Data Visualization dengan Python

# Instalasi semua library visualisasi
pip install matplotlib seaborn plotly pandas

# Untuk Jupyter Notebook / Google Colab
pip install matplotlib seaborn plotly pandas jupyter

# Verifikasi instalasi
python -c "import matplotlib; print(matplotlib.__version__)"
python -c "import seaborn; print(seaborn.__version__)"
python -c "import plotly; print(plotly.__version__)"

import matplotlib.pyplot as plt

# Data penjualan bulanan (dalam jutaan Rupiah)
bulan = ['Jan', 'Feb', 'Mar', 'Apr', 'Mei', 'Jun',
         'Jul', 'Ags', 'Sep', 'Okt', 'Nov', 'Des']
penjualan_2025 = [45, 52, 48, 61, 55, 67, 72, 69, 78, 82, 75, 91]
penjualan_2026 = [51, 58, 63, 70, 68, 79, 85, 82, 90, 95, 88, 102]

# Membuat figure dengan ukuran tertentu
fig, ax = plt.subplots(figsize=(12, 6))

# Plot dua garis
ax.plot(bulan, penjualan_2025, marker='o', linewidth=2,
        label='2025', color='#3498db')
ax.plot(bulan, penjualan_2026, marker='s', linewidth=2,
        label='2026', color='#e74c3c')

# Kustomisasi
ax.set_title('Perbandingan Penjualan Bulanan', fontsize=16, fontweight='bold')
ax.set_xlabel('Bulan', fontsize=12)
ax.set_ylabel('Penjualan (Juta Rp)', fontsize=12)
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
ax.set_ylim(0, 120)

plt.tight_layout()
plt.savefig('line_chart.png', dpi=150, bbox_inches='tight')
plt.show()

import matplotlib.pyplot as plt

# Data penggunaan bahasa pemrograman
bahasa = ['Python', 'JavaScript', 'Java', 'C++', 'Go', 'Rust']
popularitas = [31.5, 28.2, 15.3, 10.1, 8.2, 4.7]

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

# Bar chart dengan gradient colors
colors = ['#3498db', '#f1c40f', '#e74c3c', '#2ecc71', '#9b59b6', '#e67e22']
bars = ax.bar(bahasa, popularitas, color=colors, edgecolor='white', linewidth=1.2)

# Tambahkan label nilai di atas setiap bar
for bar, val in zip(bars, popularitas):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,
            f'{val}%', ha='center', fontsize=11, fontweight='bold')

ax.set_title('Popularitas Bahasa Pemrograman 2026', fontsize=16, fontweight='bold')
ax.set_xlabel('Bahasa Pemrograman', fontsize=12)
ax.set_ylabel('Market Share (%)', fontsize=12)
ax.set_ylim(0, 40)
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# Membuat 4 subplot dalam grid 2x2
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Dashboard Analisis Data', fontsize=18, fontweight='bold')

# Plot 1: Line chart
x = np.linspace(0, 10, 100)
axes[0, 0].plot(x, np.sin(x), color='#3498db', linewidth=2)
axes[0, 0].set_title('Sin Wave')
axes[0, 0].grid(True, alpha=0.3)

# Plot 2: Bar chart
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 12, 67, 34]
axes[0, 1].bar(categories, values, color='#2ecc71')
axes[0, 1].set_title('Distribusi Kategori')

# Plot 3: Scatter plot
np.random.seed(42)
x_scatter = np.random.randn(100)
y_scatter = x_scatter * 0.5 + np.random.randn(100) * 0.5
axes[1, 0].scatter(x_scatter, y_scatter, alpha=0.6, color='#e74c3c')
axes[1, 0].set_title('Scatter Plot')

# Plot 4: Histogram
data = np.random.normal(50, 15, 1000)
axes[1, 1].hist(data, bins=30, color='#9b59b6', edgecolor='white')
axes[1, 1].set_title('Distribusi Data')

plt.tight_layout()
plt.savefig('subplots.png', dpi=150, bbox_inches='tight')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Membuat sample data korelasi
np.random.seed(42)
data = pd.DataFrame({
    'Suhu': np.random.normal(30, 5, 100),
    'Kelembaban': np.random.normal(70, 10, 100),
    'Tekanan': np.random.normal(1013, 5, 100),
    'Curah Hujan': np.random.normal(200, 50, 100),
    'Angin': np.random.normal(15, 5, 100)
})

# Tambahkan korelasi buatan
data['Kelembaban'] = data['Curah Hujan'] * 0.3 + np.random.normal(0, 5, 100)
data['Suhu'] = -data['Curah Hujan'] * 0.1 + 35 + np.random.normal(0, 3, 100)

