sergiobuj/0_data.csv

## 0_data.csv

          
            Week
            cat
            dog
            fish

            
              2012-01-01 - 2012-01-07
              56
              92
              59

            
              2012-01-08 - 2012-01-14
              59
              91
              56

            
              2012-01-15 - 2012-01-21
              55
              91
              56

            
              2012-01-22 - 2012-01-28
              56
              89
              54

            
              2012-01-29 - 2012-02-04
              58
              89
              55

            
              2012-02-05 - 2012-02-11
              60
              94
              54

            
              2012-02-12 - 2012-02-18
              56
              98
              54

            
              2012-02-19 - 2012-02-25
              58
              91
              58

            
              2012-02-26 - 2012-03-03
              59
              85
              54

            
              2012-03-04 - 2012-03-10
              56
              86
              56

            
              2012-03-11 - 2012-03-17
              54
              87
              54

            
              2012-03-18 - 2012-03-24
              54
              87
              55

            
              2012-03-25 - 2012-03-31
              55
              86
              56

            
              2012-04-01 - 2012-04-07
              57
              89
              59

            
              2012-04-08 - 2012-04-14
              54
              89
              54

            
              2012-04-15 - 2012-04-21
              55
              91
              55

            
              2012-04-22 - 2012-04-28
              57
              87
              56

            
              2012-04-29 - 2012-05-05
              61
              92
              57

            
              2012-05-06 - 2012-05-12
              57
              91
              56

            
              2012-05-13 - 2012-05-19
              55
              85
              57

            
              2012-05-20 - 2012-05-26
              54
              87
              56

            
              2012-05-27 - 2012-06-02
              54
              88
              56

            
              2012-06-03 - 2012-06-09
              57
              88
              56

            
              2012-06-10 - 2012-06-16
              55
              87
              55

            
              2012-06-17 - 2012-06-23
              55
              88
              56

            
              2012-06-24 - 2012-06-30
              57
              92
              57

            
              2012-07-01 - 2012-07-07
              55
              94
              56

            
              2012-07-08 - 2012-07-14
              58
              94
              59

            
              2012-07-15 - 2012-07-21
              58
              96
              57

            
              2012-07-22 - 2012-07-28
              58
              92
              56

            
              2012-07-29 - 2012-08-04
              56
              93
              55

            
              2012-08-05 - 2012-08-11
              57
              94
              55

            
              2012-08-12 - 2012-08-18
              58
              100
              55

            
              2012-08-19 - 2012-08-25
              58
              99
              56

            
              2012-08-26 - 2012-09-01
              59
              93
              54

            
              2012-09-02 - 2012-09-08
              59
              92
              57

            
              2012-09-09 - 2012-09-15
              58
              93
              53

            
              2012-09-16 - 2012-09-22
              59
              91
              52

            
              2012-09-23 - 2012-09-29
              61
              92
              52

            
              2012-09-30 - 2012-10-06
              59
              91
              52

            
              2012-10-07 - 2012-10-13
              59
              91
              51

            
              2012-10-14 - 2012-10-20
              58
              89
              50

            
              2012-10-21 - 2012-10-27
              64
