Mercurial > hg > from-my-pen-to-your-ears-supplementary-material
view results/data/saves/plots.ipynb @ 1:eb3b846ae0ef tip
second commit
| author | Emmanouil Theofanis Chourdakis <e.t.chourdakis@qmul.ac.uk> |
|---|---|
| date | Wed, 16 May 2018 18:13:41 +0100 |
| parents | 4dad87badb0c |
| children |
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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import glob\n", "import pandas as pd\n", "\n", "from matplotlib import rc\n", "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n", "## for Palatino and other serif fonts use:\n", "#rc('font',**{'family':'serif','serif':['Palatino']})\n", "rc('text', usetex=True)\n", "\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "page1_df = pd.read_csv('ratings/page1-default-ratings.csv').set_index('file_keys')\n", "page2_df = pd.read_csv('ratings/page2-default-ratings.csv').set_index('file_keys')\n", "page3_df = pd.read_csv('ratings/page3-default-ratings.csv').set_index('file_keys')\n", "page4_df = pd.read_csv('ratings/page4-default-ratings.csv').set_index('file_keys')\n", "page5_df = pd.read_csv('ratings/page5-default-ratings.csv').set_index('file_keys')\n", "page6_df = pd.read_csv('ratings/page6-default-ratings.csv').set_index('file_keys')\n", "page7_df = pd.read_csv('ratings/page7-default-ratings.csv').set_index('file_keys')\n", "page8_df = pd.read_csv('ratings/page8-default-ratings.csv').set_index('file_keys')\n", "page9_df = pd.read_csv('ratings/page9-default-ratings.csv').set_index('file_keys')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a0000</th>\n", " <th>a0011</th>\n", " <th>a1011</th>\n", " <th>a1101</th>\n", " <th>a1110</th>\n", " <th>a1111</th>\n", " </tr>\n", " <tr>\n", " <th>file_keys</th>\n", " 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<td>0.00</td>\n", " <td>0.70</td>\n", " <td>0.33</td>\n", " <td>0.58</td>\n", " <td>0.57</td>\n", " <td>0.36</td>\n", " </tr>\n", " <tr>\n", " <th>OK2yLWD54tr3klqiS631VXjQlroZfmD3</th>\n", " <td>0.06</td>\n", " <td>0.03</td>\n", " <td>0.52</td>\n", " <td>1.00</td>\n", " <td>0.68</td>\n", " <td>0.89</td>\n", " <td>0.00</td>\n", " <td>0.03</td>\n", " <td>0.95</td>\n", " <td>1.00</td>\n", " <td>...</td>\n", " <td>0.66</td>\n", " <td>0.86</td>\n", " <td>0.50</td>\n", " <td>1.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.88</td>\n", " <td>0.51</td>\n", " <td>0.38</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>t1Ybl1O1EAdpBEbxOLbaU4QlHtAcGCxa</th>\n", " <td>0.09</td>\n", " <td>0.11</td>\n", " <td>1.00</td>\n", " <td>0.99</td>\n", " <td>0.65</td>\n", " <td>0.54</td>\n", " <td>0.00</td>\n", " <td>0.15</td>\n", " <td>0.48</td>\n", " <td>0.76</td>\n", " <td>...