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manujeevanprakash/14 pandas functions

Created December 11, 2014 04:30
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14 pandas functions
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14 pandas functions
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"cd C:\\Users\\tk\\Desktop"
],
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"C:\\Users\\tk\\Desktop\n"
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"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
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"input": [
"import brewer2mpl\n",
"from matplotlib import rcParams\n",
"\n",
"#colorbrewer2 Dark2 qualitative color table\n",
"dark2_cmap = brewer2mpl.get_map('Dark2', 'Qualitative', 7)\n",
"dark2_colors = dark2_cmap.mpl_colors\n",
"\n",
"rcParams['figure.figsize'] = (10, 6)\n",
"rcParams['figure.dpi'] = 150\n",
"rcParams['axes.color_cycle'] = dark2_colors\n",
"rcParams['lines.linewidth'] = 2\n",
"rcParams['axes.facecolor'] = 'white'\n",
"rcParams['font.size'] = 14\n",
"rcParams['patch.edgecolor'] = 'white'\n",
"rcParams['patch.facecolor'] = dark2_colors[0]\n",
"rcParams['font.family'] = 'StixGeneral'\n",
"\n",
"\n",
"def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
" \"\"\"\n",
" Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n",
" \n",
" The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
" \"\"\"\n",
" ax = axes or plt.gca()\n",
" ax.spines['top'].set_visible(top)\n",
" ax.spines['right'].set_visible(right)\n",
" ax.spines['left'].set_visible(left)\n",
" ax.spines['bottom'].set_visible(bottom)\n",
" \n",
" #turn off all ticks\n",
" ax.yaxis.set_ticks_position('none')\n",
" ax.xaxis.set_ticks_position('none')\n",
" \n",
" #now re-enable visibles\n",
" if top:\n",
" ax.xaxis.tick_top()\n",
" if bottom:\n",
" ax.xaxis.tick_bottom()\n",
" if left:\n",
" ax.yaxis.tick_left()\n",
" if right:\n",
" ax.yaxis.tick_right()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr/>\n",
"\n",
"\"The Olive Oils data has eight explanatory variables (levels of fatty acids in the oils) and nine classes (areas of Italy). The content of the oils is a subject of study in its own right: Olive oil has high nutritional value, and some of its constituent fatty acids are considered to be more beneficial than others.\""
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil = pd.read_csv('olive.csv') \n",
"olive_oil.head(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>region</th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1075</td>\n",
" <td> 75</td>\n",
" <td> 226</td>\n",
" <td> 7823</td>\n",
" <td> 672</td>\n",
" <td> 36</td>\n",
" <td> 60</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1088</td>\n",
" <td> 73</td>\n",
" <td> 224</td>\n",
" <td> 7709</td>\n",
" <td> 781</td>\n",
" <td> 31</td>\n",
" <td> 61</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 911</td>\n",
" <td> 54</td>\n",
" <td> 246</td>\n",
" <td> 8113</td>\n",
" <td> 549</td>\n",
" <td> 31</td>\n",
" <td> 63</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 966</td>\n",
" <td> 57</td>\n",
" <td> 240</td>\n",
" <td> 7952</td>\n",
" <td> 619</td>\n",
" <td> 50</td>\n",
" <td> 78</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1051</td>\n",
" <td> 67</td>\n",
" <td> 259</td>\n",
" <td> 7771</td>\n",
" <td> 672</td>\n",
" <td> 50</td>\n",
" <td> 80</td>\n",
" <td> 46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" Unnamed: 0 region area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"0 1.North-Apulia 1 1 1075 75 226 7823 672 36 60 29\n",
"1 2.North-Apulia 1 1 1088 73 224 7709 781 31 61 29\n",
"2 3.North-Apulia 1 1 911 54 246 8113 549 31 63 29\n",
"3 4.North-Apulia 1 1 966 57 240 7952 619 50 78 35\n",
"4 5.North-Apulia 1 1 1051 67 259 7771 672 50 80 46"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"(572, 11)"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil.rename(columns = {olive_oil.columns[0]:'area_Idili'}, inplace = True) \n",
"olive_oil.head(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area_Idili</th>\n",
" <th>region</th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1075</td>\n",
