LABS/LAB 9. Numpy.ipynb

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      "<type 'numpy.ndarray'>\n"
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    "# Create list baseball \n",
    "baseball = [180, 215, 210, 210, 188, 176, 209, 200]\n",
    "\n",
    "# Import the numpy package as np\n",
    "import numpy as np\n",
    "\n",
    "# Create a Numpy array from baseball: np_baseball\n",
    "np_baseball = np.array(baseball)\n",
    "\n",
    "# Print out type of np_baseball\n",
    "print(type(np_baseball))"
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    "# height is available as a regular list\n",
    "\n",
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Create a Numpy array from height: np_height\n",
    "np_height = np.array(height)\n",
    "\n",
    "# Print out np_height\n",
    "print(np_height)\n",
    "\n",
    "# Convert np_height to m: np_height_m\n",
    "np_height_m = np_height * 0.0254\n",
    "\n",
    "# Print np_height_m\n",
    "print(np_height_m)"
   ]
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    "# height and weight are available as a regular lists\n",
    "\n",
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Create array from height with correct units: np_height_m\n",
    "np_height_m = np.array(height) * 0.0254\n",
    "\n",
    "# Create array from weight with correct units: np_weight_kg \n",
    "np_weight_kg = np.array(weight) * 0.453592\n",
    "\n",
    "# Calculate the BMI: bmi\n",
    "bmi = np_weight_kg / np_height_m **2\n",
    "\n",
    "# Print out bmi\n",
    "print(bmi)"
   ]
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    "# height and weight are available as a regular lists\n",
    "\n",
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Calculate the BMI: bmi\n",
    "np_height_m = np.array(height) * 0.0254\n",
    "np_weight_kg = np.array(weight) * 0.453592\n",
    "bmi = np_weight_kg / np_height_m ** 2\n",
    "\n",
    "# Create the light array\n",
    "light = bmi < 21\n",
    "\n",
    "# Print out light\n",
    "print(light)\n",
    "\n",
    "# Print out BMIs of all baseball players whose BMI is below 21\n",
    "print(bmi[light])"
   ]
  },
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   "source": [
    "1. As Filip explained before, Numpy is great to do vector arithmetic. If you compare its functionality with regular Python lists, however, some things have changed. First of all, Numpy arrays cannot contain elements with different types. If you try to build such a list, some of the elments' types are changed to end up with a homogenous list. This is known as type coercion. Second, the typical arithmetic operators, such as +, -, * and / have a different meaning for regular Python lists and Numpy arrays. Have a look at this line of code: np.array([True, 1, 2]) + np.array([3, 4, False]). Can you tell which code chunk builds the exact same Python data structure? The Numpy package is already imported as np, so you can start experimenting in the IPython Shell straight away!\n",
    "    - np.array([4, 3, 0]) + np.array([0, 2, 2])"
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   "source": [
    "# height and weight are available as a regular lists\n",
    "\n",
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Store weight and height lists as numpy arrays\n",
    "np_weight = np.array(weight)\n",
    "np_height = np.array(height)\n",
    "\n",
    "# Print out the weight at index 50\n",
    "print(np_weight[50])\n",
    "\n",
    "# Print out sub-array of np_height: index 100 up to and including index 110\n",
    "print(np_height[100:111])"
   ]
  }
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