📊 Matplotlib for CA Professionals

Apr: NPR 12.5L | May: NPR 13.8L | Jun: NPR 11.2L | Jul: NPR 14.5L | Aug: NPR 16.2L | Sep: NPR 17.8L
Oct: NPR 15.5L | Nov: NPR 14.2L | Dec: NPR 16.8L | Jan: NPR 19.1L | Feb: NPR 20.3L | Mar: NPR 21.5L

                    ┌──────────────────────────────────────────────────────────┐
  Visualization     │  Seaborn   Plotly   Bokeh   Pandas .plot()  GeoPandas  │
  libraries         │         (all built on top of Matplotlib or parallel)    │
                    ├──────────────────────────────────────────────────────────┤
                    │                   Matplotlib                              │ ← Core viz engine
                    ├──────────────────────────────────────────────────────────┤
                    │                     NumPy                                 │ ← Numerical engine
                    └──────────────────────────────────────────────────────────┘

┌─────────────────────── Figure (the full canvas — the A4 paper) ─────────────────────────┐
│                                                                                          │
│   ┌──────────────────── Axes (one plot area) ────────────────────────────────────────┐  │
│   │                                                                                  │  │
│   │   Title                                                                          │  │
│   │                                                                                  │  │
│   │   Y-axis   ──── Artist objects ────────────────────────────────────────────      │  │
│   │   label   (Lines, Bars, Patches, Text, Markers, Grid lines, Tick marks…)         │  │
│   │                                                                                  │  │
│   │                          X-axis label                                            │  │
│   └──────────────────────────────────────────────────────────────────────────────────┘  │
│                                   Legend (attached to Figure or Axes)                    │
└──────────────────────────────────────────────────────────────────────────────────────────┘

import matplotlib.pyplot as plt
import numpy as np

# --- Demonstrating the Figure/Axes hierarchy ---

# 1. Single Axes on one Figure
fig1, ax1 = plt.subplots(figsize=(8, 4))
ax1.plot([1, 2, 3, 4], [10, 20, 15, 25], color='steelblue', marker='o')
ax1.set_title('This is the Axes title (ax.set_title)')
fig1.suptitle('This is the Figure title (fig.suptitle)', fontsize=12, color='navy')
ax1.set_xlabel('X-axis label (ax.set_xlabel)')
ax1.set_ylabel('Y-axis label (ax.set_ylabel)')
plt.tight_layout()
plt.show()

# 2. Multiple Axes on one Figure (2 columns)
fig2, (ax_left, ax_right) = plt.subplots(1, 2, figsize=(12, 4))

ax_left.bar(['Q1','Q2','Q3','Q4'], [3750, 4850, 4650, 6090], color='steelblue')
ax_left.set_title('Quarterly Revenue (left Axes)')

ax_right.pie([30, 25, 25, 20], labels=['Q1','Q2','Q3','Q4'], autopct='%1.0f%%',
             colors=['#4472C4','#ED7D31','#A9D18E','#FF5757'])
ax_right.set_title('Revenue Mix (right Axes)')

fig2.suptitle('One Figure — Two Axes', fontsize=13, fontweight='bold')
plt.tight_layout()
plt.show()

print("Key insight: fig controls the canvas; ax controls the chart.")
print(f"fig type: {type(fig2)}")
print(f"ax type:  {type(ax_left)}")

Key insight: fig controls the canvas; ax controls the chart.
fig type: <class 'matplotlib.figure.Figure'>
ax type:  <class 'matplotlib.axes._axes.Axes'>

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [10, 20, 15])
plt.title('My Chart')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

fig, ax = plt.subplots()

ax.plot([1, 2, 3], [10, 20, 15])
ax.set_title('My Chart')         # clearly targets THIS axes
ax.set_xlabel('X')
ax.set_ylabel('Y')
plt.show()

# Install if needed
# !pip install matplotlib pandas numpy

import matplotlib.pyplot as plt   # plt is the universal shorthand
import pandas as pd
import numpy as np

# This makes charts appear inline in the notebook
%matplotlib inline

# Make charts look cleaner at higher resolution
plt.rcParams['figure.dpi'] = 100

print('Matplotlib version:', plt.matplotlib.__version__)
print('Ready!')

