physics/examples/Example 01 - Python Basics for Matlab Wizards.ipynb

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   "source": [
    "# Python Basics for Matlab Wizards\n",
    "Welcome to Python.  \n",
    "\n",
    "In this example, we will highlight some of the basics of programming in Python, along with some tips and tricks for those more familiar with Matlab"
   ]
  },
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   "source": [
    "## Some Basics"
   ]
  },
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     "timestamp": 1526410406395,
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    "id": "5-29lQE-LRyL",
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    {
     "data": {
      "text/plain": [
       "4"
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     "execution_count": 3,
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   "source": [
    "2+2"
   ]
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    "executionInfo": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variable assignment\n",
    "x = 4\n",
    "\n",
    "# Object type\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "y = 3.15\n",
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "data": {
      "text/plain": [
       "str"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "z = 'Python is great'\n",
    "type(z)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
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     "height": 53
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     "status": "ok",
     "timestamp": 1526410547087,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.15\n",
      "1.2698412698412698\n"
     ]
    }
   ],
   "source": [
    "# We can do some basic math\n",
    "print( x + y )\n",
    "print( x/y )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "orHJ8GIUGgGb",
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   },
   "source": [
    "## Beware of type conflicts"
   ]
  },
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0\n",
      "7.666666666666667\n",
      "7.666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Dividing two ints results in an int\n",
    "print( 21/3 ) # Two ints\n",
    "print( 23/3 ) # Two ints does not work in Python 2\n",
    "print( 23.0/3.0 ) # Two floats"
   ]
  },
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   "execution_count": 9,
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     "height": 172
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 253,
     "status": "error",
     "timestamp": 1526410645236,
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     "slide_type": "slide"
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   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-e74f1b1777f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# You can't concatenate strings and ints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "# You can't concatenate strings and ints\n",
    "print( z + x )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
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     "height": 35
    },
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    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1526410701697,
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    "outputId": "dfbee788-9165-4e46-91f0-35fe8e62d877",
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python is greatPython is greatPython is greatPython is great\n"
     ]
    }
   ],
   "source": [
    "# But you can multiply them\n",
    "print( z * x )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 252,
     "status": "ok",
     "timestamp": 1526409791955,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "-4d39NFwFJPV",
    "outputId": "b58e3816-1ccc-47cb-f0ba-3df955511e98",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python is great 4 you\n"
     ]
    }
   ],
   "source": [
    "# Convert an int into a string\n",
    "print( z + ' ' + str(x) + ' you' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1526409819207,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "74GTd4WnFJSk",
    "outputId": "1d4bb960-a9b2-4720-e9e1-e2de1ae972b4",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python is great 4 you\n"
     ]
    }
   ],
   "source": [
    "# A better way\n",
    "print( 'Python is great %s you' % x )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3HdSyw3bHmyG",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fun with Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
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    "colab_type": "code",
    "id": "XC1RvchhFJWj",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "my_list = [3, 4, 5, 9, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1526409932854,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "OPANp_LKHsVu",
    "outputId": "9f20e08a-2ea5-4d77-8e91-561beafc9b94",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The fist item\n",
    "my_list[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 246,
     "status": "ok",
