diff --git a/Basic Python Packages for Science.ipynb b/Basic Python Packages for Science.ipynb
index a58eb60..ef7fdeb 100644
--- a/Basic Python Packages for Science.ipynb	
+++ b/Basic Python Packages for Science.ipynb	
@@ -193,7 +193,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1000 loops, best of 3: 1.62 ms per loop\n"
+      "100 loops, best of 3: 1.82 ms per loop\n"
      ]
     }
    ],
@@ -213,7 +213,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10000 loops, best of 3: 98.6 µs per loop\n"
+      "The slowest run took 14.38 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "10000 loops, best of 3: 95.7 µs per loop\n"
      ]
     }
    ],
@@ -546,6 +547,50 @@
     "chess_board"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f8bc9731c50>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPcAAAD7CAYAAAC2TgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8bd438fd68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(chess_board, cmap=plt.cm.gray)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -555,39 +600,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "x = np.linspace(0, 10)\n",
+    "x = np.linspace(1, 10)\n",
     "y = np.sin(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/miniconda/envs/pydata-basic-python/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
-      "  if __name__ == '__main__':\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8bc91e2f98>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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nnbGTJMuECbBokZ/TK1JsNPKXKpk+HY47zncR3HLL2GmS4ec/h8aNfeQvElN1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\nkRKk4i8iUoJU/EVESpCKv4hICfr/b1/w2IUFqTEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8bc9203f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
+   "source": [
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "y_2 = (1 + np.log(x)) ** 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8bc914ca90>]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0EZEMoYQuIpIhGpXQzSzPzBab2cdmNipZQYmISMMlnNDNrAUwDQgARwC/N7O+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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8bc9180be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y_2, 'r-*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -600,7 +700,7 @@
        "       [33, 16, 91]])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -628,7 +728,7 @@
        "       [10497,  9813, 11910]])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -650,7 +750,7 @@
        "array([ 240.4,  250.8,  783.1])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -663,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -676,7 +776,7 @@
        "       [-0.03598099, -0.05634759,  0.03394433]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -687,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -701,7 +801,7 @@
        "        [-0.92335283,  0.1138173 ,  0.60198985]]))"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -714,36 +814,574 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3. Graphical Representation: matplotlib"
+    "#### Air quality data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"http://www.mambiente.munimadrid.es/sica/scripts/index.php\"             width=\"700\" height=\"400\"></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<iframe src=\"http://www.mambiente.munimadrid.es/sica/scripts/index.php\" \\\n",
+    "            width=\"700\" height=\"400\"></iframe>')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![matplotlib](./static/matplotlib.png)"
+    "##### Loading the data "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estación: Barrio del Pilar;;;;\r\n",
+      "Fecha;Hora;CO;NO2;O3\r\n",
+      ";;mg/m³;µg/m³;µg/m³\r\n",
+      "22/03/2016;01:00;0.2;14;73\r\n",
+      "22/03/2016;02:00;0.2;10;77\r\n",
+      "22/03/2016;03:00;0.2;9;75\r\n",
+      "22/03/2016;04:00;0.2;3;81\r\n",
+      "22/03/2016;05:00;0.2;3;81\r\n",
+      "22/03/2016;06:00;0.2;6;79\r\n",
+      "22/03/2016;07:00;0.2;24;59\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Linux command \n",
+    "!head ./data/barrio_del_pilar-20160322.csv\n",
+    "\n",
+    "# Windows\n",
+    "# !gc log.txt | select -first 10 # head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "       [   0.3,   42. ,   46. ]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1 = np.genfromtxt('./data/barrio_del_pilar-20160322.csv', skip_header=3, delimiter=';', usecols=(2,3,4))\n",
+    "data1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Dealing with missing values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ nan,  nan,  nan])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(data1, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.33717277,  29.79581152,  55.47643979])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.nanmean(data1, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "masked_array(data = [0.3371727748691094 29.79581151832461 55.47643979057592],\n",
+       "             mask = [False False False],\n",
+       "       fill_value = 1e+20)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1 = np.ma.masked_invalid(data1)\n",
+    "np.mean(data1, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "%matplotlib notebook\n",
-    "import matplotlib.pyplot as plt"
+    "data2 = np.genfromtxt('./data/barrio_del_pilar-20151222.csv', skip_header=3, delimiter=';', usecols=(2,3,4))\n",
+    "data2 = np.ma.masked_invalid(data2)"
    ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plotting the data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Maximum values ** from: http://www.mambiente.munimadrid.es/opencms/export/sites/default/calaire/Anexos/valores_limite_1.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* NO2\n",
+    "    - Media anual: 40 µg/m3\n",
+    "    - **Media horaria: 200 µg/m3 **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 220)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JvsVqwRg35rI6kS/wyTZvBcJoNgKgAiTHwOgAtEQrK1RCpMSlqDIBqtvcDcOQ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\nBoPB0Iowom8wGAytiP8DZhkyA9miEikAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8bc915d6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data1[:, 1], label='2016')\n",
+    "plt.plot(data2[:, 1], label='2015')\n",
+    "\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.hlines(200, 0, 200, linestyles='--')\n",
+    "plt.ylim(0, 220)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"http://ccaa.elpais.com/ccaa/2015/12/24/madrid/1450960217_181674.html\" width=\"700\" height=\"400\"></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<iframe src=\"http://ccaa.elpais.com/ccaa/2015/12/24/madrid/1450960217_181674.html\" width=\"700\" height=\"400\"></iframe>')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* CO \n",
+    "    - **Máxima diaria de las medias móviles octohorarias: 10 mg/m³**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {
     "collapsed": true
    },
+   "outputs": [],
    "source": [
-    "#### Other options... \n",
-    "\n"
+    "# http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.convolve.html\n",
+    "def moving_average(x, N=8):\n",
+    "    return np.convolve(x, np.ones(N)/N, mode='same')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f8bc87fa278>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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fTqDXquUE1ahRcMUVZ/fO69d3vlC1ajnj0XPnQlSUU26LFs5/CwqcXyEFBdCq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\nAJPio4yvFU8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8bc91097f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(moving_average(data1[:, 0]), label='2016')\n",
+    "#plt.plot(data1[:, 0])\n",
+    "\n",
+    "plt.plot(moving_average(data2[:, 0]), label='2015')\n",
+    "#plt.plot(data2[:, 0])\n",
+    "\n",
+    "plt.hlines(10, 0, 250, linestyles='--')\n",
+    "plt.ylim(0, 11)\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* O3\n",
+    "    - **Máxima diaria de las medias móviles octohorarias: 120 µg/m3**\n",
+    "    - Umbral de información. 180 µg/m3\n",
+    "    - Media horaria. Umbral de alerta. 240 µg/m3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/miniconda/envs/pydata-basic-python/lib/python3.5/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
+      "  warnings.warn(\"No labelled objects found. \"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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o7vbII3J07vXr9j/HeBNXya5+ltSvD/z6q1w847HHZNPOiy8qc2xX2/VdXfTG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\nxhjTEQ59xhjTEQ59xhjTkf8Pth7JNHEzhb4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8bc88454e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(moving_average(data1[:, 2]))\n",
+    "#plt.plot(data1[:, 2])\n",
+    "\n",
+    "plt.plot(moving_average(data2[:, 2]))\n",
+    "#plt.plot(data2[:, 2])\n",
+    "\n",
+    "plt.hlines(180, 0, 250, linestyles='--')\n",
+    "plt.ylim(0, 190)\n",
+    "\n",
+    "plt.legend()"
    ]
   },
   {
@@ -760,6 +1398,72 @@
     "![matplotlib](./static/scipy_logo.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "scipy.linalg: ATLAS LAPACK and BLAS libraries\n",
+    "\n",
+    "scipy.stats: distributions, statistical functions...\n",
+    "\n",
+    "scipy.integrate: integration of functions and ODEs\n",
+    "\n",
+    "scipy.optimization: local and global optimization, fitting, root finding...\n",
+    "\n",
+    "scipy.interpolate: interpolation, splines...\n",
+    "\n",
+    "scipy.fftpack: Fourier trasnforms\n",
+    "\n",
+    "scipy.signal, scipy.special, scipy.io\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Graphical Representation: matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![matplotlib](./static/matplotlib.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "#### Other options... \n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -879,7 +1583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
@@ -1034,7 +1738,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/data/barrio_del_pilar-20151222.csv b/data/barrio_del_pilar-20151222.csv
new file mode 100644
index 0000000..4c6a106
--- /dev/null
+++ b/data/barrio_del_pilar-20151222.csv
@@ -0,0 +1,195 @@
+Estación: Barrio del Pilar;;;;
+Fecha;Hora;CO;NO2;O3
+;;mg/m³;µg/m³;µg/m³
+22/12/2015;01:00;0.5;65;7
+22/12/2015;02:00;0.4;50;7
+22/12/2015;03:00;0.3;37;11
+22/12/2015;04:00;0.3;37;10
+22/12/2015;05:00;0.3;29;17
+22/12/2015;06:00;0.3;36;13
+22/12/2015;07:00;0.3;47;7
+22/12/2015;08:00;0.4;57;6
+22/12/2015;09:00;0.5;67;6
+22/12/2015;10:00;0.5;69;8
+22/12/2015;11:00;0.5;67;11
+22/12/2015;12:00;0.5;64;15
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diff --git a/data/barrio_del_pilar-20160322.csv b/data/barrio_del_pilar-20160322.csv
new file mode 100644
index 0000000..26661b7
--- /dev/null
+++ b/data/barrio_del_pilar-20160322.csv
@@ -0,0 +1,195 @@
+Estación: Barrio del Pilar;;;;
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