Merge pull request #19 from adarshkannada/AUG6

show monthly charts

README.md

@@ -6,18 +6,12 @@ Utility code written for personal use
 - Install dependencies from requirements.txt
 - Install **pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib**
 - Basic setup guide https://developers.google.com/sheets/api/quickstart/python
-- Required Environment details
- - ENV variable SPREADSHEET_ID: provide the google sheet ID
- - ENV variable SHEET_CELLS: cell range in format '!F35:G48'
- - place the credentials.json file having google sheets details in the folder src/main/config
-
-# Analysis - My Datascience application
-- install streamlit, pandas, matplotlib
-- place the data excel file at the location <my-utils\src\main\data>
-- run streamlit main.py as below
-- open the browser with the given local IP address
+  - place the credentials.json file having google sheets details in the folder <src/main/config>
+- set the environment variables appropriately
 
 # Run
-- python -s -m src.main.expense_analysis.expense_analyzer 
-- python -s - m src.main.util.website_sanity_test
-- python -m streamlit run src/main/expense_analysis/main.py
+- Data Load
+  - SET ENV variable LOAD_TYPE to full/incremental
+  - RUN python3 -m src.main.data.data_import
+- RUN Streamlit server
+  - python -m streamlit run src/main/analyse/main.py

src/main/analyse/main.py

@@ -0,0 +1,61 @@
+import streamlit as st
+from dotenv import load_dotenv
+
+from src.main.data.core_data import CoreData
+from src.main.data.data_import import ImportData
+from loguru import logger
+from streamlit_echarts import st_echarts
+
+from src.main.data.sqlite_conn import Sql
+from src.main.utils.utils import Utils
+
+load_dotenv()
+
+
+# streamlit app
+st.title('My Analytics')
+st.markdown("# Home 🎈")
+st.sidebar.markdown("# Home 🎈")
+# Create a text element and let the reader know the data is loading.
+data_load_state = st.text('Loading data...')
+
+
+# @st.cache_data
+def get_data():
+    return Sql().get_data_from_table(query=f"select * from Jul2023 where Date = 'Jul2023'")
+
+
+data = get_data()
+
+# Notify the reader that the data was successfully loaded.
+
+bardf = Sql().get_data_from_table(query=f"select * from {Utils().get_current_month_year()} where Date = "
+                                        f"'{Utils().get_current_month_year()}'")
+home_bar = bardf['Home'].values[0]
+bills_bar = bardf['Bills'].values[0]
+shopping_bar = bardf['Shopping'].values[0]
+food_bar = bardf['Food'].values[0]
+travel_bar = bardf['Travel'].values[0]
+clothes_bar = bardf['Clothes'].values[0]
+fuel_bar = bardf['Fuel'].values[0]
+gifts_bar = bardf['Gifts'].values[0]
+general_bar = bardf['General'].values[0]
+invest_bar = bardf['Investment'].values[0]
+meds_bar = bardf['Medicine'].values[0]
+
+
+options = {
+    "xAxis": {
+        "type": "category",
+        "data": CoreData().columns,
+    },
+    "yAxis": {"type": "value"},
+    "series": [{"data": [home_bar, bills_bar, shopping_bar, food_bar, travel_bar, clothes_bar,
+                         fuel_bar, gifts_bar, general_bar, invest_bar, meds_bar],
+                "type": "bar"}],
+}
+
+st.write("# Current Month")
+st_echarts(options=options, height="500px")
+
+data_load_state.text(f'Data for the current month')

src/main/analyse/pages/monthly.py

@@ -0,0 +1,26 @@
+import streamlit as st
+import pandas as pd
+from src.main.data.core_data import CoreData
+from src.main.data.sqlite_conn import Sql
+
+st.markdown("# Monthly Chart ❄️")
+st.sidebar.markdown("# Monthly Chart ❄️")
+
+data_load_state = st.text('Loading data...')
+data_load_state.text(f'Data for the months of 2023')
+
+df_jul2023 = Sql().get_data_from_table(query=f"select * from Jul2023 where Date = 'Jul2023'")
+df_jun2023 = Sql().get_data_from_table(query=f"select * from Jun2023 where Date = 'Jun2023'")
+df_may2023 = Sql().get_data_from_table(query=f"select * from May2023 where Date = 'May2023'")
+df_apr2023 = Sql().get_data_from_table(query=f"select * from Apr2023 where Date = 'Apr2023'")
+df_mar2023 = Sql().get_data_from_table(query=f"select * from Mar2023 where Date = 'Mar2023'")
+df_feb2023 = Sql().get_data_from_table(query=f"select * from Feb2023 where Date = 'Feb2023'")
+df_jan2023 = Sql().get_data_from_table(query=f"select * from Jan2023 where Date = 'Jan2023'")
+
+frames = [df_jul2023, df_jun2023, df_may2023, df_apr2023, df_mar2023,
+          df_feb2023, df_jan2023]
+df_merged = pd.concat(frames)
+st.write(df_merged)
+for each in CoreData().columns:
+    st.line_chart(data=df_merged, x='Date', y=[each],
+                  width=0, height=0, use_container_width=True)

src/main/data/core_data.py

@@ -0,0 +1,3 @@
+class CoreData:
+    columns = ["Home", "Bills", "Shopping", "Food", "Travel", "Clothes",
+               "Fuel", "Gifts", "General", "Investment", "Medicine"]