" <td> 75</td>\n",
" <td> 226</td>\n",
" <td> 7823</td>\n",
" <td> 672</td>\n",
" <td> 36</td>\n",
" <td> 60</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1088</td>\n",
" <td> 73</td>\n",
" <td> 224</td>\n",
" <td> 7709</td>\n",
" <td> 781</td>\n",
" <td> 31</td>\n",
" <td> 61</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 911</td>\n",
" <td> 54</td>\n",
" <td> 246</td>\n",
" <td> 8113</td>\n",
" <td> 549</td>\n",
" <td> 31</td>\n",
" <td> 63</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 966</td>\n",
" <td> 57</td>\n",
" <td> 240</td>\n",
" <td> 7952</td>\n",
" <td> 619</td>\n",
" <td> 50</td>\n",
" <td> 78</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1051</td>\n",
" <td> 67</td>\n",
" <td> 259</td>\n",
" <td> 7771</td>\n",
" <td> 672</td>\n",
" <td> 50</td>\n",
" <td> 80</td>\n",
" <td> 46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"prompt_number": 7,
"text": [
" area_Idili region area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"0 1.North-Apulia 1 1 1075 75 226 7823 672 36 60 29\n",
"1 2.North-Apulia 1 1 1088 73 224 7709 781 31 61 29\n",
"2 3.North-Apulia 1 1 911 54 246 8113 549 31 63 29\n",
"3 4.North-Apulia 1 1 966 57 240 7952 619 50 78 35\n",
"4 5.North-Apulia 1 1 1051 67 259 7771 672 50 80 46"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.DataFrame(olive_oil.columns)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> area_Idili</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> region</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> area</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> palmitic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> palmitoleic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> stearic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> oleic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> linoleic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> linolenic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> arachidic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> eicosenoic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
" 0\n",
"0 area_Idili\n",
"1 region\n",
"2 area\n",
"3 palmitic\n",
"4 palmitoleic\n",
"5 stearic\n",
"6 oleic\n",
"7 linoleic\n",
"8 linolenic\n",
"9 arachidic\n",
"10 eicosenoic"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"unique_in_region = olive_oil.region.unique() # We will find how many unique entries are there in region column.\n",
"unique_in_area = olive_oil.area.unique()\n",
"print unique_in_region\n",
"print unique_in_area"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 2 3]\n",
"[1 2 3 4 5 6 9 7 8]\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.crosstab(olive_oil.area, olive_oil.region) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>region</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>area</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 25</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 56</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 206</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 36</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 0</td>\n",
" <td> 65</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0</td>\n",
" <td> 33</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 51</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"region 1 2 3\n",
"area \n",
"1 25 0 0\n",
"2 56 0 0\n",
"3 206 0 0\n",
"4 36 0 0\n",
"5 0 65 0\n",
"6 0 33 0\n",
"7 0 0 50\n",
"8 0 0 50\n",
"9 0 0 51"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil.head(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area_Idili</th>\n",
" <th>region</th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1075</td>\n",
" <td> 75</td>\n",
" <td> 226</td>\n",
" <td> 7823</td>\n",
" <td> 672</td>\n",
" <td> 36</td>\n",
" <td> 60</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1088</td>\n",
" <td> 73</td>\n",
" <td> 224</td>\n",
" <td> 7709</td>\n",
" <td> 781</td>\n",
" <td> 31</td>\n",
" <td> 61</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 911</td>\n",
" <td> 54</td>\n",
" <td> 246</td>\n",
" <td> 8113</td>\n",
" <td> 549</td>\n",