              88
              51

            
              2012-10-28 - 2012-11-03
              62
              85
              47

            
              2012-11-04 - 2012-11-10
              57
              88
              46

            
              2012-11-11 - 2012-11-17
              60
              89
              48

            
              2012-11-18 - 2012-11-24
              61
              95
              48

            
              2012-11-25 - 2012-12-01
              64
              92
              49

            
              2012-12-02 - 2012-12-08
              65
              89
              49

            
              2012-12-09 - 2012-12-15
              66
              89
              48

            
              2012-12-16 - 2012-12-22
              63
              88
              48

            
              2012-12-23 - 2012-12-29
              67
              97
              54

            
              2012-12-30 - 2013-01-05
              63
              99
              58

## learning_cloning_matplotlib_0.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Learning by cloning with matplotlib -0-"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since I only know a few thing about matplotlib, I will start easy and will try to reproduce a trend chart of terms used in Google searches.\n",
      "The chart I will use now is the comparison of the search terms 'cat', 'dog' and 'fish' from 2012."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Today's goal is to reproduce this (without the headlines, I'll leave that for later):\n",
      "from IPython.core.display import HTML\n",
      "HTML('<iframe width=\"500\" height=\"330\" src=\"//www.google.com/trends/fetchComponent?hl\\75en-US\\46q\\75cat,+dog,+fish\\46date\\0751/2012+12m\\46cmpt\\75q\\46content\\0751\\46cid\\75TIMESERIES_GRAPH_AVERAGES_CHART\\46export\\0755\\46w\\075500\\46h\\075330\" style=\"border: none;\"></iframe>')\n",
      "\n",
      "# Ready, set, go!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe width=\"500\" height=\"330\" src=\"//www.google.com/trends/fetchComponent?hl=en-US&q=cat,+dog,+fish&date=1/2012+12m&cmpt=q&content=1&cid=TIMESERIES_GRAPH_AVERAGES_CHART&export=5&w=500&h=330\" style=\"border: none;\"></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x10845b6d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Step 0\n",
      "I will start by browsing the [matplotlib gallery](http://matplotlib.org/gallery.html) to find something I can start from. I don't know the minimum preamble to start a figure, so I think this is a good way to get started.\n",
      "From the Gallery, I found a very [simple figure](http://matplotlib.org/examples/pylab_examples/pythonic_matplotlib.html), so I will start from that one."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# imports, from the docs: \"The pyplot interface is generally preferred for\n",
      "# non-interactive plotting (i.e., scripting). The pylab interface is convenient\n",
      "# for interactive calculations and plotting\"\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "# Seems that need some space\n",
      "fig = plt.figure(figsize=(5.21, 2.6), dpi=120) # got the (in x in)dpi equivalent of 500px x 300px\n",
      "\n",
      "# The image is divided in two and the first part is roughly 1/6 of the image.\n",
      "# To understand the subplots I went to http://matplotlib.org/users/gridspec.html\n",
      "bars = plt.subplot2grid((1,3), (0,0), colspan=1)\n",
      "plot = plt.subplot2grid((1,3), (0,1), colspan=2, axisbelow=True)\n",
      "\n",
      "# This has the underlying structure"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAACyCAYAAAAkn4bFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109c9ff90>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cleaning and removing lines and ticks\n",
      "bars.set_yticks([])\n",
      "bars.set_xlabel(\"Average\")\n",
      "bars.set_xticks([])\n",
      "bars.axes.set_ylim((0, 100))\n",
      "bars.spines['top'].set_visible(False)\n",
      "bars.spines['left'].set_visible(False)\n",
      "bars.spines['right'].set_visible(False)\n",
      "\n",
      "plot.set_xticklabels([\"Apr 2012\", \"Jul 2012\", \"Oct 2012\"])\n",
      "plot.set_xticks([4*4, 4*7, 4*10]) # setting the months where they begin (in weeks)\n",
      "plot.set_yticklabels([])\n",
      "plot.set_ylim((0, 120))\n",
      "plot.spines['top'].set_visible(False)\n",
