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alexpearce/nb.ipynb

Last active January 30, 2017 16:54
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Lil' notebook.
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nb.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data manipulation with numpy and pandas\n",
"\n",
"A notebook for some of the code shown during Alex's presentation at [the DIANA meeting](http://indico.cern.ch/event/596268/)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RangeIndex(start=0, stop=96, step=1)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generation\n",
"data = np.random.normal(loc=20, scale=10, size=100) # Selections\n",
"selection_mask = data > 0\n",
"# Pandas\n",
"df = pd.DataFrame(dict(pion_pt=data[selection_mask]))\n",
"df.index"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(21.070209199862475, 9.2392323518739872)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.pion_pt.mean(), df.pion_pt.std()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pion_pt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.01</th>\n",
" <td>5.416159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.99</th>\n",
" <td>42.073820</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pion_pt\n",
"0.01 5.416159\n",
"0.99 42.073820"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.quantile([0.01, 0.99])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 (5, 15]\n",
"1 (15, 100]\n",
"2 (5, 15]\n",
"3 (15, 100]\n",
"4 (15, 100]\n",
"5 (5, 15]\n",
"6 (15, 100]\n",
"7 (15, 100]\n",
"8 (15, 100]\n",
"9 (15, 100]\n",
"10 (15, 100]\n",
"11 (5, 15]\n",
"12 (15, 100]\n",
"13 (15, 100]\n",
"14 (5, 15]\n",
"15 (5, 15]\n",
"16 (15, 100]\n",
"17 (15, 100]\n",
"18 (15, 100]\n",
"19 (5, 15]\n",
"20 (5, 15]\n",
"21 (15, 100]\n",
"22 (5, 15]\n",
"23 (15, 100]\n",
"24 (5, 15]\n",
"25 (15, 100]\n",
"26 (5, 15]\n",
"27 (5, 15]\n",
"28 (15, 100]\n",
"29 (5, 15]\n",
" ... \n",
"66 (15, 100]\n",
"67 (15, 100]\n",
"68 (15, 100]\n",
"69 (15, 100]\n",
"70 (15, 100]\n",
"71 (5, 15]\n",
"72 (5, 15]\n",
"73 (15, 100]\n",
"74 (15, 100]\n",
"75 (15, 100]\n",
"76 (15, 100]\n",
"77 (15, 100]\n",
"78 (5, 15]\n",
"79 (5, 15]\n",
"80 (15, 100]\n",
"81 (15, 100]\n",
"82 (15, 100]\n",
"83 (5, 15]\n",
"84 (5, 15]\n",
"85 (15, 100]\n",
"86 (15, 100]\n",
"87 (15, 100]\n",
"88 (15, 100]\n",
"89 (15, 100]\n",
"90 (5, 15]\n",
"91 (15, 100]\n",
"92 (15, 100]\n",
"93 (5, 15]\n",
"94 (5, 15]\n",
"95 (15, 100]\n",
"Name: pion_pt, dtype: category\n",
"Categories (3, object): [(0, 5] < (5, 15] < (15, 100]]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.assign(kaon_pt=2*df.pion_pt)\n",
"cut = pd.cut(df.pion_pt, [0, 5, 15, 100])\n",
"cut"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pion_pt\n",
"(0, 5] 7.237355\n",
"(5, 15] 20.844622\n",
"(15, 100] 51.109383\n",
"Name: kaon_pt, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_kaon_pt = df.kaon_pt.groupby(cut).mean()\n",
"mean_kaon_pt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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G8ufTSlcf46lzmjnLCVX7LP/ZupfMnFzeXVPA4YpqesdFc9fZg5iemkS/+A4t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\nSB3/D3V5wwbNZx/8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1108ec8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mean_kaon_pt.plot()\n",
"plt.savefig('pandas_plotting1.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x110961d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = df.assign(is_background=np.random.choice([0, 1], size=len(df)))\n",
"df.pion_pt.hist(by=df.is_background)\n",
"plt.savefig('pandas_plotting2.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>kaon_pt</th>\n",
" <th>is_background</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>28.569002</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>25.833684</td>\n",
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" <th>3</th>\n",
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" <td>52.372494</td>\n",
" <td>1</td>\n",
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" <th>5</th>\n",
" <td>13.068480</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>44.801066</td>\n",
" <td>1</td>\n",
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" <th>8</th>\n",
" <td>52.639216</td>\n",
" <td>0</td>\n",
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" <th>9</th>\n",
" <td>73.019071</td>\n",
" <td>0</td>\n",
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" <td>83.835997</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>17.193063</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>36.448640</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>49.592644</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>18.357084</td>\n",
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" <td>1</td>\n",
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" <th>16</th>\n",
" <td>62.834707</td>\n",
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" <th>17</th>\n",
" <td>51.965297</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>46.662708</td>\n",
