Introduction to wind analysis - unit test notebook

intro_notebook.ipynb

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   "source": [
    "## First test\n",
    "This is a test notebook. This blog post assumes you can run the following code in your own notebook. The code was copied and pasted from [this 10-minute introduction to Pandas](https://pandas.pydata.org/pandas-docs/stable/10min.html). "
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   "cell_type": "code",
   "execution_count": 7,
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     "name": "stdout",
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     "text": [
      "Hello wind\n"
     ]
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   "source": [
    "print('Hello wind')"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
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   "execution_count": 11,
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     "data": {
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       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2013-01-01', periods=6)\n",
    "dates"
   ]
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   "execution_count": 13,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.529348</td>\n",
       "      <td>1.052895</td>\n",
       "      <td>0.640303</td>\n",
       "      <td>0.252844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.888459</td>\n",
       "      <td>0.795430</td>\n",
       "      <td>1.073112</td>\n",
       "      <td>-0.957200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.172106</td>\n",
       "      <td>0.040905</td>\n",
       "      <td>0.459054</td>\n",
       "      <td>-1.167859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.670537</td>\n",
       "      <td>-2.117747</td>\n",
       "      <td>1.267443</td>\n",
       "      <td>-0.292776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.798625</td>\n",
       "      <td>0.364038</td>\n",
       "      <td>1.731047</td>\n",
       "      <td>-0.228873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.395039</td>\n",
       "      <td>0.863305</td>\n",
       "      <td>-0.012052</td>\n",
       "      <td>-0.545045</td>\n",
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       "                   A         B         C         D\n",
       "2013-01-01 -0.529348  1.052895  0.640303  0.252844\n",
       "2013-01-02 -0.888459  0.795430  1.073112 -0.957200\n",
       "2013-01-03 -0.172106  0.040905  0.459054 -1.167859\n",
       "2013-01-04  0.670537 -2.117747  1.267443 -0.292776\n",
       "2013-01-05 -0.798625  0.364038  1.731047 -0.228873\n",
       "2013-01-06 -0.395039  0.863305 -0.012052 -0.545045"
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "df"
   ]
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