     "timestamp": 1526409933231,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "rFUURuohHsZF",
    "outputId": "57139842-7937-43ca-935b-5e077782c9ea",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The last item\n",
    "my_list[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 295,
     "status": "ok",
     "timestamp": 1526409943594,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "ZTwGl-knHwHq",
    "outputId": "655ec89f-7653-4c01-a545-ec36331dbde4",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 9]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract a subset\n",
    "my_list[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "GW_TT32XIDS_",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 99, 12]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update a value\n",
    "my_list[3] = 99\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 296,
     "status": "ok",
     "timestamp": 1526410059203,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "1Dbw-ivOHsc8",
    "outputId": "8f47d250-f655-4c3e-da17-cdbea0f9365b",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4, 5, 99, 12]\n",
      "[66, 4, 5, 99, 12]\n",
      "[66, 4, 5, 99, 12]\n"
     ]
    }
   ],
   "source": [
    "# Warning, python variables are object references and not copies by default\n",
    "my_second_list = my_list\n",
    "print( my_second_list )\n",
    "\n",
    "my_second_list[0] = 66\n",
    "print( my_second_list )\n",
    "print( my_list )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 286,
     "status": "ok",
     "timestamp": 1526410151000,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "PRVQpgaJIWuW",
    "outputId": "12415207-fbd8-49ed-e9e4-90da49bfd189",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[66, 4, 5, 9, 12]\n",
      "[3, 4, 5, 9, 12]\n"
     ]
    }
   ],
   "source": [
    "# The correct way to copy a list, keeping the original intact\n",
    "my_list = [3, 4, 5, 9, 12]\n",
    "my_second_list = list(my_list) # You can also use copy.copy() or my_list[:]\n",
    "\n",
    "my_second_list[0] = 66\n",
    "print( my_second_list )\n",
    "print( my_list )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SoXxwrfKI69_",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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      "startup": false,
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     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1526410302154,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "nqwCadXuI8UQ",
    "outputId": "08ccb324-f672-4b03-f9ae-d11097a2d34d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'temperature': 21, 'salinity': 35, 'sensor': 'CTD 23'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict = {'temperature': 21, 'salinity':35, 'sensor':'CTD 23'}\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 445,
     "status": "ok",
     "timestamp": 1526410323174,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "aIOWIErZJOIl",
    "outputId": "51d438de-9c0c-41f4-cf74-ff4bb4d2994e",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CTD 23'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing a key/value pair\n",
    "my_dict['sensor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 246,
     "status": "ok",
     "timestamp": 1526410352697,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "f21eZxc4JYSI",
    "outputId": "ca3bfa57-091b-419a-be6b-4ac5dcb9b1d4",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['temperature', 'salinity', 'sensor'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of dictionary keys\n",
    "my_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W-zF-yXvW-1r",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Functions, Conditions and Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 317,
     "status": "ok",
     "timestamp": 1526481147400,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "WYpQMyh1W__m",
    "outputId": "f691d18c-3af9-41a5-c9d9-ef9fd4017986"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "Not a number\n"
     ]
    }
   ],
   "source": [
    "def my_func(number):\n",
    "  if type(number)==int:\n",
    "    for i in range(number):\n",
    "      print(i)\n",
    "  else:\n",
    "    print(\"Not a number\")\n",
    "\n",
    "my_func(4)\n",
    "my_func('hi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HGGLpgTuJqO3",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## NumPy\n",
    "NumPy is an essential library for working with scientific data.  It provides an array object that is very similar to Matlab's array functionality, allowing you to perform linear algebra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "RgbHEs9sMTye"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1526482261742,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "J5QovFhQMfPd",
    "outputId": "9d091a55-58bd-44b1-bc41-a8fffa4961f0",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Types\n",
    "xi = np.array([1, 2, 3], dtype=np.int) # integer\n",
    "print(xi)\n",
    "xi.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 260,
     "status": "ok",
     "timestamp": 1526482262053,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "03Y4phsoMfRm",
