{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inventory Management Dashboard and Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 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DateTimeItem IDCategoryResponsible StaffExpected QuantityActual QuantityExpired ItemsReturned ItemsItems Out for SalesItems Out for Quality ControlItems Out for Events
802024-01-01EveningITM003CTG003Andrew Cauchi34314371012
1712024-01-01EveningITM005CTG003Jean-Pierre Ellul4744427912
5562024-01-01EveningITM005CTG003Andrew Cauchi5253526912
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" ], "text/plain": [ " Date Time Item ID Category Responsible Staff \\\n", "80 2024-01-01 Evening ITM003 CTG003 Andrew Cauchi \n", "171 2024-01-01 Evening ITM005 CTG003 Jean-Pierre Ellul \n", "556 2024-01-01 Evening ITM005 CTG003 Andrew Cauchi \n", "932 2024-01-01 Afternoon ITM003 CTG003 Aaron Vella \n", "1871 2024-01-01 Morning ITM006 CTG001 Aaron Vella \n", "\n", " Expected Quantity Actual Quantity Expired Items Returned Items \\\n", "80 34 31 4 3 \n", "171 47 44 4 2 \n", "556 52 53 5 2 \n", "932 41 37 5 3 \n", "1871 44 39 4 3 \n", "\n", " Items Out for Sales Items Out for Quality Control Items Out for Events \n", "80 7 10 12 \n", "171 7 9 12 \n", "556 6 9 12 \n", "932 7 9 12 \n", "1871 6 10 12 " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel(r'D:\\K_REPO\\Depi_freelanceYard\\PeakFit Essentials.xlsx',sheet_name='Sheet1')\n", "df.sort_values(by=\"Date\").head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Clean the data " ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "#drop unusfull columns\n", "df = df.drop(columns=['Expired Items','Returned Items','Items Out for Sales','Items Out for Quality Control','Items Out for Events'])" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "df[\"Date\"] = pd.to_datetime(df[\"Date\"], format=\"%Y-%m-%d\")" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42\n" ] }, { "data": { "text/html": [ "
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DateTimeItem IDCategoryResponsible StaffInventory Discrepancy
02024-05-29EveningITM006CTG001Robert Tabone-4
12024-08-23AfternoonITM004CTG001Simon Fenech-5
22024-05-12EveningITM001CTG001Jean-Pierre Ellul0
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42024-09-24EveningITM002CTG002Robert Tabone2
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" ], "text/plain": [ " Date Time Item ID Category Responsible Staff \\\n", "0 2024-05-29 Evening ITM006 CTG001 Robert Tabone \n", "1 2024-08-23 Afternoon ITM004 CTG001 Simon Fenech \n", "2 2024-05-12 Evening ITM001 CTG001 Jean-Pierre Ellul \n", "3 2024-03-27 Evening ITM003 CTG003 Andrew Cauchi \n", "4 2024-09-24 Evening ITM002 CTG002 Robert Tabone \n", "\n", " Inventory Discrepancy \n", "0 -4 \n", "1 -5 \n", "2 0 \n", "3 -4 \n", "4 2 " ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#replace expected & actual with stocked quantity\n", "#make column for Inventory Discrepancy\n", "df['Inventory Discrepancy'] = df['Actual Quantity'] - df['Expected Quantity']\n", "df = df.drop(columns=['Expected Quantity',\t'Actual Quantity' ])\n", "print(df['Inventory Discrepancy'].sum()) #total Inventory Discrepancy in 10 monthes [ -N : missing , +N more than expeted ] \n", "df.head()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "df['Month'] = df['Date'].dt.month_name()\n" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42\n" ] } ], "source": [ "print(df['Inventory Discrepancy'].sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### the new data discription" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2000 entries, 0 to 1999\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Date 2000 non-null datetime64[ns]\n", " 1 Time 2000 non-null object \n", " 2 Item ID 2000 non-null object \n", " 3 Category 2000 non-null object \n", " 4 Responsible Staff 2000 non-null object \n", " 5 Inventory Discrepancy 2000 non-null int64 \n", " 6 Month 2000 non-null object \n", "dtypes: datetime64[ns](1), int64(1), object(5)\n", "memory usage: 109.5+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateTimeItem IDCategoryResponsible StaffInventory DiscrepancyMonth