Matplotlib version: 3.10.6
Ready!

# ==== Shared data used throughout this notebook ====

months = ['Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar']

revenue  = [1250, 1380, 1120, 1450, 1620, 1780, 1550, 1420, 1680, 1910, 2030, 2150]  # in NPR  thousands
expenses = [ 920,  980,  870, 1050, 1180, 1250, 1100, 1020, 1190, 1320, 1410, 1490]
profit   = [r - e for r, e in zip(revenue, expenses)]

expense_categories = ['Salaries', 'Raw Materials', 'Marketing', 'Rent', 'Utilities', 'Depreciation', 'Others']
expense_values     = [4200, 2800, 1500, 1080, 480, 420, 320]   # Annual, in NPR  thousands

print('Data loaded. Amounts are in NPR  Thousands.')

Data loaded. Amounts are in NPR  Thousands.

┌─────────────────────────── Figure (the whole canvas) ─────────────────────────────┐
│                                                                                    │
│   ┌──────────────────────── Axes (the plot area) ──────────────────────────────┐  │
│   │   Title                                                                    │  │
│   │                                                                            │  │
│   │   Y-axis label                                                             │  │
│   │   │                         ← Plot (lines, bars, etc.) →                  │  │
│   │   │                                                                        │  │
│   │   └──────────────────────── X-axis label ─────────────────────────────    │  │
│   └────────────────────────────────────────────────────────────────────────────┘  │
│                                    Legend                                          │
└────────────────────────────────────────────────────────────────────────────────────┘

# Basic Line Chart
fig, ax = plt.subplots(figsize=(12, 5))

ax.plot(months, revenue, color='steelblue', linewidth=2.5, marker='o', markersize=6, label='Revenue')
ax.plot(months, expenses, color='tomato',   linewidth=2.5, marker='s', markersize=6, label='Expenses')
ax.plot(months, profit,   color='green',    linewidth=2.0, marker='^', markersize=6, label='Profit', linestyle='--')

# Labels and title
ax.set_title('Monthly P&L Trend — FY 2081-82 BS', fontsize=16, fontweight='bold', pad=15)
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Amount (NPR Thousands)', fontsize=12)

# Legend and grid
ax.legend(fontsize=11)
ax.grid(axis='y', linestyle='--', alpha=0.5)

# Format y-axis to show NPR  symbol
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'NPR {x:,.0f}K'))

# Add shaded region to highlight Q4
ax.axvspan(9, 11, alpha=0.08, color='gold', label='Q4')
ax.annotate('Best Quarter ↑', xy=(10, 2030), fontsize=9, color='goldenrod', ha='center')

plt.tight_layout()
plt.show()

# Year-on-Year Comparison Line Chart
fy24_revenue = [980, 1050, 890, 1100, 1250, 1380, 1200, 1100, 1350, 1560, 1700, 1820]
fy25_revenue = [1250, 1380, 1120, 1450, 1620, 1780, 1550, 1420, 1680, 1910, 2030, 2150]

fig, ax = plt.subplots(figsize=(12, 5))

ax.plot(months, fy24_revenue, color='slategray', linewidth=2, marker='o', markersize=5,
        label='FY 2080-81 BS', linestyle='--')
ax.plot(months, fy25_revenue, color='steelblue', linewidth=2.5, marker='o', markersize=6,
        label='FY 2081-82 BS')

# Fill area between lines to highlight growth
ax.fill_between(months, fy24_revenue, fy25_revenue, alpha=0.12, color='steelblue', label='YoY Growth')

ax.set_title('Revenue: Year-on-Year Comparison', fontsize=15, fontweight='bold')
ax.set_xlabel('Month')
ax.set_ylabel('Revenue (NPR Thousands)')
ax.legend()
ax.grid(axis='y', linestyle='--', alpha=0.4)

plt.tight_layout()
plt.show()

# Vertical Bar Chart — Annual Expenses by Category
colors = ['#2196F3', '#F44336', '#FF9800', '#4CAF50', '#9C27B0', '#795548', '#607D8B']

fig, ax = plt.subplots(figsize=(11, 6))

bars = ax.bar(expense_categories, expense_values, color=colors, edgecolor='white', linewidth=0.8, width=0.6)