    "outputId": "9ce4f310-b984-458d-e429-17b4177a19c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xf = np.array([1, 2, 3], dtype=np.float) # float\n",
    "print(xf)\n",
    "xf.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 291,
     "status": "ok",
     "timestamp": 1526482263460,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "CB81YTcTbo4_",
    "outputId": "dc319fc0-178c-482d-8ff1-d84e51c31d32",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi+xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1526482324456,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "vj7-jFpDbwct",
    "outputId": "d8f1f6ff-1307-4ac5-f72d-318f7c1eb79b",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 9])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi*xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 321,
     "status": "ok",
     "timestamp": 1526482324832,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "HwZAlZe7MT1I",
    "outputId": "9b830b20-43c5-4a35-facb-ef927190168c",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.zeros(shape=(4,5))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 278,
     "status": "ok",
     "timestamp": 1526482325581,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "lUMc4VtAMT3Y",
    "outputId": "71ad260b-3df9-4621-fbb8-e76102178188",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 4., 4., 4., 4.],\n",
       "       [4., 4., 4., 4., 4.],\n",
       "       [4., 4., 4., 4., 4.],\n",
       "       [4., 4., 4., 4., 4.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x+4\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 303,
     "status": "ok",
     "timestamp": 1526484171205,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "rznlVs4qiX7N",
    "outputId": "91b6dc40-e342-4497-96e3-9567aafe5f65",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "3\n",
      "(4, 5)\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "print( xi.shape )\n",
    "print( xi.size )\n",
    "print( y.shape )\n",
    "print( y.size )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1526482326120,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "mSy2pWymMT5z",
    "outputId": "291bcae5-1722-4076-c1da-65ed49c85828",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.40046417, 0.26384536, 0.05804485, 0.52192865, 0.58204946],\n",
       "       [0.8499382 , 0.79909869, 0.84584817, 0.23736273, 0.7998842 ],\n",
       "       [0.5302527 , 0.1754574 , 0.75003223, 0.31655475, 0.73080199],\n",
       "       [0.15157821, 0.95278997, 0.38142229, 0.44818723, 0.64970919]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random numbers\n",
    "z = np.random.random(x.shape)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 339,
     "status": "ok",
     "timestamp": 1526482327174,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "r8XfChg2MfLr",
    "outputId": "fb86db7f-5cc5-4404-fbb1-8dcb0695abad",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5302527 , 0.75003223, 0.73080199],\n",
       "       [0.15157821, 0.38142229, 0.64970919]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing\n",
    "z[2:4, ::2] # Extract 2-4 on the first axis, stride of 2 on the second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 292,
     "status": "ok",
     "timestamp": 1526482327608,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "yCnDA7_9MT_c",
    "outputId": "42f28dee-c99f-4b7a-ac3e-165f8f27f5a3",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.82633249, 3.53213199, 2.50309907, 2.58368689])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregations\n",
    "z_sum = z.sum(axis=1)\n",
    "z_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1526482328089,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "fbQk-FXSMfJf",
    "outputId": "eef134cc-6198-4c12-96d9-73fe9fd58351",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.30532995, 14.12852795, 10.01239628, 10.33474754],\n",
       "       [ 7.30532995, 14.12852795, 10.01239628, 10.33474754],\n",
       "       [ 7.30532995, 14.12852795, 10.01239628, 10.33474754],\n",
       "       [ 7.30532995, 14.12852795, 10.01239628, 10.33474754],\n",
       "       [ 7.30532995, 14.12852795, 10.01239628, 10.33474754]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Broadcasting\n",
    "y.transpose() * z_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1526482328571,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "KVqJK5ItMfTz",
    "outputId": "caf157e5-6662-4785-b04a-db773dbe0912",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.96752791, -0.3806872 ,  0.59598647,  0.52941069])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Several universal functions are included (e.g. sin, cos, exp)\n",
    "np.sin(z_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rjT_Lud-JrK_",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SciPy\n",
    "The SciPy library includes a huge collection of mathematical algorithms, functions and constants to enable scientific computing.  It is build upon NumPy, so data is typically expected to be in a NumPy array format.  Some useful features include:\n",