count200020002000200020002000.000002000
uniqueNaN3636NaN9
topNaNMorningITM001CTG001Jean-Pierre EllulNaNMarch
freqNaN6793591011368NaN247
mean2024-05-16 21:35:16.800000256NaNNaNNaNNaN0.02100NaN
min2024-01-01 00:00:00NaNNaNNaNNaN-5.00000NaN
25%2024-03-09 00:00:00NaNNaNNaNNaN-3.00000NaN
50%2024-05-17 12:00:00NaNNaNNaNNaN0.00000NaN
75%2024-07-25 00:00:00NaNNaNNaNNaN3.00000NaN
max2024-09-30 00:00:00NaNNaNNaNNaN5.00000NaN
stdNaNNaNNaNNaNNaN3.13472NaN
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" ], "text/plain": [ " Date Time Item ID Category \\\n", "count 2000 2000 2000 2000 \n", "unique NaN 3 6 3 \n", "top NaN Morning ITM001 CTG001 \n", "freq NaN 679 359 1011 \n", "mean 2024-05-16 21:35:16.800000256 NaN NaN NaN \n", "min 2024-01-01 00:00:00 NaN NaN NaN \n", "25% 2024-03-09 00:00:00 NaN NaN NaN \n", "50% 2024-05-17 12:00:00 NaN NaN NaN \n", "75% 2024-07-25 00:00:00 NaN NaN NaN \n", "max 2024-09-30 00:00:00 NaN NaN NaN \n", "std NaN NaN NaN NaN \n", "\n", " Responsible Staff Inventory Discrepancy Month \n", "count 2000 2000.00000 2000 \n", "unique 6 NaN 9 \n", "top Jean-Pierre Ellul NaN March \n", "freq 368 NaN 247 \n", "mean NaN 0.02100 NaN \n", "min NaN -5.00000 NaN \n", "25% NaN -3.00000 NaN \n", "50% NaN 0.00000 NaN \n", "75% NaN 3.00000 NaN \n", "max NaN 5.00000 NaN \n", "std NaN 3.13472 NaN " ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe(include='all')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Date started 2024-01-01 ended 2024-09-30 with 2000 record .\n", "\n", "time 3 shifts per day (morning, afternoon, evening).\n", "\n", "item ID are only 6 items (Dumbbells, Yoga Mat, Resistance Bands, Protein Powder, Foam Roller, Kettlebells).\n", "\n", "category are only 3 items , but 4 in category sheet (Strength Training Equipment, fitness accessories, strength training equipment). there is an issue (miss leading inputs)\n", "\n", "responsible staff are only 6 (['Robert Tabone', 'Simon Fenech', 'Jean-Pierre Ellul','Andrew Cauchi', 'Aaron Vella', 'Franklin Attard']).\n", "\n", "and no nulls in the data\n", "\n" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['CTG001', 'CTG003', 'CTG002', 'CTG004'], dtype=object)" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fixing category\n", "# assinge ITM001 , ITM003 , ITM006 to ctg001 as they all strength training Items\n", "df.loc[df['Item ID'].isin(['ITM001', 'ITM003', 'ITM006']), 'Category'] = 'CTG001'\n", "\n", "# assinge ITM004 as CTG004 ( protein poweder is supplements )\n", "df.loc[df[\"Item ID\"].isin([\"ITM004\"]), \"Category\"]= \"CTG003\"\n", "\n", "#assinge ITM005 as CTG005 (foam rollers are recovery tools)\n", "df.loc[df[\"Item ID\"].isin([\"ITM005\"]), \"Category\"]=\"CTG004\"\n", "\n", "df['Category'].unique()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Data proccessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### calender heatmap (Heat_fig) ####" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DayMonthInventory Discrepancy
01April5
11August0
21February9
31January-13
41July-1