" <td> 31</td>\n",
" <td> 63</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 966</td>\n",
" <td> 57</td>\n",
" <td> 240</td>\n",
" <td> 7952</td>\n",
" <td> 619</td>\n",
" <td> 50</td>\n",
" <td> 78</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1051</td>\n",
" <td> 67</td>\n",
" <td> 259</td>\n",
" <td> 7771</td>\n",
" <td> 672</td>\n",
" <td> 50</td>\n",
" <td> 80</td>\n",
" <td> 46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
" area_Idili region area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"0 1.North-Apulia 1 1 1075 75 226 7823 672 36 60 29\n",
"1 2.North-Apulia 1 1 1088 73 224 7709 781 31 61 29\n",
"2 3.North-Apulia 1 1 911 54 246 8113 549 31 63 29\n",
"3 4.North-Apulia 1 1 966 57 240 7952 619 50 78 35\n",
"4 5.North-Apulia 1 1 1051 67 259 7771 672 50 80 46"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil.area_Idili = olive_oil.area_Idili.map(lambda x: x.split('.')[-1]) \n",
"olive_oil.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area_Idili</th>\n",
" <th>region</th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1075</td>\n",
" <td> 75</td>\n",
" <td> 226</td>\n",
" <td> 7823</td>\n",
" <td> 672</td>\n",
" <td> 36</td>\n",
" <td> 60</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1088</td>\n",
" <td> 73</td>\n",
" <td> 224</td>\n",
" <td> 7709</td>\n",
" <td> 781</td>\n",
" <td> 31</td>\n",
" <td> 61</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 911</td>\n",
" <td> 54</td>\n",
" <td> 246</td>\n",
" <td> 8113</td>\n",
" <td> 549</td>\n",
" <td> 31</td>\n",
" <td> 63</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 966</td>\n",
" <td> 57</td>\n",
" <td> 240</td>\n",
" <td> 7952</td>\n",
" <td> 619</td>\n",
" <td> 50</td>\n",
" <td> 78</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1051</td>\n",
" <td> 67</td>\n",
" <td> 259</td>\n",
" <td> 7771</td>\n",
" <td> 672</td>\n",
" <td> 50</td>\n",
" <td> 80</td>\n",
" <td> 46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" area_Idili region area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"0 North-Apulia 1 1 1075 75 226 7823 672 36 60 29\n",
"1 North-Apulia 1 1 1088 73 224 7709 781 31 61 29\n",
"2 North-Apulia 1 1 911 54 246 8113 549 31 63 29\n",
"3 North-Apulia 1 1 966 57 240 7952 619 50 78 35\n",
"4 North-Apulia 1 1 1051 67 259 7771 672 50 80 46"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# How the split function works \n",
"x = '1.northapulia'\n",
"y = x.split('.')\n",
"print y\n",
"z = x.split('.')[-1] #-1 returns the last element of the list\n",
"z"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['1', 'northapulia']\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"'northapulia'"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil[['palmitic', 'palmitoleic']].head(5) # you can access subset of columns of a data frame. (http://bit.ly/1sPHf1u)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1075</td>\n",
" <td> 75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1088</td>\n",
" <td> 73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 911</td>\n",
" <td> 54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 966</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1051</td>\n",
" <td> 67</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
" palmitic palmitoleic\n",
"0 1075 75\n",
"1 1088 73\n",
"2 911 54\n",
"3 966 57\n",
"4 1051 67"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil['palmitic']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"0 1075\n",
"1 1088\n",
"2 911\n",
"3 966\n",
"4 1051\n",
"5 911\n",
"6 922\n",
"7 1100\n",
"8 1082\n",
"9 1037\n",
"10 1051\n",
"11 1036\n",
"12 1074\n",
"13 875\n",
"14 952\n",
"...\n",
"557 1010\n",
"558 1020\n",
"559 1120\n",
"560 1090\n",
"561 1100\n",
"562 1090\n",
"563 1150\n",
"564 1110\n",
"565 1010\n",
"566 1070\n",
"567 1280\n",
"568 1060\n",
"569 1010\n",
"570 990\n",
"571 960\n",
"Name: palmitic, Length: 572, dtype: int64"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"print \" the type of olive_oil[['palmitic']]: \\t\", type(olive_oil[['palmitic']])\n",