    "* **constants** - Physical and mathematical constants\n",
    "* **fftpack** - Fast Fourier Transorms\n",
    "* **integrate** - Integration and ODE solvers\n",
    "* **interpolate** - Interpolation and smoothing\n",
    "* **io** - File Input and Output\n",
    "* **linalg** - Linear algebra\n",
    "* **signal** - Signal processing\n",
    "* **stats** - Statistics\n",
    "\n",
    "For more information, check out the [SciPy Tutorial](https://docs.scipy.org/doc/scipy/reference/tutorial/index.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Aah8lhgyJtu_",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas\n",
    "Pandas is great for working with spreadsheet like tables, like Excel or CSV files.  \n",
    "\n",
    "It is not great for multidimensional arrays, for that use Xarray.\n",
    "\n",
    "For this example, we'll grab a text data file from [NDBC 44025](http://www.ndbc.noaa.gov/station_history.php?station=44025)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "9SximVYnJtSK"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "f7DysKQEMfWS",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "url = 'http://www.ndbc.noaa.gov/view_text_file.php?filename=44025h2017.txt.gz&dir=data/historical/stdmet/'\n",
    "ndbc = pd.read_csv(url,sep='\\s+',skiprows=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 276,
     "status": "ok",
     "timestamp": 1526482729850,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "jJ3ZbPv5cUTi",
    "outputId": "71548843-f912-476c-cc76-a0e743449717",
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#YY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>hh</th>\n",
       "      <th>mm</th>\n",
       "      <th>WDIR</th>\n",
       "      <th>WSPD</th>\n",
       "      <th>GST</th>\n",
       "      <th>WVHT</th>\n",
       "      <th>DPD</th>\n",
       "      <th>APD</th>\n",
       "      <th>MWD</th>\n",
       "      <th>PRES</th>\n",
       "      <th>ATMP</th>\n",
       "      <th>WTMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>VIS</th>\n",
       "      <th>TIDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>50</td>\n",
       "      <td>207</td>\n",
       "      <td>15.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.95</td>\n",
       "      <td>6.67</td>\n",
       "      <td>5.78</td>\n",
       "      <td>223</td>\n",
       "      <td>1014.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>215</td>\n",
       "      <td>14.7</td>\n",
       "      <td>19.4</td>\n",
       "      <td>3.42</td>\n",
       "      <td>7.69</td>\n",
       "      <td>6.20</td>\n",
       "      <td>220</td>\n",
       "      <td>1013.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>217</td>\n",
       "      <td>14.4</td>\n",
       "      <td>18.3</td>\n",
       "      <td>3.47</td>\n",
       "      <td>7.69</td>\n",
       "      <td>6.11</td>\n",
       "      <td>225</td>\n",
       "      <td>1013.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>228</td>\n",
       "      <td>14.9</td>\n",
       "      <td>18.7</td>\n",
       "      <td>3.31</td>\n",
       "      <td>8.33</td>\n",
       "      <td>6.17</td>\n",
       "      <td>203</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>228</td>\n",
       "      <td>15.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>3.81</td>\n",
       "      <td>8.33</td>\n",
       "      <td>6.47</td>\n",
       "      <td>219</td>\n",
       "      <td>1012.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #YY  MM  DD  hh  mm  WDIR  WSPD   GST  WVHT   DPD   APD  MWD    PRES  \\\n",
       "0  2016  12  31  23  50   207  15.2  18.2  2.95  6.67  5.78  223  1014.3   \n",
       "1  2017   1   1   0  50   215  14.7  19.4  3.42  7.69  6.20  220  1013.9   \n",
       "2  2017   1   1   1  50   217  14.4  18.3  3.47  7.69  6.11  225  1013.3   \n",
       "3  2017   1   1   2  50   228  14.9  18.7  3.31  8.33  6.17  203  1013.0   \n",
       "4  2017   1   1   3  50   228  15.4  19.6  3.81  8.33  6.47  219  1012.7   \n",
       "\n",
       "   ATMP  WTMP   DEWP   VIS  TIDE  \n",
       "0   8.8   8.8  999.0  99.0  99.0  \n",
       "1   9.1   8.8  999.0  99.0  99.0  \n",
       "2   9.1   8.8  999.0  99.0  99.0  \n",
       "3   8.7   8.8  999.0  99.0  99.0  \n",
       "4   8.9   8.8  999.0  99.0  99.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndbc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 291,
     "status": "ok",
     "timestamp": 1526482825966,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "Fg55FJendrPW",
    "outputId": "4f87e848-69a0-4d22-dc55-3ce7652d35aa",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    207\n",
       "1    215\n",
       "2    217\n",
       "Name: WDIR, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndbc['WDIR'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 308,
     "status": "ok",
     "timestamp": 1526482870360,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "c-imuA6FdzM0",
    "outputId": "c18b4f67-f725-4895-b382-e3cca07c393c",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#YY     2016.999881\n",