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" ], "text/plain": [ " Day Month Inventory Discrepancy\n", "0 1 April 5\n", "1 1 August 0\n", "2 1 February 9\n", "3 1 January -13\n", "4 1 July -1" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert DateTime to datetime format\n", "df['DateTime'] = pd.to_datetime(df['Date'])\n", "\n", "# Extract Month and Day\n", "df['Month'] = df['DateTime'].dt.strftime('%B') # Month name\n", "df['Day'] = df['DateTime'].dt.day # Day of month\n", "\n", "# Aggregate data by Day and Month (sum of Inventory Discrepancy)\n", "df_agg = df.groupby(['Day', 'Month'])['Inventory Discrepancy'].sum().reset_index()\n", "\n", "# Create pivot table for heatmap\n", "heatmap_data = df_agg.pivot(index='Day', columns='Month', values='Inventory Discrepancy')\n", "\n", "# Fill NaN values with 0 (days with no Inventory Discrepancy)\n", "heatmap_data = heatmap_data.fillna(0)\n", "\n", "# Define month order for proper sorting\n", "month_order = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \n", " \"July\", \"August\", \"September\", \"October\"]\n", "\n", "# Filter to only include months that are in our data\n", "available_months = [month for month in month_order if month in heatmap_data.columns]\n", "heatmap_data = heatmap_data.reindex(columns=available_months)\n", "df_agg.head()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "Month: %{x}
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"white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Inventory Discrepancy/day Heatmap" }, "width": 700, "xaxis": { "anchor": "y", "constrain": "domain", "domain": [ 0, 1 ], "scaleanchor": "y", "title": { "text": "Month" } }, "yaxis": { "anchor": "x", "autorange": false, "constrain": "domain", "domain": [ 0, 1 ], "range": [ 1, 31 ], "title": { "text": "Day of Month" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Create the heatmap\n", "Heat_fig = px.imshow(\n", " heatmap_data.values,\n", " labels=dict(x=\"Month\", y=\"Day\", color=\"Inventory Discrepancy\"),\n", " x=heatmap_data.columns,\n", " y=heatmap_data.index,\n", " color_continuous_scale=\"RdBu\", # Changed to Reds as Inventory Discrepancy are positive now\n", " text_auto=True # Display values in the cells\n", ")\n", "\n", "Heat_fig.update_layout(\n", " title=\"Inventory Discrepancy/day Heatmap\",\n", " xaxis_title=\"Month\",\n", " yaxis_title=\"Day of Month\",\n", "\n", " yaxis=dict(\n", " range=[1, 31], # Explicitly reverse the order (assuming max day is 31)\n", " autorange=False # Disable automatic range adjustment\n", " ), # Keep day labels in correct order\n", " \n", " width=700, # Set width\n", " height=600, # Set height\n", " plot_bgcolor=\"black\", # Set plot background to black\n", " paper_bgcolor=\"white\", # Set entire figure background to black\n", " font=dict(color=\"black\") # Set text color to white for contrast\n", " \n", ")\n", "\n", "# To display the figure\n", "\n", "Heat_fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#made a calnder as heatmap, and found :\n", "\n", "at (25/07) 31 unexpected item found \n", "\n", "at (30/05) & 03/08 highest missing record at -23 item " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### by weekday (Bar_fig)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateTimeItem IDCategoryResponsible StaffInventory DiscrepancyMonthDateTimeDayWeekday
02024-05-29EveningITM006CTG001Robert Tabone-4May2024-05-2929Wednesday
12024-08-23AfternoonITM004CTG003Simon Fenech-5August2024-08-2323Friday
22024-05-12EveningITM001CTG001Jean-Pierre Ellul0May2024-05-1212Sunday
32024-03-27EveningITM003CTG001Andrew Cauchi-4March2024-03-2727Wednesday
42024-09-24EveningITM002CTG002Robert Tabone2September2024-09-2424Tuesday
\n", "