"print \" the type of olive_oil['palmitic']: \\t\", type(olive_oil['palmitic'])\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" the type of olive_oil[['palmitic']]: \t<class 'pandas.core.frame.DataFrame'>\n",
" the type of olive_oil['palmitic']: \t<class 'pandas.core.series.Series'>\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil.palmitic # this is a convienient way to access a specific column"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"0 1075\n",
"1 1088\n",
"2 911\n",
"3 966\n",
"4 1051\n",
"5 911\n",
"6 922\n",
"7 1100\n",
"8 1082\n",
"9 1037\n",
"10 1051\n",
"11 1036\n",
"12 1074\n",
"13 875\n",
"14 952\n",
"...\n",
"557 1010\n",
"558 1020\n",
"559 1120\n",
"560 1090\n",
"561 1100\n",
"562 1090\n",
"563 1150\n",
"564 1110\n",
"565 1010\n",
"566 1070\n",
"567 1280\n",
"568 1060\n",
"569 1010\n",
"570 990\n",
"571 960\n",
"Name: palmitic, Length: 572, dtype: int64"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What map did is it took a pandas series in form of a list. Took that list and mapped each value of that list to something. here we are going to use a data frame( set of lists). for data frame you should use apply"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list_of_acids =['palmitic', 'palmitoleic', 'stearic', 'oleic', 'linoleic', 'linolenic', 'arachidic', 'eicosenoic']\n",
"df = olive_oil[list_of_acids].apply(lambda x: x/100.0)\n",
"df.head(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 10.75</td>\n",
" <td> 0.75</td>\n",
" <td> 2.26</td>\n",
" <td> 78.23</td>\n",
" <td> 6.72</td>\n",
" <td> 0.36</td>\n",
" <td> 0.60</td>\n",
" <td> 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 10.88</td>\n",
" <td> 0.73</td>\n",
" <td> 2.24</td>\n",
" <td> 77.09</td>\n",
" <td> 7.81</td>\n",
" <td> 0.31</td>\n",
" <td> 0.61</td>\n",
" <td> 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 9.11</td>\n",
" <td> 0.54</td>\n",
" <td> 2.46</td>\n",
" <td> 81.13</td>\n",
" <td> 5.49</td>\n",
" <td> 0.31</td>\n",
" <td> 0.63</td>\n",
" <td> 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 9.66</td>\n",
" <td> 0.57</td>\n",
" <td> 2.40</td>\n",
" <td> 79.52</td>\n",
" <td> 6.19</td>\n",
" <td> 0.50</td>\n",
" <td> 0.78</td>\n",
" <td> 0.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 10.51</td>\n",
" <td> 0.67</td>\n",
" <td> 2.59</td>\n",
" <td> 77.71</td>\n",
" <td> 6.72</td>\n",
" <td> 0.50</td>\n",
" <td> 0.80</td>\n",
" <td> 0.46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"0 10.75 0.75 2.26 78.23 6.72 0.36 0.60 0.29\n",
"1 10.88 0.73 2.24 77.09 7.81 0.31 0.61 0.29\n",
"2 9.11 0.54 2.46 81.13 5.49 0.31 0.63 0.29\n",
"3 9.66 0.57 2.40 79.52 6.19 0.50 0.78 0.35\n",
"4 10.51 0.67 2.59 77.71 6.72 0.50 0.80 0.46"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olive_oil[list_of_acids] =df # we are replacing the acid list values in olive_oil\n",
"olive_oil.head(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area_Idili</th>\n",
" <th>region</th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 10.75</td>\n",
" <td> 0.75</td>\n",
" <td> 2.26</td>\n",
" <td> 78.23</td>\n",
" <td> 6.72</td>\n",
" <td> 0.36</td>\n",
" <td> 0.60</td>\n",
" <td> 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 10.88</td>\n",
" <td> 0.73</td>\n",
" <td> 2.24</td>\n",
" <td> 77.09</td>\n",
" <td> 7.81</td>\n",
" <td> 0.31</td>\n",
" <td> 0.61</td>\n",
" <td> 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 9.11</td>\n",
" <td> 0.54</td>\n",
" <td> 2.46</td>\n",
" <td> 81.13</td>\n",
" <td> 5.49</td>\n",
" <td> 0.31</td>\n",
" <td> 0.63</td>\n",
" <td> 0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 9.66</td>\n",
" <td> 0.57</td>\n",
" <td> 2.40</td>\n",
" <td> 79.52</td>\n",
" <td> 6.19</td>\n",
" <td> 0.50</td>\n",
" <td> 0.78</td>\n",
" <td> 0.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> North-Apulia</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 10.51</td>\n",
" <td> 0.67</td>\n",
" <td> 2.59</td>\n",
" <td> 77.71</td>\n",
" <td> 6.72</td>\n",
" <td> 0.50</td>\n",
" <td> 0.80</td>\n",
" <td> 0.46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" area_Idili region area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"0 North-Apulia 1 1 10.75 0.75 2.26 78.23 6.72 0.36 0.60 0.29\n",
