File size: 3,808 Bytes
c25b09f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
# @Email: [email protected]
# @Website: https://pythonandvba.com
# @YouTube: https://youtube.com/c/CodingIsFun
# @Project: Sales Dashboard w/ Streamlit
import pandas as pd # pip install pandas openpyxl
import plotly.express as px # pip install plotly-express
import streamlit as st # pip install streamlit
# emojis: https://www.webfx.com/tools/emoji-cheat-sheet/
st.set_page_config(page_title="Sales Dashboard", page_icon=":bar_chart:", layout="wide")
# ---- READ EXCEL ----
@st.cache_data
def get_data_from_excel():
df = pd.read_excel(
io="supermarkt_sales.xlsx",
engine="openpyxl",
sheet_name="Sales",
skiprows=3,
usecols="B:R",
nrows=1000,
)
# Add 'hour' column to dataframe
df["hour"] = pd.to_datetime(df["Time"], format="%H:%M:%S").dt.hour
return df
df = get_data_from_excel()
# ---- SIDEBAR ----
st.sidebar.header("Please Filter Here:")
city = st.sidebar.multiselect(
"Select the City:",
options=df["City"].unique(),
default=df["City"].unique()
)
customer_type = st.sidebar.multiselect(
"Select the Customer Type:",
options=df["Customer_type"].unique(),
default=df["Customer_type"].unique(),
)
gender = st.sidebar.multiselect(
"Select the Gender:",
options=df["Gender"].unique(),
default=df["Gender"].unique()
)
df_selection = df.query(
"City == @city & Customer_type ==@customer_type & Gender == @gender"
)
# Check if the dataframe is empty:
if df_selection.empty:
st.warning("No data available based on the current filter settings!")
st.stop() # This will halt the app from further execution.
# ---- MAINPAGE ----
st.title(":bar_chart: Sales Dashboard")
st.markdown("##")
# TOP KPI's
total_sales = int(df_selection["Total"].sum())
average_rating = round(df_selection["Rating"].mean(), 1)
star_rating = ":star:" * int(round(average_rating, 0))
average_sale_by_transaction = round(df_selection["Total"].mean(), 2)
left_column, middle_column, right_column = st.columns(3)
with left_column:
st.subheader("Total Sales:")
st.subheader(f"US $ {total_sales:,}")
with middle_column:
st.subheader("Average Rating:")
st.subheader(f"{average_rating} {star_rating}")
with right_column:
st.subheader("Average Sales Per Transaction:")
st.subheader(f"US $ {average_sale_by_transaction}")
st.markdown("""---""")
# SALES BY PRODUCT LINE [BAR CHART]
sales_by_product_line = df_selection.groupby(by=["Product line"])[["Total"]].sum().sort_values(by="Total")
fig_product_sales = px.bar(
sales_by_product_line,
x="Total",
y=sales_by_product_line.index,
orientation="h",
title="<b>Sales by Product Line</b>",
color_discrete_sequence=["#0083B8"] * len(sales_by_product_line),
template="plotly_white",
)
fig_product_sales.update_layout(
plot_bgcolor="rgba(0,0,0,0)",
xaxis=(dict(showgrid=False))
)
# SALES BY HOUR [BAR CHART]
sales_by_hour = df_selection.groupby(by=["hour"])[["Total"]].sum()
fig_hourly_sales = px.bar(
sales_by_hour,
x=sales_by_hour.index,
y="Total",
title="<b>Sales by hour</b>",
color_discrete_sequence=["#0083B8"] * len(sales_by_hour),
template="plotly_white",
)
fig_hourly_sales.update_layout(
xaxis=dict(tickmode="linear"),
plot_bgcolor="rgba(0,0,0,0)",
yaxis=(dict(showgrid=False)),
)
left_column, right_column = st.columns(2)
left_column.plotly_chart(fig_hourly_sales, use_container_width=True)
right_column.plotly_chart(fig_product_sales, use_container_width=True)
# ---- HIDE STREAMLIT STYLE ----
hide_st_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
header {visibility: hidden;}
</style>
"""
st.markdown(hide_st_style, unsafe_allow_html=True)
|