      "plot.spines['left'].set_visible(False)\n",
      "plot.spines['right'].set_visible(False)\n",
      "\n",
      "# I don't know yet how many point I will have, so I will set the xticks later.\n",
      "# and the same for the bars in the other subplot."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On the [Google trends page](http://www.google.com/trends/explore#q=cat%2C%20dog%2C%20fish&cmpt=q) they give the option to download the data as a CSV file. Which I modified to leave only the important data for this chart and now the file looks like this:\n",
      "\n",
      "```\n",
      "Week,cat,dog,fish\n",
      "2012-01-01 - 2012-01-07,56,92,59\n",
      "2012-01-08 - 2012-01-14,59,91,56\n",
      "2012-01-15 - 2012-01-21,55,91,56\n",
      "2012-01-22 - 2012-01-28,56,89,54\n",
      "2012-01-29 - 2012-02-04,58,89,55\n",
      "2012-02-05 - 2012-02-11,60,94,54\n",
      "2012-02-12 - 2012-02-18,56,98,54\n",
      "2012-02-19 - 2012-02-25,58,91,58\n",
      "2012-02-26 - 2012-03-03,59,85,54\n",
      "...\n",
      "```\n",
      "\n",
      "Since I need this information individually, seems like a good moment to try [pandas](http://pandas.pydata.org/) (the Python Data Analysis Library) to import and handle this CSV file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "report = pd.read_csv('0_data.csv', na_values=' ') # na_values tells pandas, in the data, what is a null value.\n",
      "\n",
      "# Now using the attributes 'cat', 'dog' and 'fish' I can access each column of the CSV file\n",
      "report.fish.tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[59,\n",
        " 56,\n",
        " 56,\n",
        " 54,\n",
        " 55,\n",
        " 54,\n",
        " 54,\n",
        " 58,\n",
        " 54,\n",
        " 56,\n",
        " 54,\n",
        " 55,\n",
        " 56,\n",
        " 59,\n",
        " 54,\n",
        " 55,\n",
        " 56,\n",
        " 57,\n",
        " 56,\n",
        " 57,\n",
        " 56,\n",
        " 56,\n",
        " 56,\n",
        " 55,\n",
        " 56,\n",
        " 57,\n",
        " 56,\n",
        " 59,\n",
        " 57,\n",
        " 56,\n",
        " 55,\n",
        " 55,\n",
        " 55,\n",
        " 56,\n",
        " 54,\n",
        " 57,\n",
        " 53,\n",
        " 52,\n",
        " 52,\n",
        " 52,\n",
        " 51,\n",
        " 50,\n",
        " 51,\n",
        " 47,\n",
        " 46,\n",
        " 48,\n",
        " 48,\n",
        " 49,\n",
        " 49,\n",
        " 48,\n",
        " 48,\n",
        " 54,\n",
        " 58]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot each column with their color. \n",
      "plot.plot(report.cat.tolist(), color='blue', label='cat')\n",
      "plot.plot(report.dog.tolist(),  color='red', label='dog')\n",
      "plot.plot(report.fish.tolist(), color='orange', label='fish')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109e47a90>]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# and the same for the bars in the other subplot.\n",
      "bars.axes.set_xlim((0, 5))\n",
      "bars.bar(1, report.cat.mean(), color='blue', edgecolor='none', width=0.9, label='cat')\n",
      "bars.bar(2, report.dog.mean(), color='red', edgecolor='none', width=0.92, label='dog')\n",
      "bars.bar(3, report.fish.mean(), color='orange', edgecolor='none', width=0.92, label='fish')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<Container object of 1 artists>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The legend\n",
      "plot.legend(frameon=False, loc=2, fontsize='xx-small', ncol=3, mode='none')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.legend.Legend at 0x10a71c110>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Almost there...\n",
      "# A few adjustments to fonts and colors\n",
      "plot.xaxis.set_ticks_position('none')\n",
      "plot.yaxis.set_ticks_position('none')\n",
      "\n",
      "for tk in plot.axes.get_xmajorticklabels():\n",
      "    plt.setp(tk, color='gray', fontsize='xx-small')\n",
      "\n",
      "plt.setp(bars.xaxis.label, color='gray', fontsize='xx-small')\n",
      "\n",
      "plot.grid(which='major', axis='y', linewidth=.8, linestyle='-', color='lightgray')\n",
      "plot.spines['bottom'].set_color('gray')\n",
      "bars.spines['bottom'].set_color('gray')\n",
      "\n",
      "plt.subplots_adjust(wspace=-0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x109d96c90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Thoughts\n",