"1 North-Apulia 1 1 10.88 0.73 2.24 77.09 7.81 0.31 0.61 0.29\n",
"2 North-Apulia 1 1 9.11 0.54 2.46 81.13 5.49 0.31 0.63 0.29\n",
"3 North-Apulia 1 1 9.66 0.57 2.40 79.52 6.19 0.50 0.78 0.35\n",
"4 North-Apulia 1 1 10.51 0.67 2.59 77.71 6.72 0.50 0.80 0.46"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.hist(olive_oil.palmitic)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"(array([ 1., 0., 11., 71., 188., 79., 131., 73., 9., 9.]),\n",
" array([ 6.1 , 7.243, 8.386, 9.529, 10.672, 11.815, 12.958,\n",
" 14.101, 15.244, 16.387, 17.53 ]),\n",
" <a list of 10 Patch objects>)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x67b9e10>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, axes=plt.subplots(figsize=(10,10), nrows=2, ncols=2)\n",
"axes[0][0].plot(olive_oil.palmitic, olive_oil.linolenic)\n",
"axes[0][1].plot(olive_oil.palmitic, olive_oil.linolenic, '.')\n",
"axes[1][0].scatter(olive_oil.palmitic, olive_oil.linolenic)\n",
"axes[1][1].hist(olive_oil.palmitic)\n",
"fig.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fqDMqrKqq1vYFEaO1z50dHADH5xn+DJs6ai9an/S7wpr1E6FhgaFtkudqK8+2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SFa+zyAFH2FRVEsBdYKgvAF0BvFgKzBW2+9JX2Ojo4mOjraAt20Ul84yBWvPUqrKrhyK6/R+vHx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\n6xQ3wGZmZmbWKW6AzczMzKxT/g8AQibMuJnQvwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x677b810>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"region_groupby = olive_oil.groupby('region')\n",
"grp_reg=region_groupby.describe()\n",
"grp_reg.head(20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>arachidic</th>\n",
" <th>area</th>\n",
" <th>eicosenoic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>oleic</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">1</th>\n",
" <th>count</th>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" <td> 323.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.631176</td>\n",
" <td> 2.783282</td>\n",
" <td> 0.273220</td>\n",
" <td> 10.334985</td>\n",
" <td> 0.380650</td>\n",
" <td> 71.000093</td>\n",
" <td> 13.322879</td>\n",
" <td> 1.548019</td>\n",
" <td> 2.287740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.111644</td>\n",
" <td> 0.741054</td>\n",
" <td> 0.083915</td>\n",
" <td> 2.106730</td>\n",
" <td> 0.079727</td>\n",
" <td> 3.451431</td>\n",
" <td> 1.529349</td>\n",
" <td> 0.507237</td>\n",
" <td> 0.398709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.320000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.100000</td>\n",
" <td> 4.480000</td>\n",
" <td> 0.200000</td>\n",
" <td> 63.000000</td>\n",
" <td> 8.750000</td>\n",
" <td> 0.350000</td>\n",
" <td> 1.520000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 0.560000</td>\n",
" <td> 2.500000</td>\n",
" <td> 0.220000</td>\n",
" <td> 8.555000</td>\n",
" <td> 0.320000</td>\n",
" <td> 68.830000</td>\n",
" <td> 12.680000</td>\n",
" <td> 1.215000</td>\n",
" <td> 2.015000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.620000</td>\n",
" <td> 3.000000</td>\n",
" <td> 0.270000</td>\n",
" <td> 10.900000</td>\n",
" <td> 0.370000</td>\n",
" <td> 70.300000</td>\n",
" <td> 13.460000</td>\n",
" <td> 1.630000</td>\n",
" <td> 2.230000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.690000</td>\n",
" <td> 3.000000</td>\n",
" <td> 0.320000</td>\n",
" <td> 12.025000</td>\n",
" <td> 0.440000</td>\n",
" <td> 72.835000</td>\n",
" <td> 14.190000</td>\n",
" <td> 1.850000</td>\n",
" <td> 2.495000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 1.020000</td>\n",
" <td> 4.000000</td>\n",
" <td> 0.580000</td>\n",
" <td> 14.620000</td>\n",
" <td> 0.740000</td>\n",
" <td> 81.130000</td>\n",
" <td> 17.530000</td>\n",
" <td> 2.800000</td>\n",
" <td> 3.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">2</th>\n",
" <th>count</th>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" <td> 98.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.731735</td>\n",
" <td> 5.336735</td>\n",
" <td> 0.019388</td>\n",
" <td> 11.965306</td>\n",
" <td> 0.270918</td>\n",
" <td> 72.680204</td>\n",
" <td> 11.113469</td>\n",
" <td> 0.967449</td>\n",
" <td> 2.261837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.118826</td>\n",