# Add value labels on top of each bar
for bar, val in zip(bars, expense_values):
    ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 30,
            f'NPR {val:,}K', ha='center', va='bottom', fontsize=10, fontweight='bold')

ax.set_title('Annual Expense Breakdown — FY 2081-82 BS', fontsize=15, fontweight='bold', pad=15)
ax.set_xlabel('Expense Category', fontsize=12)
ax.set_ylabel('Amount (NPR Thousands)', fontsize=12)
ax.set_ylim(0, max(expense_values) * 1.18)
ax.grid(axis='y', linestyle='--', alpha=0.4)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

# Horizontal Bar — Client Revenue Contribution
clients = ['NTC Nepal Ltd', 'Ncell Pvt. Ltd.', 'Fusemachines Nepal',
           'InfoDevelopers Pvt. Ltd.', 'Deerwalk Services Pvt. Ltd.', 'LogicAbility Pvt. Ltd.']
client_revenue = [3850, 2960, 2480, 1950, 1620, 980]   # NPR  Thousands

# Sort for better visual
sorted_pairs = sorted(zip(client_revenue, clients))
client_revenue_s, clients_s = zip(*sorted_pairs)

fig, ax = plt.subplots(figsize=(10, 6))

bars = ax.barh(clients_s, client_revenue_s,
               color=plt.cm.Blues_r(np.linspace(0.3, 0.9, len(clients_s))),
               edgecolor='white')

for bar, val in zip(bars, client_revenue_s):
    ax.text(bar.get_width() + 30, bar.get_y() + bar.get_height() / 2,
            f'NPR {val:,}K', va='center', fontsize=10)

ax.set_title('Client-wise Revenue — FY 2081-82 BS', fontsize=14, fontweight='bold')
ax.set_xlabel('Revenue (NPR Thousands)')
ax.set_xlim(0, max(client_revenue_s) * 1.2)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(axis='x', linestyle='--', alpha=0.4)

plt.tight_layout()
plt.show()

# Budget vs Actual by Quarter
quarters      = ['Q1 (Shrawan-Ashwin)', 'Q2 (Kartik-Poush)', 'Q3 (Magh-Chaitra)', 'Q4 (Baishakh-Ashadh)']
budget_qtly   = [3800, 4200, 4500, 5100]   # NPR  Thousands
actual_qtly   = [3750, 4850, 4650, 6090]

x = np.arange(len(quarters))
width = 0.35

fig, ax = plt.subplots(figsize=(10, 6))

bars1 = ax.bar(x - width/2, budget_qtly, width, label='Budget', color='steelblue', alpha=0.85)
bars2 = ax.bar(x + width/2, actual_qtly, width, label='Actual', color='coral', alpha=0.85)

# Labels
for bar in bars1:
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 50,
            f'{bar.get_height():,}', ha='center', fontsize=9, color='steelblue')
for bar in bars2:
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 50,
            f'{bar.get_height():,}', ha='center', fontsize=9, color='coral')

# Variance annotation
for i, (b, a) in enumerate(zip(budget_qtly, actual_qtly)):
    variance = a - b
    color = 'green' if variance >= 0 else 'red'
    sign  = '+' if variance >= 0 else ''
    ax.text(i, max(b, a) + 250, f'Var: {sign}{variance:,}', ha='center',
            fontsize=8.5, color=color, fontweight='bold')

ax.set_title('Budget vs Actual Revenue by Quarter', fontsize=14, fontweight='bold')
ax.set_ylabel('Revenue (NPR Thousands)')
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend()
ax.set_ylim(0, max(actual_qtly) * 1.25)
ax.grid(axis='y', linestyle='--', alpha=0.4)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

# Pie Chart — Revenue by Business Segment
segments = ['Products', 'Services', 'Consulting', 'Exports']
seg_vals  = [6800, 4200, 2900, 1900]   # NPR  Thousands
colors    = ['#4472C4', '#ED7D31', '#A9D18E', '#FF5757']
explode   = [0.05, 0, 0, 0]   # Slightly separate the largest segment