      " - The `setp` function is very useful to set properties to elements\n",
      " - Pandas made it very easy to get up and running with the data.\n",
      " - Pandas has a way to plot data using matplotlib directly, this is something to explore.\n",
      " - The two graphs seem preety similar to me.\n",
      "\n",
      "## Things I could not get quite right\n",
      " - Colors, different tones of blue, red and... orange? yellow?\n",
      " - Spacing between elements in the legend\n",
      " - Boxes instead of lines in the legend"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The entire code\n",
      "\n",
      "# imports, from the docs: \"The pyplot interface is generally preferred for\n",
      "# non-interactive plotting (i.e., scripting). The pylab interface is convenient\n",
      "# for interactive calculations and plotting\"\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Seems that need some space\n",
      "fig = plt.figure(figsize=(5.21, 2.6), dpi=120) # got the (in x in)dpi equivalent of 500px x 300px\n",
      "\n",
      "# The image is divided in two and the first part is roughly 1/6 of the image.\n",
      "# To understand the subplots I went to http://matplotlib.org/users/gridspec.html\n",
      "bars = plt.subplot2grid((1,3), (0,0), colspan=1)\n",
      "plot = plt.subplot2grid((1,3), (0,1), colspan=2, axisbelow=True)\n",
      "\n",
      "# This has the underlying structure\n",
      "# Cleaning and removing lines and ticks\n",
      "bars.set_yticks([])\n",
      "bars.set_xlabel(\"Average\")\n",
      "bars.set_xticks([])\n",
      "bars.axes.set_ylim((0, 100))\n",
      "bars.spines['top'].set_visible(False)\n",
      "bars.spines['left'].set_visible(False)\n",
      "bars.spines['right'].set_visible(False)\n",
      "\n",
      "plot.set_xticklabels([\"Apr 2012\", \"Jul 2012\", \"Oct 2012\"])\n",
      "plot.set_xticks([4*4, 4*7, 4*10]) # setting the months where they begin (in weeks)\n",
      "plot.set_yticklabels([])\n",
      "plot.set_ylim((0, 120))\n",
      "plot.spines['top'].set_visible(False)\n",
      "plot.spines['left'].set_visible(False)\n",
      "plot.spines['right'].set_visible(False)\n",
      "\n",
      "# I don't know yet how many point I will have, so I will set the xticks later.\n",
      "# and the same for the bars in the other subplot.\n",
      "import pandas as pd\n",
      "report = pd.read_csv('0_data.csv', na_values=' ')\n",
      "\n",
      "# plot each column with their color. \n",
      "plot.plot(report.cat.tolist(), color='blue', label='cat')\n",
      "plot.plot(report.dog.tolist(),  color='red', label='dog')\n",
      "plot.plot(report.fish.tolist(), color='orange', label='fish')\n",
      "# and the same for the bars in the other subplot.\n",
      "bars.axes.set_xlim((0, 5))\n",
      "bars.bar(1, report.cat.mean(), color='blue', edgecolor='none', width=0.9, label='cat')\n",
      "bars.bar(2, report.dog.mean(), color='red', edgecolor='none', width=0.92, label='dog')\n",
      "bars.bar(3, report.fish.mean(), color='orange', edgecolor='none', width=0.92, label='fish')\n",
      "\n",
      "# The legend\n",
      "plot.legend(frameon=False, loc=2, fontsize='xx-small', ncol=3, mode='none')\n",
      "\n",
      "# A few adjustments to fonts and colors\n",
      "plot.xaxis.set_ticks_position('none')\n",
      "plot.yaxis.set_ticks_position('none')\n",
      "\n",
      "for tk in plot.axes.get_xmajorticklabels():\n",
      "    plt.setp(tk, color='gray', fontsize='xx-small')\n",
      "\n",
      "plt.setp(bars.xaxis.label, color='gray', fontsize='xx-small')\n",
      "\n",
      "plot.grid(which='major', axis='y', linewidth=.8, linestyle='-', color='lightgray')\n",
      "plot.spines['bottom'].set_color('gray')\n",
      "bars.spines['bottom'].set_color('gray')\n",
      "\n",
      "plt.subplots_adjust(wspace=-0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C2bNniYiIIC8vj++//54mTZpw7ty5m1J3pRROTk4cPXqUpKSkUstDQkJITEzk\n4sWLADRp0oR9+/bh5OSExWLh8OHDnDt3jsTERLZu3YrZbCYmJuam1HXgwIEEBwezb98+srKyaNOm\nDY6OjoSFhREXF0ezZs04ePBgnax7TTL0FIui5/zLli3D2dmZadOmcfLkSZKSkmjYsCH9+/fnl19+\nYcyYMezatYuWLVsSGRnJ9OnTCQgIoE2bNsTFxXHfffcRGhpKixYt6Ny5M05OTrW8d/VHyb6Zuiot\nLY3AwEAmTpyInZ2hT1JuK4YOsRsVFRVFUlISTZs2vSmdxEKIm++2DjEhhPEZuk9MCCEkxIQQhiYh\nJoQwNAkxIYShSYgJIQxNQkwIYWj/Dw2kMEph/YQzAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a737710>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sergiobuj"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}

