Quantium job simulation

README.md

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 # Quantium starter repo
-This repo contains everything you need to get started on the program! Good luck!
+
+__Task1:__
+Setting up the local repo.
+
+__Task2:__
+
+Initial datasets daily_sales_data_0.csv, daily_sales_data_1.csv, daily_sales_data_2.csv
+The attributes in the above datasets are  
+1. product - {'gold morsel', 'magenta morsel', 'periwinkle morsel', 'vermilion morsel', 'chartreuse morsel', 'lapis morsel', 'pink morsel'}
+2. price 
+3. quantity
+4. date
+5. region - {'west', 'east', 'north', 'south'}
+
+Combining the three datasets into a single dataset i.e combined_data.csv. The Soul Foods is only interested in Pink Morsels
+
+The output file contains three fields:
+1. sales = price * quantity
+2. date
+3. region
+
+__Task3:__
+
+Outputs:
+![alt text](newplot.png)
+![alt text](newplot_s.png)
+![alt text](newplot_e.png)
+![alt text](newplot_w.png)
+
+(*Threshold : sales on Jan 15 2021*)
+
+__Task4:__
+
+Added some UI to it so it is convenient to use.
+![alt text](newplot_ui.png)
+
+__Task5:__
+
+Add some unit test cases to verify the Dash application.

Task3.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7a281ff7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"650\"\n",
+       "            src=\"http://127.0.0.1:8050/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x19c544cc280>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from dash import Dash, dcc, html\n",
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "app = Dash(__name__)\n",
+    "\n",
+    "#reading csv file\n",
+    "\n",
+    "# Get the absolute path to the data folder\n",
+    "data_folder = os.path.abspath('data')\n",
+    "\n",
+    "# Construct the absolute path to the CSV file\n",
+    "\n",
+    "# Read the CSV file\n",
+    "df_0 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_0.csv'))\n",
+    "\n",
+    "\n",
+    "#filtering only pink morsel\n",
+    "df_0 = df_0[df_0['product'] == 'pink morsel']\n",
+    "\n",
+    "# from price column removing $ and converting to number\n",
+    "df_0['price'] = pd.to_numeric(df_0['price'].str.replace('$', '', regex=False), errors='coerce') \n",
+    "\n",
+    "#adding new column named sales where sales=price*quantity\n",
+    "df_0['sales'] = df_0['price'] * df_0['quantity']\n",
+    "\n",
+    "#dropping the columns\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_0 = df_0.drop(columns=columns_to_drop)\n",
+    "\n",
+    "df_1 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_1.csv'))\n",
+    "\n",
+    "df_1 = df_1[df_1['product'] == 'pink morsel']\n",
+    "\n",
+    "df_1['price'] = pd.to_numeric(df_1['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "df_1['sales'] = df_1['price'] * df_1['quantity']\n",
+    "\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_1 = df_1.drop(columns=columns_to_drop)\n",
+    "\n",
+    "df_2 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_2.csv'))\n",
+    "\n",
+    "df_2 = df_2[df_2['product'] == 'pink morsel']\n",
+    "\n",
+    "df_2['price'] = pd.to_numeric(df_2['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "df_2['sales'] = df_2['price'] * df_2['quantity']\n",
+    "\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_2 = df_2.drop(columns=columns_to_drop)\n",
+    "\n",
+    "# combining all three different datasets into single dataset\n",
+    "combined_df = pd.concat([df_0,df_1,df_2],ignore_index = True)\n",
+    "combined_df = combined_df.drop(columns = 'product')\n",
+    "\n",
+    "# sorting dataset by region \n",
+    "c_north = combined_df[combined_df['region'] == 'north']\n",
+    "c_south = combined_df[combined_df['region'] == 'south']\n",
+    "c_east = combined_df[combined_df['region'] == 'east']\n",
+    "c_west = combined_df[combined_df['region'] == 'west']\n",
+    "\n",
+    "# thresold value for north region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_north = c_north.loc[c_north['date']== '2021-01-15','sales'] \n",
+    "north_range=sales_on_query_data_north.values[0]  if not sales_on_query_data_north.empty else 0\n",
+    "\n",
+    "line_color = ['sales greater than thresold' if y > north_range else 'sales lesser than thresold' for y in c_north['sales']]\n",
+    "c_north.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "# thresold value for south region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_south = c_south.loc[c_south['date']== '2021-01-15','sales'] \n",
+    "south_range=sales_on_query_data_south.values[0]  if not sales_on_query_data_south.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > south_range else 'sales lesser than thresold' for y in c_south['sales']]\n",
+    "c_south.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "# thresold value for east region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_east = c_east.loc[c_east['date']== '2021-01-15','sales'] \n",
+    "east_range=sales_on_query_data_east.values[0]  if not sales_on_query_data_east.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > east_range else 'sales lesser than thresold' for y in c_east['sales']]\n",
+    "c_east.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "# thresold value for west region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_west = c_west.loc[c_west['date']== '2021-01-15','sales'] \n",
+    "west_range=sales_on_query_data_west.values[0]  if not sales_on_query_data_west.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > west_range else 'sales lesser than thresold' for y in c_west['sales']]\n",
+    "c_west.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "app.layout = html.Div(children=[\n",
+    "    html.H1(\n",
+    "        children='Soul Foods',\n",
+    "        style={\n",
+    "            'textAlign': 'center'\n",
+    "        }\n",
+    "    ),\n",
+    "\n",
+    "    html.Div(children='Sales of Pink Morsel', style={\n",
+    "        'textAlign': 'center',\n",
+    "        'fontSize': '30px'\n",
+    "    }),\n",
+    "    \n",
+    "    html.Div(children='Sales = Price*Quantity',style={\n",
+    "        'border':'2px solid black',\n",
+    "        'margin-right':'800px'\n",
+    "    }),\n",
+    "\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 1',\n",
+    "        figure=px.line(c_north, x='date', y='sales', color='Color',title='North region: Sales on each day')\n",
+    "    ),\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 2',\n",
+    "        figure=px.line(c_south, x='date', y='sales', color='Color',title='South region: Sales on each day')\n",
+    "    ),\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 3',\n",
+    "        figure=px.line(c_east, x='date', y='sales', color='Color',title='East region: Sales on each day')\n",
+    "    ),\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 4',\n",
+    "        figure=px.line (c_west, x='date', y='sales', color='Color',title='West region: Sales on each day')\n",
+    "    )\n",
+    "])\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    app.run(debug=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56f4e847",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}