{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bab 2: Package & Komputasi Numerik (NumPy, Pandas, Visualisasi)\n", "\n", "**Awd Course - Buku Modul Praktik Komprehensif**\n", "\n", "Notebook kompilasi lengkap untuk Bab 2 yang mencakup Manajemen Paket PIP, Komputasi Vektor NumPy, Manipulasi DataFrame Pandas, Visualisasi Data Matplotlib, Visualisasi Seaborn, serta Impor dan Ekspor Berkas Data.\n", "\n", "---\n" ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Manajemen Paket & Library Menggunakan Pip\n", "\n", "**Pip** (*Package Installer for Python*) adalah manajer paket standar resmi untuk bahasa pemrograman Python. Pip mempermudah proses pencarian, instalasi, pembaruan, dan penghapusan pustaka dari repositori publik **PyPI** (*Python Package Index*)." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Memeriksa Versi Pip dan Daftar Paket\n", "Gunakan perintah `!pip --version` dan `!pip list` di lingkungan notebook." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "! pip --version\n", "! pip list | head -n 10" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Menginstal dan Memeriksa Informasi Paket\n", "Anda dapat menginstal pustaka seperti `requests` menggunakan perintah `pip install `." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "! pip install requests" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Menggunakan Paket yang Telah Terinstal" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import requests\n", "\n", "# Mengambil status API publik\n", "response = requests.get('https://api.github.com')\n", "print(\"Status Code:\", response.status_code)\n", "print(\"Server Headers:\", response.headers.get('server'))" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n" ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pengenalan & Komputasi Array NumPy\n", "\n", "**NumPy** (*Numerical Python*) adalah pustaka dasar untuk komputasi ilmiah dalam Python. NumPy menyediakan struktur data **ndarray** (*N-dimensional array*) yang jauh lebih cepat dan hemat memori dibandingkan list bawaan Python." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Inisiasi dan Membuat Array NumPy" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import numpy as np\n", "\n", "# Membuat Array 1 Dimensi (Vektor)\n", "arr_1d = np.array([10, 20, 30, 40, 50])\n", "print(\"Array 1D:\", arr_1d)\n", "print(\"Bentuk (shape):\", arr_1d.shape)\n", "print(\"Tipe data (dtype):\", arr_1d.dtype)\n", "\n", "# Membuat Array 2 Dimensi (Matriks)\n", "matrix_2d = np.array([\n", " [1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]\n", "])\n", "print(\"\n", "Matriks 2D:\n", "\", matrix_2d)\n", "print(\"Dimensi (ndim):\", matrix_2d.ndim)\n", "print(\"Ukuran (size):\", matrix_2d.size)" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Fungsi Pembuat Array Otomatis\n", "NumPy menyediakan fungsi instan untuk membuat array bernilai nol, satu, deret, atau nilai acak." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "# Array berisi nol (zeros)\n", "zeros = np.zeros((2, 4))\n", "\n", "# Array berisi deret angka urut (arange)\n", "deret = np.arange(0, 20, 2) # start=0, stop=20, step=2\n", "\n", "# Array linear space (linspace)\n", "lin = np.linspace(0, 1, 5) # 5 titik berjarak sama dari 0 s.d. 1\n", "\n", "print(\"Zeros (2x4):\n", "\", zeros)\n", "print(\"Arange (kelipatan 2):\", deret)\n", "print(\"Linspace (0 s.d. 1):\", lin)" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Operasi Matematika Vektorisasi (Vectorization)\n", "Salah satu keunggulan terbesar NumPy adalah operasi aritmatika dieksekusi secara instan per elemen (*element-wise*) tanpa perlu menuliskan perulangan `for`." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "a = np.array([1, 2, 3, 4])\n", "b = np.array([10, 20, 30, 40])\n", "\n", "print(\"Penjumlahan (a + b) =\", a + b)\n", "print(\"Perkalian (a * 3) =\", a * 3)\n", "print(\"Kuadrat (a ** 2) =\", a ** 2)\n", "print(\"Sinus np.sin(a) =\", np.round(np.sin(a), 4))" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Fungsi Agregasi Statistik NumPy" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "data_sampel = np.array([45, 82, 67, 90, 54, 78, 88, 95, 72, 84])\n", "\n", "print(\"Rata-rata (Mean) :\", np.mean(data_sampel))\n", "print(\"Median (Nilai Tengah) :\", np.median(data_sampel))\n", "print(\"Standar Deviasi (Std) :\", np.std(data_sampel))\n", "print(\"Varians (Variance) :\", np.var(data_sampel))\n", "print(\"Nilai Minimum :\", np.min(data_sampel))\n", "print(\"Nilai Maksimum :\", np.max(data_sampel))" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n" ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pengenalan Struktur Data Pandas (Series & DataFrame)\n", "\n", "**Pandas** adalah pustaka paling populer dalam Python untuk manipulasi, pembersihan, dan analisis data terstruktur." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Pandas Series (Struktur 1 Dimensi Berlabel)\n", "Series adalah array satu dimensi yang dilengkapi dengan label indeks." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import pandas as pd\n", "\n", "# Membuat Series dengan indeks kustom\n", "populasi_juta = pd.Series([10.56, 2.93, 2.51, 1.83], index=['Jakarta', 'Surabaya', 'Bekasi', 'Bandung'])\n", "populasi_juta.name = \"Populasi (Juta Jiwa)\"\n", "\n", "print(populasi_juta)\n", "print(\"\n", "Populasi Surabaya:\", populasi_juta['Surabaya'], \"juta\")" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Pandas DataFrame (Struktur Tabel 2 Dimensi)\n", "DataFrame adalah representasi tabel dua dimensi yang terdiri dari baris (*rows*) dan kolom (*columns*)." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "# Membuat DataFrame dari Dictionary\n", "data_karyawan = {\n", " 'Nama': ['Andi', 'Budi', 'Citra', 'Dewi', 'Eko'],\n", " 'Divisi': ['Engineering', 'Data', 'Data', 'Design', 'Engineering'],\n", " 'Gaji_Juta': [15.5, 18.0, 16.5, 12.0, 14.0],\n", " 'Pengalaman_Tahun': [3, 5, 4, 2, 3]\n", "}\n", "\n", "df = pd.DataFrame(data_karyawan)\n", "print(\"DataFrame Karyawan:\n", "\", df)" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Inspeksi Cepat & Ringkasan DataFrame" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "# Mengambil kolom tertentu\n", "print(\"Daftar Gaji Karyawan:\n", "\", df['Gaji_Juta'])\n", "\n", "# Ringkasan statistik cepat\n", "print(\"\n", "Statistik Gaji:\n", "\", df['Gaji_Juta'].describe())\n", "\n", "# Rata-rata gaji per divisi\n", "print(\"\n", "Rata-rata Gaji per Divisi:\n", "\", df.groupby('Divisi')['Gaji_Juta'].mean())" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n" ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisasi Data Dasar dengan Matplotlib\n", "\n", "**Matplotlib** adalah pustaka visualisasi data tingkat dasar yang memberikan kontrol penuh atas setiap elemen grafik (garis, sumbu, label, warna, dan legenda)." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Inisiasi & Membuat Line Plot (Tren Waktu)\n", "Line Plot sangat ideal untuk menampilkan tren data deret waktu (*time series*)." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Data tren penjualan bulanan\n", "bulan = ['Jan', 'Feb', 'Mar', 'Apr', 'Mei', 'Jun', 'Jul', 'Agu']\n", "penjualan_2025 = [120, 135, 148, 160, 175, 190, 210, 230]\n", "penjualan_2026 = [140, 150, 170, 185, 205, 220, 245, 270]\n", "\n", "plt.figure(figsize=(10, 5))\n", "plt.plot(bulan, penjualan_2025, marker='o', linestyle='--', color='#2563eb', label='Tahun 2025')\n", "plt.plot(bulan, penjualan_2026, marker='s', linestyle='-', color='#059669', label='Tahun 2026')\n", "\n", "plt.title('Perbandingan Tren Penjualan Bulanan (2025 vs 2026)', fontsize=14, fontweight='bold')\n", "plt.xlabel('Bulan', fontsize=11)\n", "plt.ylabel('Total Penjualan (Unit)', fontsize=11)\n", "plt.legend()\n", "plt.grid(True, linestyle=':', alpha=0.6)\n", "plt.tight_layout()\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Bar Plot (Perbandingan Antar Kategori)\n", "Bar Plot digunakan untuk membandingkan besaran nilai diskrit antar kategori." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "kategori_kursus = ['Python', 'SQL', 'Data Science', 'Web Dev', 'Cloud']\n", "jumlah_peserta = [450, 380, 520, 290, 310]\n", "\n", "plt.figure(figsize=(9, 5))\n", "bars = plt.bar(kategori_kursus, jumlah_peserta, color='#3b82f6', edgecolor='#1e40af')\n", "\n", "# Menambahkan label angka di atas setiap batang\n", "for bar in bars:\n", " yval = bar.get_height()\n", " plt.text(bar.get_x() + bar.get_width()/2, yval + 8, f\"{int(yval)}\", ha='center', fontweight='bold')\n", "\n", "plt.title('Jumlah Peserta Berdasarkan Kategori Kursus', fontsize=13, fontweight='bold')\n", "plt.xlabel('Kategori Kursus')\n", "plt.ylabel('Jumlah Siswa Terdaftar')\n", "plt.ylim(0, 600)\n", "plt.grid(axis='y', linestyle=':', alpha=0.7)\n", "plt.tight_layout()\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Histogram & Scatter Plot\n", "- **Histogram**: Melihat sebaran frekuensi data numerik kontinu.\n", "- **Scatter Plot**: Memeriksa korelasi / hubungan antara dua variabel kuantitatif." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import numpy as np\n", "\n", "# 1. Histogram Nilai Ujian\n", "np.random.seed(42)\n", "nilai_mahasiswa = np.random.normal(loc=78, scale=8, size=200)\n", "\n", "plt.figure(figsize=(10, 4))\n", "plt.hist(nilai_mahasiswa, bins=15, color='#8b5cf6', edgecolor='white')\n", "plt.title('Distribusi Sebaran Nilai Mahasiswa', fontsize=13, fontweight='bold')\n", "plt.xlabel('Rentang Nilai')\n", "plt.ylabel('Frekuensi')\n", "plt.grid(axis='y', linestyle=':', alpha=0.6)\n", "plt.show()\n", "\n", "# 2. Scatter Plot: Jam Belajar vs Nilai Ujian\n", "jam_belajar = np.random.uniform(2, 10, size=50)\n", "nilai_ujian = 50 + (jam_belajar * 4.5) + np.random.normal(0, 3, size=50)\n", "\n", "plt.figure(figsize=(9, 5))\n", "plt.scatter(jam_belajar, nilai_ujian, color='#ef4444', alpha=0.8, edgecolors='black')\n", "plt.title('Hubungan Waktu Belajar terhadap Nilai Ujian', fontsize=13, fontweight='bold')\n", "plt.xlabel('Waktu Belajar (Jam / Minggu)')\n", "plt.ylabel('Nilai Ujian Akhir')\n", "plt.grid(True, linestyle=':', alpha=0.6)\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n" ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisasi Data Statistik dengan Seaborn\n", "\n", "**Seaborn** adalah pustaka visualisasi tingkat tinggi yang dibangun di atas Matplotlib. Seaborn dirancang untuk menghasilkan grafik statistik yang indah dan informatif secara otomatis." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Inisiasi & Menyiapkan Dataset Sampel" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Mengatur tema visual modern bawaan seaborn\n", "sns.set_theme(style=\"whitegrid\")\n", "\n", "# Membuat dataset transaksi e-commerce\n", "data_transaksi = pd.DataFrame({\n", " 'Kategori': ['Elektronik', 'Pakaian', 'Elektronik', 'Buku', 'Pakaian', 'Elektronik', 'Buku', 'Pakaian', 'Elektronik', 'Buku'] * 10,\n", " 'Rating': [4.5, 4.2, 4.8, 3.9, 4.1, 4.6, 4.0, 3.8, 4.9, 4.3] * 10,\n", " 'Harga_Ribu': [1200, 250, 1800, 95, 320, 2100, 120, 180, 1500, 85] * 10,\n", " 'Metode_Bayar': ['QRIS', 'Transfer', 'Kartu Kredit', 'QRIS', 'QRIS', 'Kartu Kredit', 'Transfer', 'QRIS', 'Kartu Kredit', 'Transfer'] * 10\n", "})\n", "\n", "data_transaksi.head()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Barplot dengan Kategorisasi Warna (`hue`)" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "plt.figure(figsize=(10, 5))\n", "sns.barplot(data=data_transaksi, x='Kategori', y='Harga_Ribu', hue='Metode_Bayar', palette='Set2')\n", "plt.title('Rata-rata Nilai Transaksi per Kategori & Metode Pembayaran', fontsize=13, fontweight='bold')\n", "plt.ylabel('Rata-rata Harga (Ribu Rp)')\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Histogram Distribusi dengan Kurva KDE (`sns.histplot`)\n", "KDE (*Kernel Density Estimate*) memperlihatkan perkiraan kurva kepadatan probabilitas kontinu." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "plt.figure(figsize=(9, 5))\n", "sns.histplot(data=data_transaksi, x='Rating', kde=True, color='#0284c7', bins=10)\n", "plt.title('Sebaran Distribusi Rating Produk dengan Kurva KDE', fontsize=13, fontweight='bold')\n", "plt.xlabel('Rating Produk (Skala 1 - 5)')\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Scatter Plot & Boxplot Statistik Multi-Dimensi" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "plt.figure(figsize=(9, 5))\n", "sns.boxplot(data=data_transaksi, x='Kategori', y='Harga_Ribu', palette='pastel')\n", "plt.title('Distribusi Variasi Harga per Kategori Produk (Boxplot)', fontsize=13, fontweight='bold')\n", "plt.ylabel('Harga Produk (Ribu Rp)')\n", "plt.show()" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n" ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Teknik Impor & Ekspor Data\n", "\n", "Dalam pekerjaan sains data nyata, data hampir selalu dimuat dari sumber file eksternal seperti CSV, Excel (.xlsx), atau format JSON." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Membaca & Menulis File CSV (`pd.read_csv` & `df.to_csv`)\n", "CSV (*Comma Separated Values*) adalah format penyimpanan data tabular yang paling universal." ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "import pandas as pd\n", "\n", "# Membuat data sampel\n", "df_sampel = pd.DataFrame({\n", " 'Kode': ['A01', 'A02', 'A03', 'A04'],\n", " 'Produk': ['Laptop Pro', 'Mouse Wireless', 'Keyboard Mekanikal', 'Monitor 4K'],\n", " 'Harga': [14500000, 250000, 850000, 4200000],\n", " 'Stok': [12, 45, 28, 15]\n", "})\n", "\n", "# Ekspor ke file CSV lokal\n", "df_sampel.to_csv('daftar_produk.csv', index=False)\n", "print(\"File CSV berhasil disimpan!\")\n", "\n", "# Membaca kembali file CSV\n", "df_baca = pd.read_csv('daftar_produk.csv')\n", "print(\"\n", "Hasil Baca File CSV:\n", "\", df_baca)" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Membaca & Menulis Format JSON (`pd.read_json` & `df.to_json`)" ], "outputs": [] }, { "cell_type": "code", "metadata": {}, "source": [ "# Ekspor ke file JSON\n", "df_sampel.to_json('daftar_produk.json', orient='records', indent=2)\n", "\n", "# Membaca kembali dari JSON\n", "df_json = pd.read_json('daftar_produk.json')\n", "print(\"Hasil Baca JSON:\n", "\", df_json)" ], "outputs": [], "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Tips Penanganan Delimiter dan Encoding\n", "- Gunakan parameter `delimiter=';'` atau `sep='\\t'` jika file menggunakan pemisah titik koma atau tab.\n", "- Gunakan `encoding='utf-8'` untuk mendukung karakter khusus atau non-ASCII." ], "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n" ], "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 2 }