" <td>0</td>\n",
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" <th>20</th>\n",
" <td>24.643522</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>56.386886</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>25.591236</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>46.406459</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>19.372286</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>49.864541</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20.639196</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>17.653133</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>35.124794</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>25.292786</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>30.758337</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>52.981138</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>34.790661</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>42.753563</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>38.518155</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>71</th>\n",
" <td>22.994884</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>18.034684</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>73</th>\n",
" <td>38.380549</td>\n",
" <td>1</td>\n",
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" <th>74</th>\n",
" <td>82.611995</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>41.747976</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>41.848248</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>71.975208</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>19.063019</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>13.310450</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>32.552022</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>54.656309</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>44.540749</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>18.574873</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>29.552915</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>40.152165</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>58.546651</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>59.585978</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>35.320041</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>54.985714</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>28.144450</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>62.766003</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>53.433779</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>11.021526</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>14.928735</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>39.465356</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>96 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" kaon_pt is_background\n",
"0 28.569002 0\n",
"1 60.393369 1\n",
"2 25.833684 1\n",
"3 54.030633 1\n",
"4 52.372494 1\n",
"5 13.068480 1\n",
"6 78.056473 1\n",
"7 44.801066 1\n",
"8 52.639216 0\n",
"9 73.019071 0\n",
"10 83.835997 1\n",
"11 17.193063 0\n",
"12 36.448640 0\n",
"13 49.592644 0\n",
"14 18.357084 1\n",
"15 27.076739 1\n",
"16 62.834707 0\n",
"17 51.965297 1\n",
"18 46.662708 0\n",
"19 24.140616 1\n",
"20 24.643522 0\n",
"21 56.386886 0\n",
"22 25.591236 1\n",
"23 46.406459 0\n",
"24 19.372286 0\n",
"25 49.864541 0\n",
"26 20.639196 0\n",
"27 17.653133 0\n",
"28 35.124794 0\n",
"29 25.292786 0\n",
".. ... ...\n",
"66 30.758337 0\n",
"67 52.981138 0\n",
"68 34.790661 1\n",
"69 42.753563 0\n",
"70 38.518155 0\n",
"71 22.994884 0\n",
"72 18.034684 1\n",
"73 38.380549 1\n",
"74 82.611995 1\n",
"75 41.747976 1\n",
"76 41.848248 0\n",
"77 71.975208 1\n",
"78 19.063019 1\n",
"79 13.310450 1\n",
"80 32.552022 0\n",
"81 54.656309 0\n",
"82 44.540749 1\n",
"83 18.574873 1\n",
"84 29.552915 0\n",
"85 40.152165 0\n",
"86 58.546651 0\n",
"87 59.585978 1\n",
"88 35.320041 1\n",
"89 54.985714 1\n",
"90 28.144450 1\n",
"91 62.766003 1\n",
"92 53.433779 1\n",
"93 11.021526 0\n",
"94 14.928735 0\n",
"95 39.465356 1\n",
"\n",
"[96 rows x 2 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.columns[1:]]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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VTnN9muxpajOzXJVLf3flpcDrU2/TXL8ZEedI+g/SftSVIuK9hUVmZmal6q0F0blr3NeL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\nMDOzXE4QZmaWywnCzMxyOUGYmVmu/wWIzfQadPuudwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11325e390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data1 = np.random.normal(loc=1, size=10000)\n",
"data2 = np.random.normal(loc=-1, size=10000)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"plot_kwargs = dict(bins=100, range=(-5, 5), alpha=0.5)\n",
"ax.hist(data1, label='Data $x$', **plot_kwargs)\n",
"ax.hist(data2, label='Data $y$', **plot_kwargs)\n",
"ax.set_xlabel('Vertex $x$ position [mm]')\n",
"ax.set_ylabel('Candidates')\n",
"ax.legend()\n",
"fig.savefig('histograms.pdf');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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