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# --- Pie Chart ---
wedges, texts, autotexts = axes[0].pie(
    seg_vals, labels=segments, colors=colors, explode=explode,
    autopct='%1.1f%%', startangle=90, pctdistance=0.75,
    wedgeprops=dict(edgecolor='white', linewidth=2)
)
for text in autotexts:
    text.set_fontsize(11)
    text.set_fontweight('bold')
axes[0].set_title('Revenue by Segment\n(Pie Chart)', fontsize=13, fontweight='bold')

# --- Donut Chart (Pie with a hole) ---
wedges2, texts2, autotexts2 = axes[1].pie(
    seg_vals, labels=segments, colors=colors,
    autopct='%1.1f%%', startangle=90, pctdistance=0.80,
    wedgeprops=dict(width=0.55, edgecolor='white', linewidth=2)   # width < 1 creates donut
)
for text in autotexts2:
    text.set_fontsize(11)
    text.set_fontweight('bold')

total = sum(seg_vals)
axes[1].text(0, 0, f'NPR {total:,}K\nTotal', ha='center', va='center',
             fontsize=12, fontweight='bold', color='dimgray')
axes[1].set_title('Revenue by Segment\n(Donut Chart)', fontsize=13, fontweight='bold')

plt.suptitle('Revenue Mix — FY 2081-82 BS', fontsize=15, y=1.02)
plt.tight_layout()
plt.show()

# Simulate 200 invoice amounts
np.random.seed(42)
invoice_amounts = np.concatenate([
    np.random.exponential(scale=50000, size=160),   # Most invoices: small-medium
    np.random.uniform(200000, 500000, size=40)       # Some large invoices
])
invoice_amounts = np.clip(invoice_amounts, 5000, 500000)

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Histogram
axes[0].hist(invoice_amounts / 1000, bins=20, color='steelblue', edgecolor='white',
             alpha=0.85, linewidth=0.8)
axes[0].axvline(np.mean(invoice_amounts)/1000, color='red', linestyle='--',
                linewidth=2, label=f'Mean: NPR {np.mean(invoice_amounts)/1000:.0f}K')
axes[0].axvline(np.median(invoice_amounts)/1000, color='orange', linestyle='--',
                linewidth=2, label=f'Median: NPR {np.median(invoice_amounts)/1000:.0f}K')
axes[0].set_title('Distribution of Invoice Amounts', fontsize=13, fontweight='bold')
axes[0].set_xlabel('Invoice Amount (NPR Thousands)')
axes[0].set_ylabel('Frequency (No. of Invoices)')
axes[0].legend()
axes[0].grid(axis='y', linestyle='--', alpha=0.4)

# Cumulative histogram — shows what % of invoices are below a value
axes[1].hist(invoice_amounts / 1000, bins=30, cumulative=True, density=True,
             color='coral', edgecolor='white', alpha=0.85)
axes[1].axhline(0.8, color='navy', linestyle='--', linewidth=1.5, label='80th Percentile')
axes[1].set_title('Cumulative Distribution (CDF)', fontsize=13, fontweight='bold')
axes[1].set_xlabel('Invoice Amount (NPR Thousands)')
axes[1].set_ylabel('Cumulative Fraction')
axes[1].legend()
axes[1].grid(axis='y', linestyle='--', alpha=0.4)
axes[1].yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y*100:.0f}%'))

plt.suptitle('Invoice Amount Analysis — Audit Sampling View', fontsize=14, y=1.02)
plt.tight_layout()
plt.show()

p80 = np.percentile(invoice_amounts, 80)
above_p80 = np.sum(invoice_amounts > p80)
print(f'80th percentile: NPR {p80:,.0f}')
print(f'Invoices above NPR {p80:,.0f}: {above_p80} ({above_p80/len(invoice_amounts)*100:.1f}%) — these form the audit focus')

80th percentile: NPR 212,208
Invoices above NPR 212,208: 40 (20.0%) — these form the audit focus