</td>\n", " <td>1.00</td>\n", " <td>0.51</td>\n", " <td>0.24</td>\n", " <td>0.76</td>\n", " <td>0.23</td>\n", " <td>0.00</td>\n", " <td>0.50</td>\n", " <td>0.74</td>\n", " <td>0.25</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>JZaBaWCeuGjSAyoUrfwdASaIHXkHCZml</th>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.71</td>\n", " <td>0.83</td>\n", " <td>0.84</td>\n", " <td>0.93</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>0.89</td>\n", " <td>0.39</td>\n", " <td>0.69</td>\n", " <td>1.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.90</td>\n", " <td>0.41</td>\n", " <td>0.68</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o</th>\n", " <td>0.23</td>\n", " <td>0.23</td>\n", " <td>0.62</td>\n", " <td>1.00</td>\n", " <td>0.79</td>\n", " <td>0.84</td>\n", " <td>0.09</td>\n", " <td>0.10</td>\n", " <td>0.77</td>\n", " <td>1.00</td>\n", " <td>...</td>\n", " <td>0.67</td>\n", " <td>1.00</td>\n", " <td>0.49</td>\n", " <td>0.91</td>\n", " <td>0.06</td>\n", " <td>0.00</td>\n", " <td>0.76</td>\n", " <td>0.90</td>\n", " <td>0.64</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT</th>\n", " <td>0.11</td>\n", " <td>0.24</td>\n", " <td>0.69</td>\n", " <td>1.00</td>\n", " <td>0.66</td>\n", " <td>0.83</td>\n", " <td>0.12</td>\n", " <td>0.11</td>\n", " <td>0.77</td>\n", " <td>0.92</td>\n", " <td>...</td>\n", " <td>1.00</td>\n", " <td>0.12</td>\n", " <td>0.50</td>\n", " <td>0.75</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>1.00</td>\n", " <td>0.24</td>\n", " <td>0.76</td>\n", " <td>0.51</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>17 rows × 48 columns</p>\n", "</div>" ], "text/plain": [ " a0000 a0011 a1011 a1101 a1110 a1111 \\\n", "file_keys \n", "IXHrDvdwYoDCO7bvwRMB7jD46hoWAjDi 0.06 0.13 0.68 0.71 0.65 0.65 \n", "iWogwaa7GooHWHcp8C0ZjRrTMDgcae0t 0.00 0.04 0.95 1.00 0.80 0.95 \n", "bOyRMU1QCcMqMpcjcHNBuVMF45oksQMD 0.24 0.24 0.76 0.75 1.00 0.76 \n", "U6mgdJX1DgAfYJ6DR7sKY0CL4YgcwZKq 0.00 0.00 0.83 0.97 0.86 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"OK2yLWD54tr3klqiS631VXjQlroZfmD3 0.86 0.50 1.00 0.00 0.00 \n", "t1Ybl1O1EAdpBEbxOLbaU4QlHtAcGCxa 0.51 0.24 0.76 0.23 0.00 \n", "JZaBaWCeuGjSAyoUrfwdASaIHXkHCZml 0.39 0.69 1.00 0.00 0.00 \n", "uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o 1.00 