       "MM         6.489590\n",
       "DD        15.862344\n",
       "hh        11.486377\n",
       "mm        49.999405\n",
       "WDIR     196.579179\n",
       "WSPD       6.805033\n",
       "GST        8.313195\n",
       "WVHT       1.378846\n",
       "DPD        7.616221\n",
       "APD        5.236626\n",
       "MWD      164.758953\n",
       "PRES    1016.071767\n",
       "ATMP      12.123629\n",
       "WTMP      13.502344\n",
       "DEWP     903.288043\n",
       "VIS       99.000000\n",
       "TIDE      99.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of each column\n",
    "ndbc.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 274,
     "status": "ok",
     "timestamp": 1526483077174,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "Iy6LC85OeJ84",
    "outputId": "fd469a23-d454-4c1b-b059-cd186fe53879",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MM\n",
       "1     8.140457\n",
       "2     7.449776\n",
       "3     8.694054\n",
       "4     5.682033\n",
       "5     6.117862\n",
       "6     5.843759\n",
       "7     4.857459\n",
       "8     5.061417\n",
       "9     6.074028\n",
       "10    6.949866\n",
       "11    7.986890\n",
       "12    8.059005\n",
       "Name: WSPD, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Monthly means\n",
    "ndbc['WSPD'].groupby(ndbc['MM']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 409,
     "status": "ok",
     "timestamp": 1526483077922,
     "user": {
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     },
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    },
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    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting is bulit into Pandas\n",
    "ndbc['WSPD'].groupby(ndbc['MM']).mean().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Create a datetime vector\n",
    "df = pd.DataFrame({'year': ndbc['#YY'], 'month': ndbc['MM'], 'day': ndbc['DD'], 'hour': ndbc['hh'], 'minute': ndbc['mm']})\n",
    "ndbc['datetime'] = pd.to_datetime(df)\n",
    "\n",
    "# Change the index values to the datetime vector\n",
    "ndbc = ndbc.set_index('datetime')\n",
    "\n",
    "# Plot!\n",
    "ndbc['ATMP'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#YY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>hh</th>\n",
       "      <th>mm</th>\n",
       "      <th>WDIR</th>\n",
       "      <th>WSPD</th>\n",
       "      <th>GST</th>\n",
       "      <th>WVHT</th>\n",
       "      <th>DPD</th>\n",
       "      <th>APD</th>\n",
       "      <th>MWD</th>\n",
       "      <th>PRES</th>\n",
       "      <th>ATMP</th>\n",
       "      <th>WTMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>VIS</th>\n",
       "      <th>TIDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8405.0</td>\n",
       "      <td>8405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.999881</td>\n",
       "      <td>6.489590</td>\n",
       "      <td>15.862344</td>\n",
       "      <td>11.486377</td>\n",
       "      <td>49.999405</td>\n",
       "      <td>196.579179</td>\n",
       "      <td>6.805033</td>\n",
       "      <td>8.313195</td>\n",
       "      <td>1.378846</td>\n",
       "      <td>7.616221</td>\n",
       "      <td>5.236626</td>\n",
       "      <td>164.758953</td>\n",
       "      <td>1016.071767</td>\n",
       "      <td>12.123629</td>\n",
       "      <td>13.502344</td>\n",
       "      <td>903.288043</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.010908</td>\n",
       "      <td>3.506544</td>\n",
       "      <td>8.663989</td>\n",
       "      <td>6.925693</td>\n",
       "      <td>0.054538</td>\n",
       "      <td>99.891368</td>\n",
       "      <td>3.513243</td>\n",
       "      <td>4.283560</td>\n",
       "      <td>1.982873</td>\n",
       "      <td>3.924234</td>\n",
       "      <td>2.112808</td>\n",
       "      <td>72.208377</td>\n",
       "      <td>8.328311</td>\n",
       "      <td>7.642789</td>\n",
       "      <td>6.218949</td>\n",
       "      <td>291.313517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>977.100000</td>\n",
       "      <td>-8.400000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>5.560000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>1011.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>1.170000</td>\n",
       "      <td>7.140000</td>\n",
       "      <td>4.970000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>1015.800000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.690000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>5.760000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>1021.200000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1041.500000</td>\n",
       "      <td>26.300000</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               #YY           MM           DD           hh           mm  \\\n",
       "count  8405.000000  8405.000000  8405.000000  8405.000000  8405.000000   \n",
       "mean   2016.999881     6.489590    15.862344    11.486377    49.999405   \n",
