{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0kGpJIhciXH",
        "outputId": "e390920e-8238-4994-9a25-aea6bf8f9784"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.44444444 42.77777778 -1.          0.55555556 -0.77777778  9.44444444\n",
            " -0.33333333  0.33333333 -1.         20.55555556]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Sample data with outliers\n",
        "data = np.array([1, 200, 3, 10, 4, 50, 6, 9, 3, 100])\n",
        "\n",
        "# One-liner: Robust scaling using MAD\n",
        "scaled = (data - np.median(data)) / np.median(np.abs(data - np.median(data)))\n",
        "print(scaled)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample continuous data (e.g., customer ages)\n",
        "ages = np.array([18, 25, 35, 22, 45, 67, 23, 29, 34, 56, 41, 38, 52, 28, 33])\n",
        "\n",
        "# One-liner: Create 4 equal-frequency bins\n",
        "binned = np.digitize(ages, np.percentile(ages, [25, 50, 75])) - 1\n",
        "print(binned)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ654h7ec34I",
        "outputId": "144564d7-b48c-4bf2-c034-b131fbaeeefa"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1 -1  1 -1  2  2 -1  0  1  2  1  1  2  0  0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Original features (e.g., temperature, humidity)\n",
        "X = np.array([[20, 65], [25, 70], [30, 45], [22, 80]])\n",
        "\n",
        "# One-liner: Generate degree-2 polynomial features\n",
        "poly_features = np.column_stack([X[:, [i, j]].prod(axis=1) for i in range(X.shape[1]) for j in range(i, X.shape[1])])\n",
        "print(poly_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzwwApnQc9pV",
        "outputId": "11249482-96eb-47cc-db05-73bf1b620d27"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 400 1300 4225]\n",
            " [ 625 1750 4900]\n",
            " [ 900 1350 2025]\n",
            " [ 484 1760 6400]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Time series data (e.g., daily sales)\n",
        "sales = np.array([100, 98, 120,130, 74, 145, 110, 140, 65, 105, 135])\n",
        "\n",
        "lags = np.column_stack([np.roll(sales, shift) for shift in [1, 2, 3]])[3:]\n",
        "print(lags)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XTtCYpQdfML",
        "outputId": "9bbe8c29-c1e2-4cbb-be7d-00b77bfe89b7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[120  98 100]\n",
            " [130 120  98]\n",
            " [ 74 130 120]\n",
            " [145  74 130]\n",
            " [110 145  74]\n",
            " [140 110 145]\n",
            " [ 65 140 110]\n",
            " [105  65 140]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorical data (e.g., product categories)\n",
        "categories = np.array([0, 1, 2, 1, 0, 2, 3, 1])\n",
        "\n",
        "# One-liner: One-hot encode\n",
        "one_hot = (categories[:, None] == np.arange(categories.max() + 1)).astype(int)\n",
        "print(one_hot)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0HPVkzmdgvW",
        "outputId": "a7a1e5b3-9dac-46c3-90bb-14915619a3dd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 0 0 0]\n",
            " [0 1 0 0]\n",
            " [0 0 1 0]\n",
            " [0 1 0 0]\n",
            " [1 0 0 0]\n",
            " [0 0 1 0]\n",
            " [0 0 0 1]\n",
            " [0 1 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Coordinate data\n",
        "locations = np.array([[40.7128, -74.0060],\n",
        "                      [34.0522, -118.2437],\n",
        "                      [41.8781, -87.6298],\n",
        "                      [29.7604, -95.3698]])\n",
        "reference = np.array([39.7392, -104.9903])\n",
        "\n",
        "# One-liner: Calculate Euclidean distances from reference point\n",
        "distances = np.sqrt(((locations - reference) ** 2).sum(axis=1))\n",
        "print(distances)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0rszMzidyjs",
        "outputId": "8eb2ff56-f6a1-4b4a-b383-b59770133c49"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[30.99959263 14.42201722 17.4917653  13.86111358]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample features (e.g., price, quality, brand_score)\n",
        "features = np.array([[10, 8, 7], [15, 9, 6], [12, 7, 8], [20, 10, 9]])\n",
        "\n",
        "# One-liner: Create all pairwise interactions\n",
        "interactions = np.array([features[:, i] * features[:, j]\n",
        "                        for i in range(features.shape[1])\n",
        "                        for j in range(i+1, features.shape[1])]).T\n",
        "print(interactions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0o8FYjTZd7ey",
        "outputId": "cd8ce3c1-309b-4c75-d0b6-ac7f602d02fb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 80  70  56]\n",
            " [135  90  54]\n",
            " [ 84  96  56]\n",
            " [200 180  90]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Noisy signal data (e.g., stock prices, sensor readings)\n",
        "signal = np.array([10, 27, 12, 18, 11, 19, 20, 26, 12, 19, 25, 31, 28])\n",
        "window_size = 4\n",
        "\n",
        "# One-liner: Create rolling mean features\n",
        "rolling_mean = np.convolve(signal, np.ones(window_size)/window_size, mode='valid')\n",
        "print(rolling_mean)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amZgqlEZeAOI",
        "outputId": "fa48d13f-f98b-43b9-8dd0-a1a66c9c7557"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16.75 17.   15.   17.   19.   19.25 19.25 20.5  21.75 25.75]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data with potential outliers (e.g., transaction amounts)\n",
        "amounts = np.array([25, 30, 28, 32, 500, 29, 31, 27, 33, 26])\n",
        "\n",
        "# One-liner: Create outlier indicator features\n",
        "outlier_flags = ((amounts < np.percentile(amounts, 5)) |\n",
        "                 (amounts > np.percentile(amounts, 95))).astype(int)\n",
        "print(outlier_flags)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiHHeWgreq21",
        "outputId": "80db2904-677c-4eee-f6b1-2dedadabf2b5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 0 1 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorical data (e.g., product categories)\n",
        "categories = np.array(['Electronics', 'Books', 'Electronics', 'Clothing',\n",
        "                      'Books', 'Electronics', 'Home', 'Books'])\n",
        "\n",
        "# One-liner: Frequency encode\n",
        "unique_cats, counts = np.unique(categories, return_counts=True)\n",
        "freq_encoded = np.array([counts[np.where(unique_cats == cat)[0][0]] for cat in categories])\n",
        "print(freq_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nor6f_t-ezhk",
        "outputId": "1d142d36-f381-4f1a-eff8-162b6b2c5bd1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 3 3 1 3 3 1 3]\n"
          ]
        }
      ]
    }
  ]
}