# Scatter plot — Monthly Revenue vs Profit
revenue_arr = np.array(revenue)
profit_arr  = np.array(profit)

fig, ax = plt.subplots(figsize=(9, 6))

scatter = ax.scatter(revenue_arr, profit_arr,
                     c=range(12), cmap='RdYlGn', s=120, zorder=5, edgecolors='white', linewidth=1.5)

# Label each point with month name
for i, month in enumerate(months):
    ax.annotate(month, (revenue_arr[i], profit_arr[i]),
                textcoords='offset points', xytext=(8, 4), fontsize=9)

# Add trend line
z = np.polyfit(revenue_arr, profit_arr, 1)
p = np.poly1d(z)
x_line = np.linspace(min(revenue_arr), max(revenue_arr), 100)
ax.plot(x_line, p(x_line), 'navy', linestyle='--', linewidth=1.5, label='Trend', alpha=0.7)

plt.colorbar(scatter, ax=ax, label='Month (Apr=0 → Mar=11)')
ax.set_title('Revenue vs Profit — Monthly Correlation', fontsize=13, fontweight='bold')
ax.set_xlabel('Monthly Revenue (NPR Thousands)')
ax.set_ylabel('Monthly Profit (NPR Thousands)')
ax.grid(linestyle='--', alpha=0.3)
ax.legend()

corr = np.corrcoef(revenue_arr, profit_arr)[0, 1]
ax.text(0.05, 0.95, f'Correlation: {corr:.3f}', transform=ax.transAxes,
        fontsize=11, verticalalignment='top',
        bbox=dict(boxstyle='round', facecolor='lightyellow', alpha=0.8))

plt.tight_layout()
plt.show()

# 2×2 Financial Dashboard
fig, axes = plt.subplots(2, 2, figsize=(16, 10))
fig.suptitle('Management Dashboard — FY 2081-82 BS\nSunrise Trading Pvt. Ltd.', fontsize=16, fontweight='bold', y=0.98)

# ── Chart 1 (top-left): Revenue & Expense trend ──
axes[0, 0].plot(months, revenue, 'steelblue', marker='o', markersize=5, linewidth=2, label='Revenue')
axes[0, 0].plot(months, expenses, 'tomato', marker='s', markersize=5, linewidth=2, label='Expenses')
axes[0, 0].fill_between(months, expenses, revenue, alpha=0.1, color='green')
axes[0, 0].set_title('Revenue vs Expenses Trend', fontweight='bold')
axes[0, 0].set_ylabel('NPR  Thousands')
axes[0, 0].legend(fontsize=9)
axes[0, 0].grid(axis='y', linestyle='--', alpha=0.4)
axes[0, 0].tick_params(axis='x', rotation=45)

# ── Chart 2 (top-right): Monthly Profit bars ──
bar_colors = ['green' if p > 0 else 'red' for p in profit]
axes[0, 1].bar(months, profit, color=bar_colors, edgecolor='white', linewidth=0.8)
axes[0, 1].axhline(np.mean(profit), color='navy', linestyle='--', linewidth=1.5,
                    label=f'Avg: NPR {np.mean(profit):,.0f}K')
axes[0, 1].set_title('Monthly Net Profit', fontweight='bold')
axes[0, 1].set_ylabel('NPR  Thousands')
axes[0, 1].legend(fontsize=9)
axes[0, 1].grid(axis='y', linestyle='--', alpha=0.4)
axes[0, 1].tick_params(axis='x', rotation=45)

# ── Chart 3 (bottom-left): Expense mix donut ──
colors_pie = ['#4472C4','#ED7D31','#A9D18E','#FF5757','#9B59B6','#E74C3C','#1ABC9C']
axes[2-2, 0+2-2]  # placeholder to maintain comment alignment
axes[1, 0].pie(expense_values, labels=expense_categories, autopct='%1.0f%%',
               colors=colors_pie, startangle=90, pctdistance=0.82,
               wedgeprops=dict(width=0.55, edgecolor='white'))
axes[1, 0].set_title('Expense Mix (Donut)', fontweight='bold')