       "std       0.010908     3.506544     8.663989     6.925693     0.054538   \n",
       "min    2016.000000     1.000000     1.000000     0.000000    45.000000   \n",
       "25%    2017.000000     3.000000     8.000000     5.000000    50.000000   \n",
       "50%    2017.000000     6.000000    16.000000    11.000000    50.000000   \n",
       "75%    2017.000000    10.000000    23.000000    17.000000    50.000000   \n",
       "max    2017.000000    12.000000    31.000000    23.000000    50.000000   \n",
       "\n",
       "              WDIR         WSPD          GST         WVHT          DPD  \\\n",
       "count  8405.000000  8405.000000  8405.000000  8405.000000  8405.000000   \n",
       "mean    196.579179     6.805033     8.313195     1.378846     7.616221   \n",
       "std      99.891368     3.513243     4.283560     1.982873     3.924234   \n",
       "min       1.000000     0.000000     0.100000     0.200000     2.470000   \n",
       "25%     105.000000     4.200000     5.100000     0.830000     5.560000   \n",
       "50%     212.000000     6.300000     7.600000     1.170000     7.140000   \n",
       "75%     280.000000     9.100000    11.000000     1.690000     9.090000   \n",
       "max     360.000000    20.400000    27.000000    99.000000    99.000000   \n",
       "\n",
       "               APD          MWD         PRES         ATMP         WTMP  \\\n",
       "count  8405.000000  8405.000000  8405.000000  8405.000000  8405.000000   \n",
       "mean      5.236626   164.758953  1016.071767    12.123629    13.502344   \n",
       "std       2.112808    72.208377     8.328311     7.642789     6.218949   \n",
       "min       2.880000     1.000000   977.100000    -8.400000     4.300000   \n",
       "25%       4.400000   114.000000  1011.700000     6.500000     7.500000   \n",
       "50%       4.970000   154.000000  1015.800000    11.700000    13.000000   \n",
       "75%       5.760000   196.000000  1021.200000    19.400000    19.500000   \n",
       "max      99.000000   999.000000  1041.500000    26.300000    26.900000   \n",
       "\n",
       "              DEWP     VIS    TIDE  \n",
       "count  8405.000000  8405.0  8405.0  \n",
       "mean    903.288043    99.0    99.0  \n",
       "std     291.313517     0.0     0.0  \n",
       "min      -0.500000    99.0    99.0  \n",
       "25%     999.000000    99.0    99.0  \n",
       "50%     999.000000    99.0    99.0  \n",
       "75%     999.000000    99.0    99.0  \n",
       "max     999.000000    99.0    99.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndbc.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAqDEedZgGUU",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1526484684825,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "XR8QRziGfOjT",
    "outputId": "b561fbd3-9d03-4acc-f93f-04fde94a3ac0",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(0,10,100)\n",
    "\n",
    "plt.plot(t, np.cos(t)*np.sin(t), '-rs'); #Add a ; to the last line to supress output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 388,
     "status": "ok",
     "timestamp": 1526483864619,
     "user": {
      "displayName": "Sage Lichtenwalner",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "107661401208389246829"
     },
     "user_tz": 240
    },
    "id": "CJ93ezI5hJNB",
    "outputId": "70a1cc68-67a3-4dcb-8c03-4577f2bb7a54",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, sigma = 100, 15\n",
    "x = mu + sigma * np.random.randn(100)\n",
    "\n",
    "plt.plot(x,'rs')\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Histogram of IQ')\n",
    "plt.text(10, 135, r'$\\mu=100, \\ \\sigma=15$'); #LaTex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bLpTzP346rF-",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Resources\n",
    "\n",
    "* [NumPy for Matlab users](https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html) - Quick introduction from SciPy\n",
    "* [NumPy for Matlab users Cheatsheet](http://mathesaurus.sourceforge.net/matlab-numpy.html)\n",
    "* [MATLAB vs. Python: Top Reasons to Choose MATLAB](https://www.mathworks.com/products/matlab/matlab-vs-python.html)\n",
    "* [Python for Matlab Users](http://researchcomputing.github.io/meetup_fall_2014/pdfs/fall2014_meetup13_python_matlab.pdf) - Meetup presentation from CSU.  A great overview.  Many of the above examples come from this.\n",
    "* [Pyzo Python vs. Matlab](http://www.pyzo.org/python_vs_matlab.html)\n",
    "* [Pangeo Python Basics Tutorial](https://github.com/pangeo-data/pangeo-tutorial-sea-2018/blob/master/notebooks/1.0.scientific_python_ecosystem.ipynb)  - Many of the above examples come from this.\n",
    "* [Webinar: Python for MATLAB Users, What You Need to Know](https://www.youtube.com/watch?v=YkCegjtoHFQ)\n",
    "* [Python Graph Gallery](https://python-graph-gallery.com)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "colab": {
   "default_view": {},
   "name": "Example 1 - Python Basics for Matlab Wizards",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "OOI",
   "language": "python",
   "name": "ooi"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}