numpy.ipynb

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   "source": [
    "import numpy as np"
   ]
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   "source": [
    "## Initialize numpy array\n"
   ]
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   "source": [
    "a_1d = np.zeros(4, dtype=int)\n",
    "print(f\"a_1d = \\n{a_1d}, \\na_1d shape = {a_1d.shape}\")\n",
    "a_2d = np.zeros((4, 4), dtype=int)\n",
    "print(f\"a_2d = \\n{a_2d}, \\na_2d shape = {a_2d.shape}\")"
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   "source": [
    "a = np.arange(10)\n",
    "print(a)"
   ]
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   "source": [
    "## np array boolean\n"
   ]
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    "# Calculate the BMI: bmi\n",
    "height_in = [74, 74, 72, 72, 73, 69, 69, 71, 76, 71]\n",
    "weight_lb = [180, 215, 210, 210, 188, 176, 209, 200, 231, 180]\n",
    "\n",
    "np_height_m = np.array(height_in) * 0.0254\n",
    "np_weight_kg = np.array(weight_lb) * 0.453592\n",
    "bmi = np_weight_kg / (np_height_m**2)\n",
    "\n",
    "# Create the light array (boolean array)\n",
    "light = bmi < 25\n",
    "print(light)\n",
    "# BMIs of all baseball players whose BMI is below 21\n",
    "print(bmi[light])"
   ]
  },
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   "source": [
    "my_house = np.array([18.0, 20.0, 10.75, 9.50])\n",
    "your_house = np.array([14.0, 24.0, 14.25, 9.0])\n",
    "\n",
    "# my_house greater than or equal to 18\n",
    "print(my_house >= 18)\n",
    "\n",
    "# my_house less than your_house\n",
    "print(my_house < your_house)"
   ]
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   "source": [
    "## Logical operators\n",
    "\n",
    "`np.logical_and()`\n",
    "\n",
    "`np.logical_or()`\n",
    "\n",
    "`np.logical_not()`\n"
   ]
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   "source": [
    "my_house = np.array([18.0, 20.0, 10.75, 9.50])\n",
    "your_house = np.array([14.0, 24.0, 14.25, 9.0])\n",
    "\n",
    "# my_house greater than 18.5 or smaller than 10\n",
    "print(np.logical_or(my_house > 18.5, my_house < 10))\n",
    "\n",
    "# Both my_house and your_house smaller than 11\n",
    "print(np.logical_and(my_house < 11, your_house < 11))"
   ]
  },
  {
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   "id": "48794a357b871e21",
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   "source": [
    "## Know the number of rows and cols of np array\n"
   ]
  },
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   "source": [
    "baseball = [[180, 78.4], [215, 102.7], [210, 98.5], [188, 75.2]]\n",
    "\n",
    "# Create a 2D numpy array from baseball: np_baseball\n",
    "np_baseball = np.array(baseball)\n",
    "\n",
    "# Print out the type of np_baseball\n",
    "print(type(np_baseball))\n",
    "\n",
    "# Print out the shape of np_baseball\n",
    "print(np_baseball.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d894fdc2f9569116",
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   "source": [
    "## 2D Arithmetic\n"
   ]
  },
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   "source": [
    "np_mat = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "\n",
    "# Create numpy array: conversion\n",
    "# Multiply the first col by 2 & the second col by 3\n",
    "conversion = np.array([2, 3])\n",
    "\n",
    "# Print out product of np_baseball and conversion\n",
    "print(np_mat * conversion)"
   ]
  },
  {
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   "id": "7bba23cb2479ceff",
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   "source": [
    "## Filtering np array\n"
   ]
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    "positions = [\"GK\", \"M\", \"A\", \"D\"]\n",
    "heights = [191, 184, 185, 180]\n",
    "# Convert positions and heights to numpy arrays: np_positions, np_heights\n",
    "np_positions = np.array(positions)\n",
    "np_heights = np.array(heights)\n",
    "\n",
    "# Heights of the goalkeepers: gk_heights\n",
    "gk_heights = np_heights[np_positions == \"GK\"]\n",
    "\n",
    "# Heights of the other players: other_heights\n",
    "other_heights = np_heights[np_positions != \"GK\"]\n",
    "\n",
    "# Print out the median height of goalkeepers\n",
    "print(\"Median height of goalkeepers: \" + str(np.median(gk_heights)))\n",
    "\n",
    "# Print out the median height of other players\n",
    "print(\"Median height of other players: \" + str(np.median(other_heights)))"
   ]
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