" ], "text/plain": [ " Date Time Item ID Category Responsible Staff \\\n", "0 2024-05-29 Evening ITM006 CTG001 Robert Tabone \n", "1 2024-08-23 Afternoon ITM004 CTG003 Simon Fenech \n", "2 2024-05-12 Evening ITM001 CTG001 Jean-Pierre Ellul \n", "3 2024-03-27 Evening ITM003 CTG001 Andrew Cauchi \n", "4 2024-09-24 Evening ITM002 CTG002 Robert Tabone \n", "\n", " Inventory Discrepancy Month DateTime Day Weekday \n", "0 -4 May 2024-05-29 29 Wednesday \n", "1 -5 August 2024-08-23 23 Friday \n", "2 0 May 2024-05-12 12 Sunday \n", "3 -4 March 2024-03-27 27 Wednesday \n", "4 2 September 2024-09-24 24 Tuesday " ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Extract weekday name\n", "df[\"Weekday\"] = df[\"Date\"].dt.day_name()\n", "# Group by weekday and sum Inventory Discrepancy\n", "df_grouped = df.groupby(\"Weekday\")[\"Inventory Discrepancy\"].sum().reset_index()\n", "# Define weekday order\n", "weekday_order = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n", "# Create a category type with our custom order\n", "df_grouped[\"Weekday\"] = pd.Categorical(df_grouped[\"Weekday\"], categories=weekday_order, ordered=True)\n", "\n", "# Sort by our ordered category\n", "df_grouped = df_grouped.sort_values(\"Weekday\")\n", "\n", "# # Create color sequence to match your original\n", "# colors = [\"skyblue\", \"orange\", \"green\", \"red\", \"purple\", \"pink\", \"brown\"]\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Day of the Week=%{x}
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"paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Inventory Discrepancy by Day of the Week" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Day of the Week" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "gridcolor": "rgba(255,255,255,0.2)", "gridwidth": 1, "showgrid": true, "title": { "text": "Total Inventory Discrepancy" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create the bar chart\n", "Bar_fig = px.bar(\n", " df_grouped,\n", " x=\"Weekday\",\n", " y=\"Inventory Discrepancy\",\n", " title=\"Inventory Discrepancy by Day of the Week\",\n", " color=\"Inventory Discrepancy\", # Color by value\n", " color_continuous_scale=['red', 'blue'], # Red for negative, blue for positive\n", " labels={\"Weekday\": \"Day of the Week\", \"Inventory Discrepancy\": \"Total Inventory Discrepancy\"}\n", ")\n", "\n", "# Update layout for dark background\n", "Bar_fig.update_layout(\n", " plot_bgcolor=\"black\", # Set plot background to black\n", " paper_bgcolor=\"black\", # Set entire figure background to black\n", " font=dict(color=\"white\") # Set text color to white for contrast\n", ")\n", "\n", "# Add grid lines\n", "Bar_fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(255,255,255,0.2)')\n", "\n", "component0 = pn.panel(Bar_fig)\n", "# Display the figure\n", "Bar_fig\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "allover the week tuesdays were for losing items . about 94 item were missed " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### staff sum of recorded stocks allover the year (Bar2_fig)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Inventory Discrepancy
Responsible Staff
Aaron Vella8
Andrew Cauchi28
Franklin Attard-39
Jean-Pierre Ellul-11
Robert Tabone31
Simon Fenech25
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" ], "text/plain": [ " Inventory Discrepancy\n", "Responsible Staff \n", "Aaron Vella 8\n", "Andrew Cauchi 28\n", "Franklin Attard -39\n", "Jean-Pierre Ellul -11\n", "Robert Tabone 31\n", "Simon Fenech 25" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Inventory Discrepancy per staff\n", "df_miss_perstaff=df.groupby(\"Responsible Staff\").agg({\n", " 'Inventory Discrepancy': 'sum'\n", "})\n", "df_miss_perstaff.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "franklin & jean were responsible for most missing recordes ( 39 , 11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### shifts and responsible staff (Line_fig)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TimeAfternoonEveningMorningTotal Shifts
