Time Series Charts

import pandas as pd
import matplotlib.pyplot as plt
 
# Load Data
orders = pd.read_csv('src_orders.csv', parse_dates=['created_at'])
items = pd.read_csv('src_order_items.csv')
products = pd.read_csv('src_products.csv')
 
# Merge for Analysis
df = orders.merge(items, on='order_id').merge(products, on='product_id')
 
# Ensure datetime conversion
df['created_at'] = pd.to_datetime(df['created_at'], format='mixed')

import pandas as pd
import matplotlib.pyplot as plt
 
# Daily sales data
daily_sales = df.groupby(df['created_at'].dt.date)['sale_price'].sum()
 
# Basic line chart
plt.figure(figsize=(14, 6))
plt.plot(daily_sales.index, daily_sales.values, linewidth=2, color='steelblue')
plt.title('Daily Sales Trend', fontsize=16, fontweight='bold')
plt.xlabel('Date', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

plt.figure(figsize=(14, 6))
 
plt.plot(daily_sales.index, daily_sales.values,
         color='green',
         linestyle='--',
         marker='o',
         markersize=8,
         label='Daily Sales')
 
plt.title('Daily Sales Trend (Styled)', fontsize=16, fontweight='bold')
plt.xlabel('Date', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.grid(True, alpha=0.3, linestyle='-.')
plt.legend()
 
plt.show()

plt.figure(figsize=(14, 6))
 
plt.plot(
    daily_sales.index,
    daily_sales.values,
    linewidth=2,
    color='steelblue',
    marker='o',           # Add marker
    markersize=4,
    markerfacecolor='white',
    markeredgewidth=1.5,
    label='Daily Sales'
)
 
plt.fill_between(
    daily_sales.index,
    daily_sales.values,
    alpha=0.2,           # Fill area
    color='steelblue'
)
 
plt.title('Daily Sales Trend', fontsize=16, fontweight='bold')
plt.xlabel('Date', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# Monthly sales by category
category_monthly = df.groupby([
    df['created_at'].dt.to_period('M'),
    'category'
])['sale_price'].sum().unstack()
 
# Top 5 categories
top_5 = df.groupby('category')['sale_price'].sum().nlargest(5).index
category_monthly = category_monthly[top_5]
 
# Multiple line chart
plt.figure(figsize=(14, 6))
 
for col in category_monthly.columns:
    plt.plot(
        category_monthly.index.astype(str),
        category_monthly[col],
        marker='o',
        linewidth=2,
        markersize=5,
        label=col
    )
 
plt.title('Monthly Sales Trend by Category (Top 5)', fontsize=16, fontweight='bold')
plt.xlabel('Month', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.legend(title='Category', bbox_to_anchor=(1.02, 1), loc='upper left')
plt.xticks(rotation=45)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 3, figsize=(16, 10), sharex=True)
axes = axes.flatten()
 
for i, col in enumerate(top_5):
    ax = axes[i]
    ax.plot(category_monthly.index.astype(str), category_monthly[col],
            marker='o', linewidth=2, color=f'C{i}')
    ax.set_title(col, fontsize=12, fontweight='bold')
    ax.grid(True, alpha=0.3)
    ax.tick_params(axis='x', rotation=45)
 
# Hide empty subplots
for j in range(len(top_5), len(axes)):
    axes[j].set_visible(False)
 
fig.suptitle('Monthly Sales Trend by Category', fontsize=16, fontweight='bold')
plt.tight_layout()
plt.show()

# Daily sales and moving averages
daily_sales = df.groupby(df['created_at'].dt.date)['sale_price'].sum()
ma_7 = daily_sales.rolling(window=7).mean()
ma_30 = daily_sales.rolling(window=30).mean()
 
plt.figure(figsize=(14, 6))
 
# Original data (light)
plt.plot(daily_sales.index, daily_sales.values,
         alpha=0.3, color='gray', label='Daily Sales')
 
# 7-day moving average
plt.plot(daily_sales.index, ma_7.values,
         linewidth=2, color='steelblue', label='7-Day Moving Average')
 
# 30-day moving average
plt.plot(daily_sales.index, ma_30.values,
         linewidth=2, color='coral', label='30-Day Moving Average')
 
plt.title('Daily Sales and Moving Averages', fontsize=16, fontweight='bold')
plt.xlabel('Date', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

from scipy import stats
import numpy as np
 
# Linear regression
x = np.arange(len(daily_sales))
slope, intercept, r_value, p_value, std_err = stats.linregress(x, daily_sales.values)
trend_line = slope * x + intercept
 
plt.figure(figsize=(14, 6))
 
plt.plot(daily_sales.index, daily_sales.values,
         alpha=0.5, color='steelblue', label='Daily Sales')
plt.plot(daily_sales.index, trend_line,
         linewidth=2, color='red', linestyle='--', label=f'Trend (slope: {slope:.2f})')
 
plt.title('Sales Trend Analysis', fontsize=16, fontweight='bold')
plt.xlabel('Date', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()
 
print(f"📈 Daily Average Growth: ${slope:.2f}")
print(f"📊 R² = {r_value**2:.3f}")

# Monthly sales
monthly = df.groupby([
    df['created_at'].dt.year.rename('year'),
    df['created_at'].dt.month.rename('month')
])['sale_price'].sum().reset_index()
 
# Pivot
monthly_pivot = monthly.pivot(index='month', columns='year', values='sale_price')
 
# Comparison chart
plt.figure(figsize=(12, 6))
 
for year in monthly_pivot.columns:
    plt.plot(
        monthly_pivot.index,
        monthly_pivot[year],
        marker='o',
        linewidth=2,
        label=f'{year}'
    )
 
plt.title('Year-over-Year Monthly Sales Comparison', fontsize=16, fontweight='bold')
plt.xlabel('Month', fontsize=12)
plt.ylabel('Sales ($)', fontsize=12)
plt.xticks(range(1, 13), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
plt.legend(title='Year')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

import plotly.express as px
 
# Monthly sales
monthly_sales = df.groupby(df['created_at'].dt.to_period('M'))['sale_price'].sum()
monthly_df = pd.DataFrame({
    'month': monthly_sales.index.astype(str),
    'sales': monthly_sales.values
})
 
fig = px.line(
    monthly_df,
    x='month',
    y='sales',
    title='Monthly Sales Trend',
    markers=True
)
 
fig.update_layout(
    xaxis_title='Month',
    yaxis_title='Sales ($)',
    hovermode='x unified'
)
 
fig.show()

import plotly.graph_objects as go
 
fig = go.Figure()
 
# Add multiple categories
for cat in top_5:
    cat_data = category_monthly[cat]
    fig.add_trace(go.Scatter(
        x=cat_data.index.astype(str),
        y=cat_data.values,
        mode='lines+markers',
        name=cat,
        hovertemplate='%{y:$,.0f}<extra></extra>'
    ))
 
fig.update_layout(
    title='Monthly Sales by Category (Interactive)',
    xaxis_title='Month',
    yaxis_title='Sales ($)',
    hovermode='x unified',
    legend_title='Category',
    # Range selector
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=3, label='3 Months', step='month', stepmode='backward'),
                dict(count=6, label='6 Months', step='month', stepmode='backward'),
                dict(step='all', label='All')
            ])
        ),
        rangeslider=dict(visible=True)
    )
)
 
fig.show()