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   "source": [
    "### <center>San Jose State University<br>Department of Applied Data Science<br><br>**DATA 200<br>Computational Programming for Data Analytics**<br><br>Spring 2024<br>Instructor: Ron Mak</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e6a3af-456b-4286-8721-8e7cd2572aeb",
   "metadata": {},
   "source": [
    "# Reading, splitting, and combining `numpy` arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62f1a3b7-313d-43d0-8813-c06a23d0862c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef76e5b-aee6-45ae-95c9-2b8f8c5774e4",
   "metadata": {},
   "source": [
    "## Use the `genfromtxt()` function to read from a CSV file\n",
    "`data.csv`\n",
    "```\n",
    "1,2,3,4,5,6\n",
    "7,8,9,10,11,12\n",
    "13,14,15,16,17,18\n",
    "19,20,21,22,23,24\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d216af4-6a86-4f67-9143-248d76d37e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.genfromtxt('data.csv', delimiter=',')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7ca5d3-0340-4f82-9813-85b07a8f280d",
   "metadata": {},
   "source": [
    "## Split an array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db33c9c3-f3ad-46a0-9251-1a2a93eacbf8",
   "metadata": {},
   "source": [
    "### Split horizontally into subarrays with an equal number of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8bcf37e-5a66-4e26-b7fd-bd9e899c9fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.hsplit(dataset, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a4c340-d674-49d6-abe7-83b5828bd224",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.split(dataset, 3, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871912ba-2f26-4e13-b482-878b38c9b443",
   "metadata": {},
   "source": [
    "### Split vertically into subarrays with an equal number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f91b5c3c-c051-452b-b940-67fe9cdbaf13",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.vsplit(dataset, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3fd1717-27c7-4dba-9515-d2ebd1d788e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.split(dataset, 2, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85ef0ce-abd7-4b23-91cb-642d7ecb6139",
   "metadata": {},
   "source": [
    "### Split horizontally at given column indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd53da2-42b0-4834-944e-547e2a0e4348",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.hsplit(dataset, [1, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e624a337-bcc7-4bc9-a596-5d6096fad18e",
   "metadata": {},
   "source": [
    "### Split vertically at given row indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9976d860-afd0-4f30-8d42-9776c6515118",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.vsplit(dataset, [3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0866ca51-9b9f-41f0-8d23-c0c9c28154e6",
   "metadata": {},
   "source": [
    "## Combining arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76c1d43-2d85-4906-b6bb-cfc634e9ecf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_1 = np.array([[1, 2, 3, 4],\n",
    "                    [5, 6, 7, 8]])\n",
    "array_2 = np.array([[10, 20, 30, 40],\n",
    "                    [50, 60, 70, 80]])\n",
    "array_3 = np.array([100, 200, 300, 400])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b09ac0-7434-435b-8952-dfcd0d10d3d6",
   "metadata": {},
   "source": [
    "### Vertically stack arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bf464e3-0319-4da3-8fb1-b32b3f7b5188",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.vstack([array_1, array_2, array_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47920462-52e1-486a-82e0-16d3098f9bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.row_stack([array_1, array_2, array_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18275a9a-a011-4428-a926-3570e21c19c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_a = np.array([[1, 2, 3, 4],\n",
    "                    [5, 6, 7, 8]])\n",
    "array_b = np.array([[10, 20, 30],\n",
    "                    [50, 60, 70]])\n",
    "array_c = np.array([[100], \n",
    "                    [200]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48789d58-fdb7-4b3d-ac5b-4c924b52a81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.hstack([array_a, array_b, array_c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b469a8a-0781-4aac-8197-41d3a1df6e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.column_stack([array_a, array_b, array_c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8801d23-acec-43ae-a815-322bd334522b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (C) Copyright 2023 by Ronald Mak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16f4b6a5-3262-45b6-b8ea-ab6d0861f5c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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