Responsible Staff
Aaron Vella103107104314
Andrew Cauchi11010696312
Franklin Attard11598106319
Jean-Pierre Ellul106140122368
Robert Tabone106124123353
Simon Fenech104102128334
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" ], "text/plain": [ "Time Afternoon Evening Morning Total Shifts\n", "Responsible Staff \n", "Aaron Vella 103 107 104 314\n", "Andrew Cauchi 110 106 96 312\n", "Franklin Attard 115 98 106 319\n", "Jean-Pierre Ellul 106 140 122 368\n", "Robert Tabone 106 124 123 353\n", "Simon Fenech 104 102 128 334" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#staff per shift\n", "df_staff_shifts = df.groupby([\"Responsible Staff\", \"Time\"]).size().unstack(fill_value=0)\n", "df_staff_shifts[\"Total Shifts\"] = df_staff_shifts.sum(axis=1)\n", "df_staff_shifts.head(10)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{x} Shift
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"white", "zerolinewidth": 2 } } }, "title": { "text": "Shift Distribution Across Staff Members" }, "width": 800, "xaxis": { "gridcolor": "rgba(0,0,0,0.1)", "gridwidth": 1, "showgrid": true, "title": { "text": "Shifts" } }, "yaxis": { "gridcolor": "rgba(0,0,0,0.1)", "gridwidth": 1, "showgrid": true, "title": { "text": "Shift Distribution" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Data\n", "staff = [\"Aaron Vella\", \"Andrew Cauchi\", \"Franklin Attard\", \"Jean-Pierre Ellul\", \"Robert Tabone\", \"Simon Fenech\"]\n", "shifts = [\"Morning\", \"Afternoon\", \"Evening\"]\n", "morning = [104, 96, 106, 122, 123, 128]\n", "afternoon = [103, 110, 115, 106, 106, 104]\n", "evening = [107, 106, 98, 140, 124, 102]\n", "\n", "# Define custom colors for each staff member\n", "colors = ['blue', 'red', 'green', 'purple', 'orange', 'brown']\n", "\n", "# Create figure with specified name\n", "Line_fig = go.Figure()\n", "\n", "# Add traces for each staff member with specific colors\n", "for i, staff_name in enumerate(staff):\n", " y_values = [morning[i], afternoon[i], evening[i]]\n", " Line_fig.add_trace(go.Scatter(\n", " x=shifts,\n", " y=y_values,\n", " mode='lines+markers',\n", " name=staff_name,\n", " line=dict(color=colors[i], width=2),\n", " marker=dict(color=colors[i], size=8),\n", " hovertemplate='%{x} Shift
' + \n", " f'{staff_name}: ' + '%{y}'\n", " ))\n", "\n", "# Update layout\n", "Line_fig.update_layout(\n", " title=\"Shift Distribution Across Staff Members\",\n", " xaxis_title=\"Shifts\",\n", " yaxis_title=\"Shift Distribution\",\n", " legend_title=\"Staff Members\",\n", " height=600,\n", " width=800,\n", " hovermode=\"closest\"\n", ")\n", "\n", "# Add grid lines for better readability\n", "Line_fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')\n", "Line_fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')\n", "\n", "# Show the figure\n", "Line_fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### final stocks of Items" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\S\\AppData\\Local\\Temp\\ipykernel_3408\\3132821364.py:1: FutureWarning:\n", "\n", "The provided callable is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Inventory Discrepancy
Item IDCategory
ITM001CTG00116
ITM002CTG002-71
ITM003CTG00135
ITM004CTG00335
ITM005CTG004-42
ITM006CTG00169
