dashboard.py

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import streamlit as st
from babel.numbers import format_currency   
sns.set(style='dark')


# create_daily_orders_df() digunakan untuk menyiapkan daily_orders_df.
def create_daily_orders_df(df):
    daily_orders_df = df.resample(rule='D', on='order_date').agg({
        "order_id": "nunique",
        "total_price": "sum"
    })
    daily_orders_df = daily_orders_df.reset_index()
    daily_orders_df.rename(columns={
        "order_id": "order_count",
        "total_price": "revenue"
    }, inplace=True)

    return daily_orders_df

# create_sum_order_items_df() bertanggung jawab untuk menyiapkan sum_orders_items_df.
def create_sum_order_items_df(df):
    sum_order_items_df = df.groupby("product_name").quantity_x.sum().sort_values(ascending=False).reset_index()
    return sum_order_items_df

# create_bygender_df() digunakan untuk menyiapkan bygender_df.
def create_bygender_df(df):
    bygender_df = df.groupby(by="gender").customer_id.nunique().reset_index()
    bygender_df.rename(columns={
        "customer_id": "customer_count"
    }, inplace=True)

    return bygender_df

# create_byage_df() merupakan helper function yang digunakan untuk menyiapkan byage_df.
def create_byage_df(df):
    byage_df = df.groupby(by="age_group").customer_id.nunique().reset_index()
    byage_df.rename(columns={
        "customer_id": "customer_count"
    }, inplace=True)
    byage_df['age_group'] = pd.Categorical(byage_df['age_group'], categories=["Youth", "Adults", "Seniors"])

    return byage_df

# create_bystate_df() digunakan untuk menyiapkan bystate_df.
def create_bystate_df(df):
    bystate_df = df.groupby(by="state").customer_id.nunique().reset_index()
    bystate_df.rename(columns={
        "customer_id": "customer_count"
    }, inplace=True)

    return bystate_df

# create_rfm_df() bertanggung jawab untuk menghasilkan rfm_df.
def create_rfm_df(df):
    rfm_df = df.groupby(by="customer_id", as_index=False).agg({
        "order_date": "max",
        "order_id": "nunique",
        "total_price": "sum"
    })
    rfm_df.columns = ["customer_id", "max_order_timestamp", "frequency", "monetary"]

    rfm_df["max_order_timestamp"] = rfm_df["max_order_timestamp"].dt.date
    recent_date = df["order_date"].dt.date.max()
    rfm_df["recency"] = rfm_df["max_order_timestamp"].apply(lambda x: (recent_date - x).days)
    rfm_df.drop("max_order_timestamp", axis=1, inplace=True)

    return rfm_df


# Load cleaned data
all_df = pd.read_csv("all_data.csv")


datetime_columns = ["order_date", "delivery_date"]
all_df.sort_values(by="order_date", inplace=True)
all_df.reset_index(inplace=True)

for column in datetime_columns:
    all_df[column] = pd.to_datetime(all_df[column])


# Filter data
min_date = all_df["order_date"].min()
max_date = all_df["order_date"].max()

with st.sidebar:
    # Menambahkan logo perusahaan
    st.image("/~https://github.com/dicodingacademy/assets/raw/main/logo.png")

    # Mengambil start_date & end_date dari date_input
    start_date, end_date = st.date_input(
        label = 'Rentang Waktu', 
        min_value = min_date,
        max_value = max_date,
        value = [min_date, max_date]
    )

main_df = all_df[(all_df["order_date"] >= str(start_date)) & (all_df["order_date"] <= str(end_date))]

# Menyiapakan berbagai dataframe
daily_orders_df = create_daily_orders_df(main_df)
sum_order_items_df = create_sum_order_items_df(main_df)
bygender_df = create_bygender_df(main_df)
byage_df = create_byage_df(main_df)
bystate_df = create_bystate_df(main_df)
rfm_df = create_rfm_df(main_df)


# plot number of daily orders (2021)
st.header('Dicoding Collection Dashboard :sparkles:')
st.subheader('Daily Orders')

col1, col2 = st.columns(2)

with col1:
    total_orders = daily_orders_df["order_count"].sum()
    st.metric("Total orders", value=total_orders)

with col2:
    total_revenue = format_currency(daily_orders_df["revenue"].sum(), 'AUD', locale='es_CO')
    st.metric("Total revenue", value=total_revenue)

fig, ax = plt.subplots(figsize=(16, 8))
ax.plot(
    daily_orders_df["order_date"],
    daily_orders_df["order_count"],
    marker='o',
    linewidth=2,
    color="#90CAF9"
)
ax.tick_params(axis='y', labelsize=20)
ax.tick_params(axis='x', labelsize=15)

st.pyplot(fig)


