Maintenance 01

README.md

@@ -1,10 +1,22 @@
-# Hi...
+# Monthly Report Maker
 
 ## My Motive
 I saw staff at my office doing a monthly recapitulation using Microsoft Excel manually. It takes around 3 to 5 days for one person to finish. Then I started to write this program, hoping to ease the pressure and speed up the time to complete the work.
 
 ## Disclaimer
-This program's design would process raw data into a hard-coded output. In my case, the data contains every ship's information that docked at the harbor. If anyone would use this code, they will need to re-write the properties of both the input and output.
+This program's design would process raw data into a hard-coded output. If anyone would use this code, they will need to re-write the properties of both the input and output.
 
-## How Does My Code Work?
-(To be documented)
+## Program's workflow
+
+### A. File preparations
+1. Manually input data to an `xlsx` file
+2. Place the `xlsx` file inside `files` folder
+
+### B. Running Sequence in main.py
+1. Reads the `xlsx` file
+2. Sorts data by date and time
+3. Split data into categories
+4. Re-create data into some column formats
+5. Write sheets using newly generated data
+6. Write load's resumes for some sheets
+7. Program stopped

blanks.py

@@ -0,0 +1,30 @@
+import json
+import general
+import operator as op
+
+def clearance(dataframe=None):
+    source_dict = dataframe.to_dict(orient='list')
+    openjson = open('json/clearance.json')
+    destination_dict, len_source = json.load(openjson), 0
+
+    # Collect length of value's list
+    for key in source_dict:
+        if len(source_dict[key]) > 0:
+            len_source = len(source_dict[key])
+            break
+
+    # Add necessary new column(s)
+    for key in destination_dict:
+        if key not in source_dict:
+            destination_dict[key] = ['--' * len_source]
+        if key in source_dict:
+            destination_dict[key] = source_dict[key]
+
+    # Add a new column (Number) to destination
+    destination_dict['NUMBER'] = list(range(1, len_source+1))
+
+    # Mark keys in dictionary that contains ";"
+    marked_keys = general.semicolon_list(destination_dict)
+
+    # Using marked_keys to add new rows for ';' occurrence
+

general.py

@@ -0,0 +1,65 @@
+from typing import List, Any
+
+import pandas as pd
+import operator as op
+from pandas import DataFrame
+
+
+def reader(filename='None'):
+    dataframe_name = pd.read_excel(filename)
+    del dataframe_name['NO']
+    dataframe_name = sorter(dataframe_name, ['TANGGAL BERTOLAK', 'JAM BERTOLAK'])
+
+    return dataframe_name
+
+
+def sorter(dataframe=None, sorted_by=None):
+    dataframe.sort_values(by=sorted_by, inplace=True)
+
+    return dataframe
+
+
+def contain(data=None, type_name=None):
+    return all(isinstance(item, type_name) for item in data) or any(isinstance(item, type_name) for item in data)
+
+
+def classifier(dataframe=None, column_name=None, value=None, model: str = None):
+    model.lower()
+    if model == 'ssib':
+        filtered: DataFrame = dataframe.loc[dataframe[column_name] <= value]
+        filtered.reset_index(drop=True, inplace=True)
+
+        return filtered
+
+    elif model == 'bsib':
+        filtered: DataFrame = dataframe.loc[dataframe[column_name] > value]
+        filtered.reset_index(drop=True, inplace=True)
+
+        return filtered
+
+    elif model == 'other':
+        filtered: DataFrame = dataframe.loc[dataframe[column_name] == value]
+        filtered.reset_index(drop=True, inplace=True)
+
+        return filtered
+
+    else:
+        print('Specify correct data label!')
+
+
+def semicolon_list(dict_data):
+    result: list[Any] = []
+    for key in dict_data:
+        if contain(dict_data[key], str):
+            for item in dict_data[key]:
+                try:
+                    op.contains(item, '; ')
+                except TypeError:
+                    item = str(item)
+                finally:
+                    if op.contains(item, '; '):
+                        result.append(key)
+                        break
+                break
+
+    return result

json/clearance.json

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+{
+  "NO SPB TOLAK": [],
+  "PPK 27": [],
+  "PPK 29": [],
+  "NAMA KAPAL": [],
+  "NAHKODA": [],
+  "BENDERA": [],
+  "GT": [],
+  "SIPI": [],
+  "SIKPI": [],
+  "SLO": [],
+  "TIBA DARI": [],
+  "TANGGAL TIBA": [],
+  "CREW": [],
+  "TUJUAN": [],
+  "TANGGAL BERTOLAK": [],
+  "MUATAN MUAT": [],
+  "JUMLAH MUATAN MUAT": [],
+  "SATUAN MUATAN MUAT": [],
+  "PEMILIK / AGEN": []
+}

main.py

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+import general
+import blanks
+from pandas import DataFrame
+
+
+def main():
+    data_from_excel: DataFrame = general.reader(filename='files/File Input.xlsx')
+    # with pd.ExcelWriter('files/File Output.xlsx', engine='xlsxwriter') as writer:
+    blanks.clearance(data_from_excel)
+
+
+if __name__ == '__main__':
+    main()