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" ], "text/plain": [ " Inventory Discrepancy\n", "Item ID Category \n", "ITM001 CTG001 16\n", "ITM002 CTG002 -71\n", "ITM003 CTG001 35\n", "ITM004 CTG003 35\n", "ITM005 CTG004 -42\n", "ITM006 CTG001 69" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_stocks = df.groupby([\"Item ID\", \"Category\"])[\"Inventory Discrepancy\"].agg(sum)\n", "pd.DataFrame(df_stocks).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ITM002 & ITM005 are the missed itmes : (Yoga Mat: 71 missing , foam rollers: 42 missing) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## the interactive bar chart (fig)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TimeMonthResponsible StaffCategoryInventory Discrepancy
0EveningMayRobert TaboneCTG001-4
1AfternoonAugustSimon FenechCTG003-5
2EveningMayJean-Pierre EllulCTG0010
3EveningMarchAndrew CauchiCTG001-4
4EveningSeptemberRobert TaboneCTG0022
\n", "
" ], "text/plain": [ " Time Month Responsible Staff Category Inventory Discrepancy\n", "0 Evening May Robert Tabone CTG001 -4\n", "1 Afternoon August Simon Fenech CTG003 -5\n", "2 Evening May Jean-Pierre Ellul CTG001 0\n", "3 Evening March Andrew Cauchi CTG001 -4\n", "4 Evening September Robert Tabone CTG002 2" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#make a df to customize\n", "activ_df = pd.DataFrame(df[[\"Time\", \"Month\",\"Responsible Staff\",\"Category\",\"Inventory Discrepancy\"]])\n", "activ_df.head()" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6f0b46edbc0d4e2e964a0304096251cd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "BokehModel(combine_events=True, render_bundle={'docs_json': {'45a36b19-d5a7-41f8-881d-3e1239c3249f': {'version…" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a vertical radio button group\n", "list_dropdown = pn.widgets.RadioButtonGroup(\n", " name=\"Select Data on X axis\",\n", " options=[\"Date\", \"Month\", \"Time\", \"Responsible Staff\", \"Category\", \"Item ID\" , \"Weekday\"],\n", " description=\"select data type to calculate stock for!\",\n", " button_style='outline'\n", ")\n", "\n", "# Wrap in a Panel component (optional, but can help with layout)\n", "component1 = pn.panel(list_dropdown)\n", "list_dropdown" ] }, { "cell_type": "code", "execution_count": 246, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Month=%{x}
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Discrepancy'].sum().reset_index()\n", " \n", " # Ensure 'Month' column is ordered correctly if the dropdown is \"Month\"\n", " if list_dropdown == \"Month\":\n", " grouped_df[\"Month\"] = pd.Categorical(grouped_df[\"Month\"], categories=month_order, ordered=True)\n", " grouped_df = grouped_df.sort_values(\"Month\")\n", "\n", " # Set title dynamically\n", " tit = \"Missing/overstock by item ID\" if list_dropdown == \"item ID\" else f'Inventory Discrepancy by {list_dropdown}'\n", "\n", " # Create the figure using Plotly Express\n", " fig = px.bar(\n", " grouped_df,\n", " x=list_dropdown,\n", " y=\"Inventory Discrepancy\",\n", " title=tit,\n", " color=\"Inventory Discrepancy\", # Color by category\n", " color_continuous_scale=['red', ACCENT], # Adjust color scale\n", " )\n", " # Improve layout\n", " fig.update_layout(\n", " xaxis_title=list_dropdown,\n", " yaxis_title=\"Total Inventory Discrepancy\",\n", " plot_bgcolor=\"#f8f8f8\", \n", " paper_bgcolor=\"#f8f8f8\", \n", " font=dict(color=\"black\"), \n", " xaxis=dict(gridcolor='lightgray', showgrid=True),\n", " yaxis=dict(gridcolor='black'),\n", " width=1200, \n", " height=500,\n", " )\n", " \n", " return fig\n", "\n", "component2 = pn.panel(inter_bar) # Assign to Panel\n", "\n", "inter_bar(\"Month\") # Show ordered months\n", "\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## creating KPIs cards " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "total actual stock= 94489\n", "\n", "total expected stock= 94447\n", "\n", "total overstock= 42 (.044% of the stock)\n", "\n", "ITM002(Yoga Mat) lost 71 pieces , ITM005(Foam Roller) lost 42 piece, totlal= 113\n", "\n", "the