" <td> 0.475023</td>\n",
" <td> 0.007436</td>\n",
" <td> 1.072336</td>\n",
" <td> 0.053844</td>\n",
" <td> 1.418783</td>\n",
" <td> 0.404111</td>\n",
" <td> 0.138514</td>\n",
" <td> 0.176363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.450000</td>\n",
" <td> 5.000000</td>\n",
" <td> 0.010000</td>\n",
" <td> 10.570000</td>\n",
" <td> 0.150000</td>\n",
" <td> 68.820000</td>\n",
" <td> 10.300000</td>\n",
" <td> 0.350000</td>\n",
" <td> 1.990000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 0.660000</td>\n",
" <td> 5.000000</td>\n",
" <td> 0.010000</td>\n",
" <td> 11.122500</td>\n",
" <td> 0.230000</td>\n",
" <td> 71.372500</td>\n",
" <td> 10.852500</td>\n",
" <td> 0.882500</td>\n",
" <td> 2.120000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.720000</td>\n",
" <td> 5.000000</td>\n",
" <td> 0.020000</td>\n",
" <td> 11.465000</td>\n",
" <td> 0.270000</td>\n",
" <td> 73.255000</td>\n",
" <td> 11.075000</td>\n",
" <td> 0.960000</td>\n",
" <td> 2.220000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.810000</td>\n",
" <td> 6.000000</td>\n",
" <td> 0.020000</td>\n",
" <td> 13.065000</td>\n",
" <td> 0.300000</td>\n",
" <td> 73.810000</td>\n",
" <td> 11.372500</td>\n",
" <td> 1.040000</td>\n",
" <td> 2.395000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 1.050000</td>\n",
" <td> 6.000000</td>\n",
" <td> 0.030000</td>\n",
" <td> 14.700000</td>\n",
" <td> 0.430000</td>\n",
" <td> 74.390000</td>\n",
" <td> 12.130000</td>\n",
" <td> 1.350000</td>\n",
" <td> 2.720000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">3</th>\n",
" <th>count</th>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" <td> 151.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.375762</td>\n",
" <td> 8.006623</td>\n",
" <td> 0.019735</td>\n",
" <td> 7.270331</td>\n",
" <td> 0.217881</td>\n",
" <td> 77.930530</td>\n",
" <td> 10.948013</td>\n",
" <td> 0.837351</td>\n",
" <td> 2.308013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.293586</td>\n",
" <td> 0.820542</td>\n",
" <td> 0.007298</td>\n",
" <td> 1.431226</td>\n",
" <td> 0.168865</td>\n",
" <td> 1.648155</td>\n",
" <td> 0.825635</td>\n",
" <td> 0.264388</td>\n",
" <td> 0.389560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 0.010000</td>\n",
" <td> 5.100000</td>\n",
" <td> 0.000000</td>\n",
" <td> 73.400000</td>\n",
" <td> 6.100000</td>\n",
" <td> 0.150000</td>\n",
" <td> 1.700000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
" arachidic area eicosenoic linoleic linolenic oleic palmitic palmitoleic stearic\n",
"region \n",
"1 count 323.000000 323.000000 323.000000 323.000000 323.000000 323.000000 323.000000 323.000000 323.000000\n",
" mean 0.631176 2.783282 0.273220 10.334985 0.380650 71.000093 13.322879 1.548019 2.287740\n",
" std 0.111644 0.741054 0.083915 2.106730 0.079727 3.451431 1.529349 0.507237 0.398709\n",
" min 0.320000 1.000000 0.100000 4.480000 0.200000 63.000000 8.750000 0.350000 1.520000\n",
" 25% 0.560000 2.500000 0.220000 8.555000 0.320000 68.830000 12.680000 1.215000 2.015000\n",
" 50% 0.620000 3.000000 0.270000 10.900000 0.370000 70.300000 13.460000 1.630000 2.230000\n",
" 75% 0.690000 3.000000 0.320000 12.025000 0.440000 72.835000 14.190000 1.850000 2.495000\n",
" max 1.020000 4.000000 0.580000 14.620000 0.740000 81.130000 17.530000 2.800000 3.750000\n",
"2 count 98.000000 98.000000 98.000000 98.000000 98.000000 98.000000 98.000000 98.000000 98.000000\n",
" mean 0.731735 5.336735 0.019388 11.965306 0.270918 72.680204 11.113469 0.967449 2.261837\n",
" std 0.118826 0.475023 0.007436 1.072336 0.053844 1.418783 0.404111 0.138514 0.176363\n",
" min 0.450000 5.000000 0.010000 10.570000 0.150000 68.820000 10.300000 0.350000 1.990000\n",
" 25% 0.660000 5.000000 0.010000 11.122500 0.230000 71.372500 10.852500 0.882500 2.120000\n",
" 50% 0.720000 5.000000 0.020000 11.465000 0.270000 73.255000 11.075000 0.960000 2.220000\n",
" 75% 0.810000 6.000000 0.020000 13.065000 0.300000 73.810000 11.372500 1.040000 2.395000\n",
" max 1.050000 6.000000 0.030000 14.700000 0.430000 74.390000 12.130000 1.350000 2.720000\n",
"3 count 151.000000 151.000000 151.000000 151.000000 151.000000 151.000000 151.000000 151.000000 151.000000\n",