# Product performance
st.subheader('Best & Worst Performing Products')

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(35, 15))

colors = ["#90CAF9", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3"]

sns.barplot(
    x="quantity_x",
    y="product_name",
    data=sum_order_items_df.head(5),
    palette=colors,
    ax=ax[0]
)
ax[0].set_ylabel(None)
ax[0].set_xlabel("Number of Sales", fontsize=30)
ax[0].set_title("Best Performing Products", loc="center", fontsize=50)
ax[0].tick_params(axis='y', labelsize=35)
ax[0].tick_params(axis='x', labelsize=30)

sns.barplot(
    x="quantity_x",
    y="product_name",
    data=sum_order_items_df.sort_values(by="quantity_x", ascending=True).head(5),
    palette=colors,
    ax=ax[1]
)
ax[1].set_ylabel(None)
ax[1].set_xlabel("Number of Sales", fontsize=30)
ax[1].invert_xaxis()
ax[1].yaxis.set_label_position("right")
ax[1].yaxis.tick_right()
ax[1].set_title("Worst Performing Products", loc="center", fontsize=50)
ax[1].tick_params(axis='y', labelsize=35)
ax[1].tick_params(axis='x', labelsize=30)

st.pyplot(fig)

# Customer demographic
st.subheader('Customer Demographics')

col1, col2 = st.columns(2)

with col1:
    fig, ax = plt.subplots(figsize=(20, 10))

    sns.barplot(
        y="customer_count", 
        x="gender",
        data=bygender_df.sort_values(by="customer_count", ascending=False),
        palette=colors,
        ax=ax
    )
    ax.set_title("Number of Customers by Gender", loc="center", fontsize=50)
    ax.set_ylabel(None)
    ax.set_xlabel(None)
    ax.tick_params(axis='x', labelsize=35)
    ax.tick_params(axis='y', labelsize=30)
    st.pyplot(fig)

with col2:
    fig, ax = plt.subplots(figsize=(20, 10))

    colors = ["#D3D3D3", "#90CAF9", "#D3D3D3", "#D3D3D3", "#D3D3D3"]

    sns.barplot(
        y="customer_count",
        x="age_group",
        data=byage_df.sort_values(by="age_group", ascending=False),
        palette=colors,
        ax=ax
    )
    ax.set_title("Number of Customers by Age", loc="center", fontsize=50)
    ax.set_ylabel(None)
    ax.set_xlabel(None)
    ax.tick_params(axis='x', labelsize=35)
    ax.tick_params(axis='y', labelsize=30)
    st.pyplot(fig)

fig, ax = plt.subplots(figsize=(20, 10))

colors = ["#90CAF9", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3"]

sns.barplot(
    x="customer_count",
    y="state",
    data=bystate_df.sort_values(by="customer_count", ascending=False),
    palette=colors,
    ax=ax
)
ax.set_title("Number of Customers by States", loc="center", fontsize=30)
ax.set_ylabel(None)
ax.set_xlabel(None)
ax.tick_params(axis='x', labelsize=20)
ax.tick_params(axis='y', labelsize=20)
st.pyplot(fig)

# Best Customers Based on RFM Parameters
st.subheader('Best Customers Based on RFM Parameters')

col1, col2, col3 = st.columns(3)

with col1:
    avg_recency = round(rfm_df.recency.mean(), 1)
    st.metric("Average Recency (days)", value=avg_recency)

with col2:
    avg_frequency = round(rfm_df.frequency.mean(), 2)
    st.metric("Average Frequency", value=avg_frequency)

with col3:
    avg_monetary = format_currency(rfm_df.monetary.mean(), 'AUD', locale='es_CO')
    st.metric("Average Monetary", value=avg_monetary)

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(35, 15))
colors = ["#90CAF9", "#90CAF9", "#90CAF9", "#90CAF9", "#90CAF9"]

sns.barplot(
    y="recency", 
    x="customer_id",
    data=rfm_df.sort_values(by="recency", ascending=True).head(5), palette=colors, 
    ax=ax[0]
)
ax[0].set_ylabel(None)
ax[0].set_xlabel("customer_id", fontsize=30)
ax[0].set_title("By Recency (days)", loc="center", fontsize=50)
ax[0].tick_params(axis='y', labelsize=30)
ax[0].tick_params(axis='x', labelsize=35)

sns.barplot(
    y="frequency",
    x="customer_id",
    data=rfm_df.sort_values(by="frequency", ascending=False).head(5),
    palette=colors,
    ax=ax[1]
)
ax[1].set_ylabel(None)
ax[1].set_xlabel("customer_id", fontsize=30)
ax[1].set_title("By Frequency", loc="center", fontsize=50)
ax[1].tick_params(axis='y', labelsize=30)
ax[1].tick_params(axis='x', labelsize=35)

sns.barplot(
    y="monetary",
    x="customer_id",
    data=rfm_df.sort_values(by="monetary", ascending=False).head(5),
    palette=colors,
    ax=ax[2]
)
ax[2].set_ylabel(None)
ax[2].set_xlabel("customer_id", fontsize=30)
ax[2].set_title("By Monetary", loc="center", fontsize=50)
ax[2].tick_params(axis='y', labelsize=30)
ax[2].tick_params(axis='x', labelsize=35)

st.pyplot(fig)

st.caption('Copyright (c) Dicoding 2023')