# ── Chart 4 (bottom-right): Cumulative profit ──
cum_profit = np.cumsum(profit)
axes[1, 1].fill_between(months, cum_profit, color='steelblue', alpha=0.3)
axes[1, 1].plot(months, cum_profit, color='steelblue', marker='o', markersize=5, linewidth=2)
axes[1, 1].axhline(0, color='black', linewidth=0.8)
axes[1, 1].set_title('Cumulative Profit (YTD)', fontweight='bold')
axes[1, 1].set_ylabel('NPR  Thousands')
axes[1, 1].grid(axis='y', linestyle='--', alpha=0.4)
axes[1, 1].tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

# Using a built-in style for a more professional look
available_styles = plt.style.available
print('Available styles (first 10):', available_styles[:10])

# Use 'seaborn-v0_8-whitegrid' for clean, publication-ready charts
with plt.style.context('seaborn-v0_8-whitegrid'):
    fig, ax = plt.subplots(figsize=(11, 5))
    ax.plot(months, revenue, marker='o', linewidth=2.5, color='#2196F3', markersize=7)
    ax.fill_between(months, revenue, alpha=0.15, color='#2196F3')

    # Add data labels on each point
    for i, (m, r) in enumerate(zip(months, revenue)):
        ax.annotate(f'NPR {r:,}K', (m, r), textcoords='offset points',
                    xytext=(0, 10), ha='center', fontsize=8.5, color='#1565C0')

    ax.set_title('Revenue Trend — FY 2081-82 BS\n(Seaborn Whitegrid Style)', fontsize=14, fontweight='bold')
    ax.set_ylabel('NPR  Thousands')
    ax.set_ylim(900, 2400)
    plt.tight_layout()
    plt.show()

Available styles (first 10): ['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot']

# Create a chart and save it
fig, ax = plt.subplots(figsize=(11, 5))
ax.plot(months, revenue, color='steelblue', marker='o', linewidth=2.5, label='Revenue')
ax.plot(months, expenses, color='tomato',   marker='s', linewidth=2.5, label='Expenses')
ax.set_title('Monthly P&L Trend — FY 2081-82 BS', fontsize=14, fontweight='bold')
ax.set_ylabel('NPR  Thousands')
ax.legend()
ax.grid(axis='y', linestyle='--', alpha=0.4)
plt.tight_layout()

# Save options:
plt.savefig('revenue_trend.png', dpi=300, bbox_inches='tight')       # High-res PNG
plt.savefig('revenue_trend.pdf', bbox_inches='tight')                 # PDF (vector)

print('Charts saved:')
print('  revenue_trend.png  (300 DPI — for presentations)')
print('  revenue_trend.pdf  (vector — for print/reports)')

plt.show()

Charts saved:
  revenue_trend.png  (300 DPI — for presentations)
  revenue_trend.pdf  (vector — for print/reports)

quarters    = ['Q1 (Shrawan-Ashwin)', 'Q2 (Kartik-Poush)', 'Q3 (Magh-Chaitra)', 'Q4 (Baishakh-Ashadh)']
salaries    = [1200, 1250, 1280, 1350]   # NPR  Thousands
materials   = [800,  950,  880,  1050]
overheads   = [320,  345,  330,  380]
marketing   = [180,  210,  195,  260]

import matplotlib.pyplot as plt
import numpy as np

quarters  = ['Q1 (Shrawan-Ashwin)', 'Q2 (Kartik-Poush)', 'Q3 (Magh-Chaitra)', 'Q4 (Baishakh-Ashadh)']
salaries  = [1200, 1250, 1280, 1350]
materials = [800,  950,  880, 1050]
overheads = [320,  345,  330,  380]
marketing = [180,  210,  195,  260]

# Hint: Use ax.bar(..., bottom=...) to stack bars
# Each subsequent bar's bottom = sum of all previous categories

import matplotlib.pyplot as plt
import numpy as np

# Hint: For a waterfall chart:
# - Positive items (Revenue, Gross Profit, Net Profit) = green/blue bars from 0
# - Negative items (COGS, Opex, etc.) = red bars sitting on top of running total
# - Use 'bottom' parameter in ax.bar() to position bars correctly

items  = ['Revenue', 'COGS', 'Gross\nProfit', 'Opex', 'Depreciation', 'Finance\nCost', 'Net Profit\nBefore Tax']
values = [18000, -10800, 7200, -3300, -420, -216, 3264]   # NPR  Thousands