over stock items (ITM001: 16 , ITM003: 35 , ITM004: 35 , ITM006: 69), total= 155" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [], "source": [ "ACCENT = \"teal\"\n", "\n", "styles = {\n", " \"box-shadow\": \"rgba(50, 50, 93, 0.25) 0px 6px 12px -2px, rgba(0, 0, 0, 0.3) 0px 3px 7px -3px\",\n", " \"border-radius\": \"10px\",\n", " \"padding\": \"10px\",\n", " \"background\": \"#DCEFF0\",\n", " 'box-shadow': \"3px 3px 6px rgba(0,0,0,0.3)\",\n", " \"width\": \"385px\",\n", " \"height\": \"220px\" \n", " } # a dict to use later in the cards \n" ] }, { "cell_type": "code", "execution_count": 345, "metadata": {}, "outputs": [], "source": [ "# KPIs cards\n", "cards= pn.FlexBox(\n", "\n", "\n", "\n", " #'Highest miss/day\n", " pn.indicators.Number(name='The worst-performing day
(03.Aug & 30.May)📉',\n", " value=23, \n", " format='{value}'\n", " ' items',\n", " colors=[(1, 'black')],\n", " styles=styles,\n", " ),\n", "\n", " pn.indicators.Number(\n", " name='Franklin Attard
the biggest Loser',\n", " value=39,\n", " # Embed HTML to style the units separately:\n", " format='{value}'\n", " ' items',\n", " colors=[(0, ACCENT)]\n", " ,styles=styles\n", " ),\n", "\n", " pn.indicators.Number(\n", " name='Morning shift had the
hieghest missing rate',\n", " value=84,\n", " # Embed HTML to style the units separately:\n", " format='{value}'\n", " ' items',\n", " colors=[(0, ACCENT)]\n", " ,styles=styles\n", " )\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c1a9a63c491643ae9fce2c6354d3f970", "version_major": 2, "version_minor": 0 }, "text/plain": [ "BokehModel(combine_events=True, render_bundle={'docs_json': {'1183480e-5217-4646-99d1-1ec3fbe4428d': {'version…" ] }, "execution_count": 239, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cards" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### making cards of stock discrepancy" ] }, { "cell_type": "code", "execution_count": 240, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "# Data for missing and overstocked items\n", "missing_items = {\n", " \"Yoga Mat\": 71,\n", " \"Foam Roller\": 42\n", "}\n", "overstock_items = {\n", " \"Dumbbells\": 16,\n", " \"Resistance Band\": 35,\n", " \"Jump Rope\": 35,\n", " \"Kettlebell\": 69\n", "}\n", "\n", "# Convert to DataFrame\n", "df_missing = pd.DataFrame(missing_items.items(), columns=[\"Item\", \"Quantity Lost\"])\n", "df_overstock = pd.DataFrame(overstock_items.items(), columns=[\"Item\", \"Quantity Overstocked\"])\n" ] }, { "cell_type": "code", "execution_count": 241, "metadata": {}, "outputs": [], "source": [ "\n", "# Create bar plots\n", "fig_missing = px.bar(\n", " df_missing, \n", " x=\"Item\", \n", " y=\"Quantity Lost\", \n", " title=\"Missing Items(max:71, sum:113)\", \n", " color=\"Quantity Lost\",\n", " color_continuous_scale=[(0, \"lightcoral\"), (0.5, \"red\"), (1, \"darkred\")]\n", "\n", ")\n", "\n", "\n", "\n", "fig_overstock = px.bar(\n", " df_overstock, \n", " x=\"Item\", \n", " y=\"Quantity Overstocked\", \n", " title=\"Overstocked Items(max:69, sum:155)\", \n", " color=\"Quantity Overstocked\",\n", " color_continuous_scale=\"blues\"\n", ")\n", "\n", "# Improve layout\n", "fig_missing.update_layout(paper_bgcolor=\"#DCEFF0\", plot_bgcolor=\"#DCEFF0\", width=295, height=295, coloraxis_showscale=False,title_font=dict(size=16))\n", "fig_overstock.update_layout(paper_bgcolor=\"#DCEFF0\", plot_bgcolor=\"#DCEFF0\", width=295, height=295,coloraxis_showscale=False,title_font=dict(size=14) )\n", "\n", "component3=pn.panel(fig_missing)\n", "component4=pn.panel(fig_overstock)\n" ] }, { "cell_type": "code", "execution_count": 242, "metadata": {}, "outputs": [], "source": [ "\n", "# Convert to DataFrame\n", "df_missing = pd.DataFrame(missing_items.items(), columns=[\"Item\", \"Quantity Lost\"])\n", "df_overstock = pd.DataFrame(overstock_items.items(), columns=[\"Item\", \"Quantity Overstocked\"])\n", "\n", "# Create bar plots\n", "fig_missing = px.bar(\n", " df_missing, \n", " x=\"Item\", \n", " y=\"Quantity Lost\", \n", " title=\"Missing Items(max:71, sum:113)\", \n", " color=\"Quantity Lost\",\n", " color_continuous_scale=[(0, \"lightcoral\"), (0.5, \"red\"), (1, \"darkred\")]\n", ")\n", "\n", "\n", "missing_items_indicator = pn.indicators.Number(\n", " name=\"Missing Items\", # Title\n", " value=113, # Number displayed\n", " format=\"{:.0f}\", # No decimal places\n", " colors=[(50, \"red\")], # Customize color if needed\n", " font_size=\"20pt\" # Adjust font size\n", ")\n", "\n", "\n", "fig_overstock = px.bar(\n", " df_overstock, \n", " x=\"Item\", \n", " y=\"Quantity Overstocked\", \n", " title=\"Overstocked Items(max:69, sum:155)\", \n", " color=\"Quantity Overstocked\",\n", " color_continuous_scale=\"blues\"\n", ")\n", "\n", "# Improve layout\n", "fig_missing.update_layout(\n", " paper_bgcolor=\"#DCEFF0\",\n", " plot_bgcolor=\"#DCEFF0\",\n", " width=600,\n", " height=295,\n", " coloraxis_showscale=False,\n", " title_font=dict(size=28, color=\"#8B0000\") # Set title color to red\n", ")\n", "\n", "fig_overstock.update_layout(\n", " paper_bgcolor=\"#DCEFF0\",\n", " plot_bgcolor=\"#DCEFF0\",\n", " width=600,\n", " height=295,\n", " coloraxis_showscale=False,\n", " title_font=dict(size=28, color=\"teal\") # Set title color to blue\n", ")\n", "\n", "\n", "component3=pn.panel(fig_missing)\n", "component4=pn.panel(fig_overstock)\n", "comp5=pn.panel(missing_items_indicator)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dashboard layout" ] }, { "cell_type": "code", "execution_count": 381, "metadata": {}, "outputs": [], "source": [ "styles2 = {\n", " \"box-shadow\": \"rgba(50, 50, 93, 0.25) 0px 6px 12px -2px, rgba(0, 0, 0, 0.3) 0px 3px 7px -3px\",\n", " \"border-radius\": \"10px\",\n", " \"padding\": \"10px\",\n", " \"background\": \"#DADADA\",\n", " 'box-shadow': \"3px 3px 6px rgba(0,0,0,0.3)\",\n", " \"width\": \"280px\",\n", " \"height\": \"80px\" \n", " } \n", "sidebar_card1 = pn.indicators.Number(\n", " name=(\"Total records: 2000\"),\n", " styles=styles2\n", " \n", ")\n", "\n", "sidebar_card2 = pn.indicators.Number(\n", " name=(\"Start date: 01-01-2024 To 30-09-2024\"),\n", " styles=styles2\n", ")\n", "\n", "insight1 = pn.indicators.Number(\n", " name=\"More managment on Morinig shift is essential \",\n", " styles={\"background\": \"#6AB187\", \"border-radius\": \"10px\", \"padding\": \"10px\", \"width\": \"300\" , \"height\":\"120px\"}\n", ")\n", "\n", "insight2 = pn.indicators.Number(\n", " name=\"light items must be moved in more watched ereas \",\n", " styles={\"background\": \"#6AB187\", \"border-radius\": \"10px\", \"padding\": \"10px\", \"width\": \"300\" , \"height\":\"120px\"}\n", ")\n", "\n", "insight3 = pn.indicators.Number(\n", " name=\"need better rocording system to overcome the overstocking issue\",\n", " styles={\"background\": \"#6AB187\", \"border-radius\": \"10px\", \"padding\": \"10px\", \"width\": \"300\" , \"height\":\"120px\"}\n", ")\n" ] }, { "cell_type": "code", "execution_count": 389, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:12: SyntaxWarning:\n", "\n", "invalid escape sequence '\\K'\n", "\n", "C:\\Users\\S\\AppData\\Local\\Temp\\ipykernel_3408\\1352288665.py:12: SyntaxWarning:\n", "\n", "invalid escape sequence '\\K'\n", "\n" ] }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "\n", "\n", " \n", " \n", " Panel App\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", "
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