE' in col: + my_df[col] = my_df[col].apply(clean_dates) + if 'AMOUNT' in col: + my_df[col] = my_df[col].apply(clean_amounts) + date_cols = my_df.filter(like='DATE') + amount_cols = my_df.filter(like='AMOUNT') + inconsistent_date = my_df[(my_df['LOSS DATE'] > my_df['PAID DATE']) | (my_df['LOSS DATE'] > my_df['REPORTED DATE'])] + bad_dates = my_df[date_cols.apply(pd.to_datetime, errors='coerce', dayfirst=False).isna().any(axis=1)] + bad_amounts = my_df[amount_cols.apply(pd.to_numeric, errors='coerce').isna().any(axis=1)] + error_df = pd.concat([bad_dates, bad_amounts, inconsistent_date]) + error_df['REFERENCE'] = error_df.index + 2 + error_df = error_df.drop_duplicates().sort_index() + clean_df = (my_df.drop(error_df.index, axis=0)).reset_index(drop=True) + return clean_df, error_df + +# @st.cache_data +def clean_os_dataframe(my_df): + for col in my_df.columns: + if 'Unnamed' in col: + my_df = my_df.drop(col, axis=1) + for col in my_df.columns: + my_df[col] = my_df[col].astype(str) + if 'DATE' in col: + my_df[col] = my_df[col].apply(clean_dates) + if 'AMOUNT' in col: + my_df[col] = my_df[col].apply(clean_amounts) + date_cols = my_df.filter(like='DATE') + amount_cols = my_df.filter(like='AMOUNT') + inconsistent_date = my_df[(my_df['LOSS DATE'] > my_df['OUTSTANDING DATE']) | (my_df['LOSS DATE'] > my_df['REPORTED DATE'])] + bad_dates = my_df[date_cols.apply(pd.to_datetime, errors='coerce', dayfirst=False).isna().any(axis=1)] + bad_amounts = my_df[amount_cols.apply(pd.to_numeric, errors='coerce').isna().any(axis=1)] + error_df = pd.concat([bad_dates, bad_amounts, inconsistent_date]) + error_df['REFERENCE'] = error_df.index + 2 + error_df = error_df.drop_duplicates().sort_index() + clean_df = (my_df.drop(error_df.index, axis=0)).reset_index(drop=True) + return clean_df, error_df diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..237d33512dff6d597a9fc49e7a63efd3a0983c45 GIT binary patch literal 3758 zcmb7{&2J+`5XJk9#Gf)KcDw-&d*HH2h?OF7K%9_2v(DOMGq&^f#{=(IZ^}KJn3a$< z9>rb#>Q&XN>drrZPs^qp%e1UZSKjOMu6$QUddB73@{N9P%Ry`7@<~=(*u2pVBfNdD$9Qw0 z&BbOZ#GO9iC2u?%WgffjbKUd0>bZe6KHGhY*jwRD%R-;@dL=7j9O;`(;By*L;<*vS zo&MVDH~7As_rYPvgVnjBfur$=#)z;7Wg0kG;FXNroGsdj^CYW%c@7j;r{?2X_?xqT z5JrlQZ@esoeh_!phF_iW;aVdbDpajNalUhZQ)ZFxqtK}KRD2qF-iiBO->n$H)zyF_ zN_vffeJ9KR-U;(d-cx`7e<$ijRm;eMSyg6htytA~rE;#0qv@1$qC=4x8+Mb(?FS)O zosN6niM`V5LsXyaWOb{OKgch&u{Q8r<6cEZ>8qi}soW&$=e*tt{iq7h0-aS5Mt$Xf zj@4U5KxLByeyGkwwrGI7&QyK!@TK~1WaVyJYo!%eVqm|cU-y;ogn-Its_Ir*Cf}%r zH#@+o(*nkaSZ~XntUEC~^{TOI)9Ww7Y|Fh+#%IsgKD_U>&$GhOvjR6Wz84}9AEIxU 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APP/Home.py"] + sys.exit(stcli.main())