# Hitung matriks korelasi
corr_matrix = data.corr()

# Membuat heatmap
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(
    corr_matrix,
    annot=True,           # Tampilkan angka korelasi
    fmt='.2f',            # Format 2 desimal
    cmap='RdYlBu_r',     # Color map (Red-Yellow-Blue reversed)
    center=0,             # Pusat warna di 0
    square=True,          # Bentuk kotak persegi
    linewidths=1,         # Garis antar sel
    cbar_kws={'label': 'Koefisien Korelasi'},
    ax=ax
)

ax.set_title('Matriks Korelasi Variabel Cuaca', fontsize=16, fontweight='bold')
plt.tight_layout()
plt.savefig('heatmap.png', dpi=150, bbox_inches='tight')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Dataset: Hubungan pendapatan dan pengeluaran
np.random.seed(42)
n = 200
df = pd.DataFrame({
    'Pendapatan (Juta)': np.concatenate([
        np.random.normal(5, 1.5, 50),   # Kelompok rendah
        np.random.normal(10, 2, 80),    # Kelompok menengah
        np.random.normal(20, 3, 70)     # Kelompok tinggi
    ]),
    'Pengeluaran (Juta)': np.concatenate([
        np.random.normal(4, 1, 50),
        np.random.normal(7, 1.5, 80),
        np.random.normal(13, 2.5, 70)
    ]),
    'Kategori': ['Rendah']*50 + ['Menengah']*80 + ['Tinggi']*70
})

# Scatter plot dengan hue (warna berdasarkan kategori)
fig, ax = plt.subplots(figsize=(10, 8))
sns.scatterplot(
    data=df,
    x='Pendapatan (Juta)',
    y='Pengeluaran (Juta)',
    hue='Kategori',
    palette='viridis',
    s=80,
    alpha=0.7,
    ax=ax
)

# Tambahkan regresi line
sns.regplot(
    data=df,
    x='Pendapatan (Juta)',
    y='Pengeluaran (Juta)',
    scatter=False,
    color='red',
    line_kws={'linestyle': '--', 'linewidth': 2},
    ax=ax
)

ax.set_title('Hubungan Pendapatan vs Pengeluaran', fontsize=16, fontweight='bold')
ax.legend(title='Kelompok', fontsize=11)
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('scatter_seaborn.png', dpi=150, bbox_inches='tight')
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Data distribusi nilai ujian
np.random.seed(42)
nilai_a = np.random.normal(75, 10, 200)  # Kelas A
nilai_b = np.random.normal(68, 12, 200)  # Kelas B
nilai_c = np.random.normal(82, 8, 200)   # Kelas C

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

# Histogram + KDE
sns.histplot(nilai_a, kde=True, color='#3498db', label='Kelas A', alpha=0.5, ax=axes[0])
sns.histplot(nilai_b, kde=True, color='#e74c3c', label='Kelas B', alpha=0.5, ax=axes[0])
sns.histplot(nilai_c, kde=True, color='#2ecc71', label='Kelas C', alpha=0.5, ax=axes[0])
axes[0].set_title('Distribusi Nilai per Kelas')
axes[0].legend()

# Boxplot perbandingan
import pandas as pd
df_nilai = pd.DataFrame({
    'Nilai': np.concatenate([nilai_a, nilai_b, nilai_c]),
    'Kelas': ['A']*200 + ['B']*200 + ['C']*200
})
sns.boxplot(data=df_nilai, x='Kelas', y='Nilai', palette='Set2', ax=axes[1])
axes[1].set_title('Boxplot Nilai per Kelas')

plt.tight_layout()
plt.savefig('distribution.png', dpi=150, bbox_inches='tight')
plt.show()

import plotly.graph_objects as go
import pandas as pd

# Data saham fiksi
dates = pd.date_range('2026-01-01', periods=180, freq='D')
import numpy as np
np.random.seed(42)
harga = 100 + np.cumsum(np.random.randn(180) * 2)

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=dates,
    y=harga,
    mode='lines',
    name='Harga Saham',
    line=dict(color='#3498db', width=2),
    fill='tozeroy',
    fillcolor='rgba(52, 152, 219, 0.1)'
))

fig.update_layout(
    title='Harga Saham Fiksi (6 Bulan)',
    xaxis_title='Tanggal',
    yaxis_title='Harga (Rp)',
    template='plotly_dark',
    hovermode='x unified',
    height=500
)

fig.show()  # Buka di browser
# fig.write_html('stock_chart.html')  # Simpan sebagai HTML

import plotly.graph_objects as go

# Data perbandingan framework Python
frameworks = ['FastAPI', 'Flask', 'Django', 'Tornado', 'Sanic']
stars = [78, 68, 79, 24, 7]
contributors = [600, 650, 2200, 400, 120]

fig = go.Figure()

fig.add_trace(go.Bar(
    name='GitHub Stars (K)',
    x=frameworks,
    y=stars,
    marker_color='#3498db',
    text=stars,
    textposition='outside'
))

fig.add_trace(go.Bar(
    name='Contributors',
    x=frameworks,
    y=contributors,
    marker_color='#e74c3c',
    text=contributors,
    textposition='outside'
))

fig.update_layout(
    title='Perbandingan Framework Python Populer',
    barmode='group',
    template='plotly_dark',
    height=500,
    legend=dict(orientation='h', y=1.12)
)

fig.show()