## learning_cloning_matplotlib_1.ipynb

      
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## learning_cloning_matplotlib_2.ipynb

      
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## learning_cloning_matplotlib_3.ipynb

      
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              learning_cloning_matplotlib_3.ipynb
            
          
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Week	cat	dog	fish
2012-01-01 - 2012-01-07	56	92	59
2012-01-08 - 2012-01-14	59	91	56
2012-01-15 - 2012-01-21	55	91	56
2012-01-22 - 2012-01-28	56	89	54
2012-01-29 - 2012-02-04	58	89	55
2012-02-05 - 2012-02-11	60	94	54
2012-02-12 - 2012-02-18	56	98	54
2012-02-19 - 2012-02-25	58	91	58
2012-02-26 - 2012-03-03	59	85	54
2012-03-04 - 2012-03-10	56	86	56
2012-03-11 - 2012-03-17	54	87	54
2012-03-18 - 2012-03-24	54	87	55
2012-03-25 - 2012-03-31	55	86	56
2012-04-01 - 2012-04-07	57	89	59
2012-04-08 - 2012-04-14	54	89	54
2012-04-15 - 2012-04-21	55	91	55
2012-04-22 - 2012-04-28	57	87	56
2012-04-29 - 2012-05-05	61	92	57
2012-05-06 - 2012-05-12	57	91	56
2012-05-13 - 2012-05-19	55	85	57
2012-05-20 - 2012-05-26	54	87	56
2012-05-27 - 2012-06-02	54	88	56
2012-06-03 - 2012-06-09	57	88	56
2012-06-10 - 2012-06-16	55	87	55
2012-06-17 - 2012-06-23	55	88	56
2012-06-24 - 2012-06-30	57	92	57
2012-07-01 - 2012-07-07	55	94	56
2012-07-08 - 2012-07-14	58	94	59
2012-07-15 - 2012-07-21	58	96	57
2012-07-22 - 2012-07-28	58	92	56
2012-07-29 - 2012-08-04	56	93	55
2012-08-05 - 2012-08-11	57	94	55
2012-08-12 - 2012-08-18	58	100	55
2012-08-19 - 2012-08-25	58	99	56
2012-08-26 - 2012-09-01	59	93	54
2012-09-02 - 2012-09-08	59	92	57
2012-09-09 - 2012-09-15	58	93	53
2012-09-16 - 2012-09-22	59	91	52
2012-09-23 - 2012-09-29	61	92	52
2012-09-30 - 2012-10-06	59	91	52
2012-10-07 - 2012-10-13	59	91	51
2012-10-14 - 2012-10-20	58	89	50
2012-10-21 - 2012-10-27	64	88	51
2012-10-28 - 2012-11-03	62	85	47
2012-11-04 - 2012-11-10	57	88	46
2012-11-11 - 2012-11-17	60	89	48
2012-11-18 - 2012-11-24	61	95	48
2012-11-25 - 2012-12-01	64	92	49
2012-12-02 - 2012-12-08	65	89	49
2012-12-09 - 2012-12-15	66	89	48
2012-12-16 - 2012-12-22	63	88	48
2012-12-23 - 2012-12-29	67	97	54
2012-12-30 - 2013-01-05	63	99	58