" mean 0.375762 8.006623 0.019735 7.270331 0.217881 77.930530 10.948013 0.837351 2.308013\n",
" std 0.293586 0.820542 0.007298 1.431226 0.168865 1.648155 0.825635 0.264388 0.389560\n",
" min 0.000000 7.000000 0.010000 5.100000 0.000000 73.400000 6.100000 0.150000 1.700000"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olstd = olive_oil.groupby('region').std()\n",
"olstd"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 0.741054</td>\n",
" <td> 1.529349</td>\n",
" <td> 0.507237</td>\n",
" <td> 0.398709</td>\n",
" <td> 3.451431</td>\n",
" <td> 2.106730</td>\n",
" <td> 0.079727</td>\n",
" <td> 0.111644</td>\n",
" <td> 0.083915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.475023</td>\n",
" <td> 0.404111</td>\n",
" <td> 0.138514</td>\n",
" <td> 0.176363</td>\n",
" <td> 1.418783</td>\n",
" <td> 1.072336</td>\n",
" <td> 0.053844</td>\n",
" <td> 0.118826</td>\n",
" <td> 0.007436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.820542</td>\n",
" <td> 0.825635</td>\n",
" <td> 0.264388</td>\n",
" <td> 0.389560</td>\n",
" <td> 1.648155</td>\n",
" <td> 1.431226</td>\n",
" <td> 0.168865</td>\n",
" <td> 0.293586</td>\n",
" <td> 0.007298</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
" area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"region \n",
"1 0.741054 1.529349 0.507237 0.398709 3.451431 2.106730 0.079727 0.111644 0.083915\n",
"2 0.475023 0.404111 0.138514 0.176363 1.418783 1.072336 0.053844 0.118826 0.007436\n",
"3 0.820542 0.825635 0.264388 0.389560 1.648155 1.431226 0.168865 0.293586 0.007298"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olmean=region_groupby.aggregate(np.mean)\n",
"olmean.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>palmitic</th>\n",
" <th>palmitoleic</th>\n",
" <th>stearic</th>\n",
" <th>oleic</th>\n",
" <th>linoleic</th>\n",
" <th>linolenic</th>\n",
" <th>arachidic</th>\n",
" <th>eicosenoic</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2.783282</td>\n",
" <td> 13.322879</td>\n",
" <td> 1.548019</td>\n",
" <td> 2.287740</td>\n",
" <td> 71.000093</td>\n",
" <td> 10.334985</td>\n",
" <td> 0.380650</td>\n",
" <td> 0.631176</td>\n",
" <td> 0.273220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 5.336735</td>\n",
" <td> 11.113469</td>\n",
" <td> 0.967449</td>\n",
" <td> 2.261837</td>\n",
" <td> 72.680204</td>\n",
" <td> 11.965306</td>\n",
" <td> 0.270918</td>\n",
" <td> 0.731735</td>\n",
" <td> 0.019388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 8.006623</td>\n",
" <td> 10.948013</td>\n",
" <td> 0.837351</td>\n",
" <td> 2.308013</td>\n",
" <td> 77.930530</td>\n",
" <td> 7.270331</td>\n",
" <td> 0.217881</td>\n",
" <td> 0.375762</td>\n",
" <td> 0.019735</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" area palmitic palmitoleic stearic oleic linoleic linolenic arachidic eicosenoic\n",
"region \n",
"1 2.783282 13.322879 1.548019 2.287740 71.000093 10.334985 0.380650 0.631176 0.273220\n",
"2 5.336735 11.113469 0.967449 2.261837 72.680204 11.965306 0.270918 0.731735 0.019388\n",
"3 8.006623 10.948013 0.837351 2.308013 77.930530 7.270331 0.217881 0.375762 0.019735"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"renamedict_std={k:k+\"_std\" for k in list_of_acids}\n",
"renamedict_mean={k:k+\"_mean\" for k in list_of_acids}\n",
"olstd.rename(columns=renamedict_std,inplace=True)\n",
"olmean.rename(columns=renamedict_mean,inplace=True) \n",
"olstd.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>palmitic_std</th>\n",
" <th>palmitoleic_std</th>\n",
" <th>stearic_std</th>\n",
" <th>oleic_std</th>\n",
" <th>linoleic_std</th>\n",
" <th>linolenic_std</th>\n",
" <th>arachidic_std</th>\n",
" <th>eicosenoic_std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 0.741054</td>\n",
" <td> 1.529349</td>\n",
" <td> 0.507237</td>\n",
" <td> 0.398709</td>\n",
" <td> 3.451431</td>\n",
" <td> 2.106730</td>\n",
" <td> 0.079727</td>\n",
" <td> 0.111644</td>\n",
" <td> 0.083915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.475023</td>\n",
" <td> 0.404111</td>\n",
" <td> 0.138514</td>\n",
" <td> 0.176363</td>\n",
" <td> 1.418783</td>\n",
" <td> 1.072336</td>\n",