import plotly.graph_objects as go
import numpy as np
import pandas as pd

# Data penjualan per hari per jam
hari = ['Senin', 'Selasa', 'Rabu', 'Kamis', 'Jumat', 'Sabtu', 'Minggu']
jam = [f'{h}:00' for h in range(8, 22)]  # 08:00 - 21:00

np.random.seed(42)
# Simulasi: lebih ramai jam makan siang dan weekend
data = np.random.randint(10, 100, size=(7, 14))
data[:, 4:7] += 30   # Jam makan siang (12-15)
data[:, 11:13] += 20  # Jam makan malam (19-21)
data[5:, :] += 15     # Weekend lebih ramai

fig = go.Figure(data=go.Heatmap(
    z=data,
    x=jam,
    y=hari,
    colorscale='YlOrRd',
    text=data,
    texttemplate='%{text}',
    textfont={'size': 10},
    colorbar=dict(title='Jumlah Transaksi')
))

fig.update_layout(
    title='Heatmap Transaksi Restoran per Hari & Jam',
    xaxis_title='Jam',
    yaxis_title='Hari',
    template='plotly_dark',
    height=400
)

fig.show()

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
import pandas as pd

np.random.seed(42)

# Membuat subplot 2x2
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=(
        'Tren Penjualan',
        'Distribusi Kategori',
        'Top 5 Produk',
        'Target vs Aktual'
    ),
    specs=[
        [{"type": "scatter"}, {"type": "pie"}],
        [{"type": "bar"}, {"type": "bar"}]
    ]
)

# Plot 1: Line chart (tren)
bulan = ['Jan', 'Feb', 'Mar', 'Apr', 'Mei', 'Jun']
penjualan = [45, 52, 48, 61, 55, 67]
fig.add_trace(
    go.Scatter(x=bulan, y=penjualan, mode='lines+markers',
               name='Penjualan', line=dict(color='#3498db')),
    row=1, col=1
)

# Plot 2: Pie chart (kategori)
kategori = ['Elektronik', 'Fashion', 'Makanan', 'Lainnya']
nilai = [35, 25, 22, 18]
fig.add_trace(
    go.Pie(labels=kategori, values=nilai,
           marker=dict(colors=['#3498db','#e74c3c','#2ecc71','#f1c40f'])),
    row=1, col=2
)

# Plot 3: Bar chart (top produk)
produk = ['Laptop', 'HP', 'Tablet', 'TV', 'Headset']
jml = [120, 95, 78, 65, 52]
fig.add_trace(
    go.Bar(x=produk, y=jml, name='Terjual',
           marker_color='#2ecc71'),
    row=2, col=1
)

# Plot 4: Target vs Aktual
target = [50, 55, 60, 65, 70, 75]
aktual = [45, 52, 58, 61, 68, 72]
fig.add_trace(
    go.Bar(x=bulan, y=target, name='Target',
           marker_color='#bdc3c7'),
    row=2, col=2
)
fig.add_trace(
    go.Bar(x=bulan, y=aktual, name='Aktual',
           marker_color='#e74c3c'),
    row=2, col=2
)

# Update layout
fig.update_layout(
    title_text='Dashboard Penjualan Q1-Q2 2026',
    template='plotly_dark',
    height=700,
    showlegend=True
)

fig.show()
# fig.write_html('dashboard.html')

# pip install dash

import dash
from dash import dcc, html
import plotly.graph_objects as go

app = dash.Dash(__name__)

app.layout = html.Div([
    html.H1('Dashboard Penjualan', style={'textAlign': 'center'}),

    # Dropdown filter
    html.Label('Pilih Periode:'),
    dcc.Dropdown(
        id='periode-dropdown',
        options=[
            {'label': 'Q1 2026', 'value': 'q1'},
            {'label': 'Q2 2026', 'value': 'q2'},
        ],
        value='q1'
    ),

    # Grafik
    dcc.Graph(id='grafik-penjualan'),

    # Statistik cards
    html.Div([
        html.Div([html.H3('Rp 500Jt'), html.P('Total Penjualan')],
                 className='stat-card'),
        html.Div([html.H3('1,250'), html.P('Total Transaksi')],
                 className='stat-card'),
        html.Div([html.H3('Rp 400K'), html.P('Rata-rata per Transaksi')],
                 className='stat-card'),
    ], style={'display': 'flex', 'gap': '20px'})
])

if __name__ == '__main__':
    app.run(debug=True)
    # Buka http://localhost:8050

import matplotlib.pyplot as plt

# Definisikan color palette konsisten
COLORS = {
    'primary': '#3498db',
    'secondary': '#2ecc71',
    'danger': '#e74c3c',
    'warning': '#f1c40f',
    'info': '#9b59b6',
    'dark': '#2c3e50',
    'light': '#ecf0f1'
}

# Custom style sheet
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'figure.dpi': 100,
    'axes.titlesize': 16,
    'axes.titleweight': 'bold',
    'axes.labelsize': 12,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'axes.grid': True,
    'grid.alpha': 0.3,
    'font.family': 'sans-serif',
    'axes.spines.top': False,    # Hilangkan border atas
    'axes.spines.right': False,  # Hilangkan border kanan
})