0.49 0.91 0.06 0.00 \n", "lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT 0.12 0.50 0.75 0.00 0.00 \n", "\n", " b10113 b11013 b11103 b11113 \n", "file_keys \n", "IXHrDvdwYoDCO7bvwRMB7jD46hoWAjDi 0.66 0.95 0.56 0.85 \n", "iWogwaa7GooHWHcp8C0ZjRrTMDgcae0t 0.82 0.86 0.89 0.49 \n", "bOyRMU1QCcMqMpcjcHNBuVMF45oksQMD 0.50 1.00 0.76 0.75 \n", "U6mgdJX1DgAfYJ6DR7sKY0CL4YgcwZKq 0.70 0.97 0.44 0.83 \n", "9UODgpqx7pTDhiiLm7ds39wh59aYBrHK 0.91 1.00 0.39 0.84 \n", "VyX492RQzqQXRL84PByL9pLt8C5p4c50 0.98 0.59 0.86 0.92 \n", "bkHwhN78d7k2kIanOievgityZQD7gVOr 0.60 0.84 0.46 0.94 \n", "bKhIcFzfZ3YXPxNiw4E7kFyoTFAqeFTp 0.09 0.49 0.50 0.00 \n", "ufohn9b31ZNI2zLGNqmmRYOMtIwiw6o1 1.00 0.79 0.17 1.00 \n", "fZGbsBmuEtrB35G2IzQu0IxOCVoljWpz 0.99 0.10 0.35 0.63 \n", "YMVW8vVq8kZbGFRMWn9ElJBJ38bVV5GU 0.71 0.95 0.98 0.87 \n", "G2WO7k3tSjvBrlMG1Nqz4DsKBO8oaEcQ 0.33 0.58 0.57 0.36 \n", "OK2yLWD54tr3klqiS631VXjQlroZfmD3 0.88 0.51 0.38 1.00 \n", "t1Ybl1O1EAdpBEbxOLbaU4QlHtAcGCxa 0.50 0.74 0.25 1.00 \n", "JZaBaWCeuGjSAyoUrfwdASaIHXkHCZml 0.90 0.41 0.68 1.00 \n", "uoBPmOWdbNI4uowtBZRZK8BIEUVvUn1o 0.76 0.90 0.64 1.00 \n", "lIOWKvmCLdlUhGFYwE3lOfizSeqtxyNT 1.00 0.24 0.76 0.51 \n", "\n", "[17 rows x 48 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "big_results = page1_df.join(page2_df).join(page3_df).join(page4_df).join(page5_df).join(page6_df).join(page7_df).join(page8_df)\n", "big_results" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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7546TefR05U1V0p4kmdlq0u7IOTewA9bMrkt6TJIHgGLlqaPfkrRtZv0Dpm6lauh31Bvh76SfYGbvqTeiXzWz5xw0BQDFsRAO2KlWq+7w8LDoMACgVMzsiXOumtWOc90AQORI9AAQORI9AESORA8AkSPRA0DkSPQAEDkSPQBEjkQPAJEj0QNA5Ej0ABA5Ej0ARI5EDwCRI9GjFNrtttbW1nThwgWtra2p3W4XHRJQGnlOUwwUqt1uq9FoqNVqqVarqdPpqF6vS5Ju3rxZcHRA+DhNMYK3trame/fuaWNj43TZwcGBdnd39etf/7rAyIBi5T1NMYkewbtw4YL++Mc/6uLFi6fLTk5O9PLLL+tPf/pTgZEBxeJ89IhGpVJRp9MZWNbpdFSpVAqKCCgXEj2C12g0VK/XdXBwoJOTEx0cHKher6vRaBQdGlAK7IxF8Po7XHd3d9XtdlWpVNRsNtkRC+TEHD0AlBRz9AAASSR6AIhe8ImeIyIBYDZB74z1eUSkmY1cHsI+CgCYp6BH9M1mU++88452d3f18ssva3d3V++8846azebUfTnnTm+v7314eh8AYhf0iP43v/mNfv/7358Z0X/66adFh4YF4xfZ4vnc5qP64r1bnKBH9C+99JLeffddbWxs6OLFi9rY2NC7776rl156qejQsGCjfo2RKObL5zbn13Sxgk70X331le7duzdwROS9e/f01VdfFR0aAJRG0FM33/rWt/T9739/4IjIH/7wh/r5z39edGgAUBqZid7MViRtSzqStCpp3zn3dEzbdUmbyZ9/Iem+c27/vME1Go2RVTfn2RkLAMsqz4j+oaQd59yRJJnZYzPbcs4dj2i76Zy7m7RbkfT/zOzauC+GLJzjpPza7baazebp+9doNHj/gAWbmOiTZL3aT/KJI/VG7Y+G2q5Lel/SXUlyzh2b2WHS9lyJXuolexJDOXFlKCAMWTtjq5KGR+7Hkq4PN0xG7VtDi1dHPB9LotlsqtVqDVRNtVotpt6ABcuaulmR9Hxo2TP1EvgZ6fl4M1uVdEnSP49qa2bb6s3968qVKznDnd63//7f9OUfTs4sv3r7o9P7r37ton7147fmFsOy6na7qtVqA8tqtZq63W5BEQHLKc8c/aVz9n1f0rUxc/lyzj2Q9EDqnab4nK+R6cs/nOjTD747sU066cOf/pWh0td65cpQwOJlTd0cqzeqT3tNZ0f5A8zsPUl7590JizhwZSggDFkj+kOdHdGvSHo87glmdkOpEkwzWyfhLyeqpoAwTEz0/coZM0tX3lQl7Umn8/BKlV5uSjpOJfnVpD2JfklRNQUUL88c/ZakbTPrHzB1KzXvvqPeCH8nSeqPpTMnMHrTX7gAgGllJvokqd8d89he6v6RpNGnuwMAFCbok5oBAGZHogeAyAV99koA5ZXnYEWJAxYXgUQPYC7yHKwoccDiIjB1AwCRI9EDQOSYugEwYNTcOvPq5Uaix9wMHTh3igtDh40TAcaHqRvMjXPu9Pb63oen9wEsFokeACJHogeAyAU/R88873JjxyAwu+ATfT+hX739Ua6DL5ZZu91Ws9k8Pfd7o9Eo/SmC2TEIzC74RD+rr1du68//6XZGG0kq95dIu91Wo9FQq9VSrVZTp9NRvV6XpNInewCziT7R/677wVKMCJvNplqt1un1WTc2NtRqtbS7u0uiB5ZcsImeudnpdLtd1Wq1gWW1Wk3dbregiLDs8vya7rWTyv6LOnTBJnrmZqdTqVTU6XROR/SS1Ol0VKlUCowKyyzPr2mJ/8eLQHllJBqNhur1ug4ODnRycqKDgwPV63U1Go2iQwNQsGBH9JhOfx5+d3f3tOqm2WwyPw+ARB+TmzdvBpHY81xwgv0rwOKQ6OEd+1eAsASb6Jel/h0A5i3YRL8s9e8AMG9U3QBA5Ej0ABA5Ej0ARI5EDwCRI9EDQOQyq27MbEXStqQjSauS9p1zT2dtC+RBmS0wuzzllQ8l7TjnjiTJzB6b2ZZz7njGtkAmymwXjy/X+ExM9MkIfbWfuBNHkjYlPTpvWwDh4ss1Pllz9FVJw6PxY0nXZ2wLAFiQrES/Iun50LJnki7N2BYAsCB55uinSdS525rZtno7bnXlypWRba7e/kif3fneyMde3/tQr37tYq7X8tUP8vE9x8v7t3g+/+9JGtnX63sfShLv3wKYc278g2abku47595ILbsjacU5t3PetsOq1ao7PDw85yoAwHIysyfOuWpWu6ypm0OdHaWvSHo8Y1sAwIJMTPRJWeShma2mFlcl7UuSma32H8tqCwAoRp45+i1J22bWPwjqVqoufke9UftOjrYAgAJkJvokUd8d89he3rYAgGJwrhsAiByJHgAiR6IHgMhNrKNfWBBmn0v6LKPZNyT9h6eX9NVXiDH57IuYFt8XMS2+rzLH9Lpz7nJWoyASfR5mdpjnwIBF9hViTD77IqbF90VMi+8r9pgkpm4AIHokegCIXJkS/YMA+woxJp99EdPi+yKmxfcVe0zlmaMHAJxPmUb0AIBzINEDQORI9AAQuTxnrwSmYmavqHdR+Ot6cY2C5+pdm2DfOfefRcUWkhC3U4gxhapM2yrIEb2ZvWJmf2VmPzWznyW3nybLXik6vlCEuJ3M7DuSHkl6S9KX6l2Q5jC5/5akX5jZf/f0Wj/y0Y/PvvL2s8jtlDeuEGNaZD/T9FW2z3lwI/pkA96RdCTpOPlXkl5TbwO+b2a3nHP/19Pr/cg59w+h9JO3r4C30zXn3FtZfUmaGJeZ/WWO17ouKTMmX335jEmetpPnuIKLKcTPQSK4z/kkwSV68WHL21dw2ylxlN0kV5u/kfRNSV9MaPNmjn589uUzJl/bSfIXV4gxhfg5kML8nI8VYqLnw1be/5SS9IaZXXXOfTrqQTO7KumNUY8N+YmkS865X4xrYGZ/lzMmX335jMnXdvIZV4gxhfg5kML8nI8VYqLnw1be/5RS74i+h2b2TfWmlJ6rt6NqJfn3UL1LTk7knPvYzK5lNMt1PWJfffmMSZ62k+e4gospxM9BIrjP+STBHRlrZq9KeqjeCHPsBsy7R9vMrmUksO845z5eVD+++gp1Ow3FV03iURLjoXPuy7x9LIMQt1OIMYWqLNsquETfV5YNWLQQt1OZys6mFfO6Saxf2ddvnCATfexvRszrN6Ia6Fny0GvqfRm9KclLNVAAlU5zW7e8Mfnsq6zrl7ef2NdvkuDm6AMuG/TSzyLXb9GJIhFc2VnslU4xr1+IJZE+46K8coKSvxkxJ0IpzLKz2CudYl6/EEsipTDXb6wQE33sb0bMiVAKs+ws9kqnmNcvxJJIn3FRXjnqwQjejJgToRRg2VnM5Yee+wpu/UIsifQZF+WVgZUNeuzH2/qFVvI51D64aiBfYl43ifWLUXCJvi/2NyPm9fNVVeSzOinmSiefQtzmvHezCzLR82