" <td> 0.053844</td>\n",
" <td> 0.118826</td>\n",
" <td> 0.007436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.820542</td>\n",
" <td> 0.825635</td>\n",
" <td> 0.264388</td>\n",
" <td> 0.389560</td>\n",
" <td> 1.648155</td>\n",
" <td> 1.431226</td>\n",
" <td> 0.168865</td>\n",
" <td> 0.293586</td>\n",
" <td> 0.007298</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
" area palmitic_std palmitoleic_std stearic_std oleic_std linoleic_std linolenic_std arachidic_std eicosenoic_std\n",
"region \n",
"1 0.741054 1.529349 0.507237 0.398709 3.451431 2.106730 0.079727 0.111644 0.083915\n",
"2 0.475023 0.404111 0.138514 0.176363 1.418783 1.072336 0.053844 0.118826 0.007436\n",
"3 0.820542 0.825635 0.264388 0.389560 1.648155 1.431226 0.168865 0.293586 0.007298"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"olpalmiticmean = olmean[['palmitic_mean']] \n",
"olpalmiticstd = olstd[['palmitic_std']] \n",
"newolbyregion=olpalmiticmean.join(olpalmiticstd)\n",
"newolbyregion"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>palmitic_mean</th>\n",
" <th>palmitic_std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 13.322879</td>\n",
" <td> 1.529349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 11.113469</td>\n",
" <td> 0.404111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 10.948013</td>\n",
" <td> 0.825635</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" palmitic_mean palmitic_std\n",
"region \n",
"1 13.322879 1.529349\n",
"2 11.113469 0.404111\n",
"3 10.948013 0.825635"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"eico=(olive_oil.eicosenoic < 0.05)\n",
"eico"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"10 False\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"...\n",
"557 True\n",
"558 True\n",
"559 True\n",
"560 True\n",
"561 True\n",
"562 True\n",
"563 True\n",
"564 True\n",
"565 True\n",
"566 True\n",
"567 True\n",
"568 True\n",
"569 True\n",
"570 True\n",
"571 True\n",
"Name: eicosenoic, Length: 572, dtype: bool"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_data = pd.DataFrame({'Bigdata' : [12, 34, 99, 45, 13], \\\n",
"'Examiner' : [0.9, 0.8, 0.7, 0.6, None], 'Data science' \\\n",
": ['L', 'M', None, 'c', 'a']})\n",
"new_data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bigdata</th>\n",
" <th>Data science</th>\n",
" <th>Examiner</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 12</td>\n",
" <td> L</td>\n",
" <td> 0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 34</td>\n",
" <td> M</td>\n",
" <td> 0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 99</td>\n",
" <td> None</td>\n",
" <td> 0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 45</td>\n",
" <td> c</td>\n",
" <td> 0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 13</td>\n",
" <td> a</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
" Bigdata Data science Examiner\n",
"0 12 L 0.9\n",
"1 34 M 0.8\n",
"2 99 None 0.7\n",
"3 45 c 0.6\n",
"4 13 a NaN"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_data.dropna()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bigdata</th>\n",
" <th>Data science</th>\n",
" <th>Examiner</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 12</td>\n",
" <td> L</td>\n",
" <td> 0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 34</td>\n",
" <td> M</td>\n",
" <td> 0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 45</td>\n",
" <td> c</td>\n",
" <td> 0.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" Bigdata Data science Examiner\n",
"0 12 L 0.9\n",
"1 34 M 0.8\n",
"3 45 c 0.6"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = pd.DataFrame([1., None, 3.5, None, 7])\n",
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
" 0\n",
"0 1.0\n",
"1 NaN\n",
"2 3.5\n",
"3 NaN\n",
"4 7.0"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean = data.mean()\n",
"data.fillna(mean)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 3.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
" 0\n",
"0 1.000000\n",
"1 3.833333\n",
"2 3.500000\n",
"3 3.833333\n",
"4 7.000000"
]
}
],
"prompt_number": 31
}
],
"metadata": {}
}
]
}
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