# SOLUTION — Exercise 1: Stacked Bar Chart
import matplotlib.pyplot as plt
import numpy as np

quarters  = ['Q1 (Shrawan-Ashwin)', 'Q2 (Kartik-Poush)', 'Q3 (Magh-Chaitra)', 'Q4 (Baishakh-Ashadh)']
salaries  = np.array([1200, 1250, 1280, 1350])
materials = np.array([800,  950,  880, 1050])
overheads = np.array([320,  345,  330,  380])
marketing = np.array([180,  210,  195,  260])

fig, ax = plt.subplots(figsize=(10, 6))

b1 = ax.bar(quarters, salaries,  label='Salaries',   color='#4472C4', width=0.5)
b2 = ax.bar(quarters, materials, label='Materials',  color='#ED7D31', width=0.5, bottom=salaries)
b3 = ax.bar(quarters, overheads, label='Overheads',  color='#A9D18E', width=0.5, bottom=salaries+materials)
b4 = ax.bar(quarters, marketing, label='Marketing',  color='#FF5757', width=0.5, bottom=salaries+materials+overheads)

# Total labels on top
totals = salaries + materials + overheads + marketing
for i, total in enumerate(totals):
    ax.text(i, total + 30, f'NPR {total:,}K', ha='center', fontsize=10, fontweight='bold')

ax.set_title('Quarterly Cost Structure — Stacked', fontsize=13, fontweight='bold')
ax.set_ylabel('NPR  Thousands')
ax.set_ylim(0, max(totals) * 1.15)
ax.legend(loc='upper left')
ax.grid(axis='y', linestyle='--', alpha=0.4)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.show()

# SOLUTION — Exercise 2: Waterfall / P&L Bridge Chart
import matplotlib.pyplot as plt
import numpy as np

items  = ['Revenue', 'COGS', 'Gross\nProfit', 'Opex', 'Depreciation', 'Finance\nCost', 'Net Profit\nBef. Tax']
values = [18000, -10800, 7200, -3300, -420, -216, 3264]

# Calculate running totals to position bars
subtotals = [0, 18000, 18000, 7200, 7200, 3900-420, 3480-216]
# For absolute bars (Revenue, Gross Profit, Net Profit)
absolute_items = {0, 2, 6}

bottoms = []
for i, (item, val) in enumerate(zip(items, values)):
    if i in absolute_items:
        bottoms.append(0)
    else:
        bottoms.append(subtotals[i])

colors = []
for i, val in enumerate(values):
    if i in absolute_items:
        colors.append('steelblue')
    elif val > 0:
        colors.append('seagreen')
    else:
        colors.append('tomato')

fig, ax = plt.subplots(figsize=(12, 6))

bars = ax.bar(items, [abs(v) for v in values], bottom=bottoms,
              color=colors, edgecolor='white', linewidth=1.5, width=0.55)

# Value labels
for bar, val, bot in zip(bars, values, bottoms):
    label = f'NPR {abs(val):,}K'
    y_pos = bot + abs(val) / 2
    ax.text(bar.get_x() + bar.get_width()/2, y_pos, label,
            ha='center', va='center', fontsize=9.5, fontweight='bold', color='white')

# Connector lines
for i in range(len(items) - 1):
    if i not in absolute_items and i+1 not in absolute_items:
        ax.plot([i + 0.28, i + 0.72], [bottoms[i+1], bottoms[i+1]],
                color='gray', linewidth=1, linestyle='--', alpha=0.6)

ax.set_title('P&L Bridge — FY 2081-82 BS\n(Revenue → Net Profit Before Tax)', fontsize=13, fontweight='bold')
ax.set_ylabel('NPR  Thousands')
ax.grid(axis='y', linestyle='--', alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
from matplotlib.patches import Patch
ax.legend(handles=[Patch(color='steelblue', label='Total'),
                   Patch(color='tomato',    label='Reduction'),
                   Patch(color='seagreen',  label='Addition')])
plt.tight_layout()
plt.show()