ErL18lbD5L4WIu+fTZV4jbvKwlkT77orxyQX3FXvLpuS9fVVPeqq989RVqpVOI5ZUe+wquCs9nX5RXTsCHLcwPbcJXVZHP6qvYK51CLK8M8XMQ4janvHLGNj77CvHDFuKHVvJXVeSz+ir2SqcQyytD/ByEuM0prxz1IB827/347stXCZu3UjhffQVaEhlkeaXHvoIrifTZF+WVfsoPvfQVe8mn776S9l6qinxWJ8Vc6eRTiNuc9242wSX6Pj5s5UWlU3mFWPGG2QV7cXBJ1yTdkPSD5HZD0jWb8qLXPvtyzn3pnPuFc+5fktsvSPKDzNNFk331M0Xcpb1Qta++fG7zRb5/Zd7mi+onuDn6EMsrc8YdZf3tOfqi0onySm99BbqdgvxMTRJcohcftlx9hRhTgkqnxcbks68QK95C3E4++6K8csY2PvuK/QMSe3ll7JVOMZdXhridfPZFeeWoB/mwBR2TFHFZXYjleZ77Cq68MtDtFORnapLgqm5CLK9M+gqulDHEmFLtqXQqqRAr3jCb4BJ935gPyG/VG3nmTji++xrT9zc97dD10ldoMZnZ/1Tvl9Mnzrn/XXQ/scfkuy+UX5DlldLYUsbPJH2RfIgL6WtU35KOZ+3HZ1+hxGRm3zSz36r3E/2/SXrLzH6ZTJktvJ+hvn7gKaaZ+gm1LzP7TvK8V1LL/vqcMXnpK8SYfPblM6YznHPB3iS9Iul/SfpXSb9M3X5aVF/ENFVfPxqz/CdF9BN7TJ7X75qk74xY/lfniMlLXyHGFOr6Dd9C3Bmbdle9o+iO1DvCbl/SqqSnBfZFTPmNmxY7LKgfn32FGJPPvl51E/bZFNRXiDH57MtnTAOCnbpJPHG9qZaPJX3hnPvYOfcv6u1cLaovYsrv1XM8Z579+OwrxJh89vU/zOzr6QXJFMLE41Lm3FeIMfnsy2dMA0If0T83s287534l6c/M7OvOud+pN8L894L6IqYx7Oyh2n9mZm9r8FiFVfXKOefeT+wx+e5ryH1JH5vZF3pRrSb1phem5auvEGPy2ZfPmAYEW3UjnZ7C4KGkdUkm6Yl6B/UcOuf+toi+iGnic3+r3oc1yyfOuf8z735ij8l3X2P6/2slU3ezTiv46ivEmHz25TOm0z5DTvTDLCmT9LHyvvoipoG2E2vxp3hNL/3EHpPvvhCvUiV6AMD0Qt8ZCwCYEYkeACJHogeAyJHoASByJHoAiByJHgAi9/8BHHONpjKz+7oAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc097d92ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "char_df = page1_df.join(page2_df).join(page3_df)\n", "\n", "immers_df = page4_df.join(page5_df).join(page6_df)\n", "renamedict = {k: k[:-1] for k in immers_df.columns}\n", "immers_df = immers_df.rename(columns=renamedict)\n", "\n", "prefer_df = page7_df.join(page8_df).join(page9_df)\n", "renamedict = {k: k[:-1] for k in prefer_df.columns}\n", "prefer_df = prefer_df.rename(columns=renamedict)\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "char_df.boxplot(grid=False, rot=90,fontsize=15)\n", "plt.title('Character Identification')\n", "plt.savefig('characterid.pdf', dpi=300)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc097de89e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "immers_df.boxplot(grid=False, rot=90,fontsize=15)\n", "plt.title('Immersion')\n", "plt.savefig('immersion.pdf', dpi=300)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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17cYBAIxXvxn9to7O6Gck3e8w9rSk+ZYlmyVJp82sOXsH0EG/T5R0X2FYPQu9u++Z2baZtXbezEvakCQzm83G7br7oQOwZnZF0n2KPNBd+7Ii3VcYhTxr9MuSVs2secLUtZYe+jU1ZvhrrT9gZm+rMaOfNbOnnDQFAMXpW+izon6zy3MbXR6/2e1nAADjxbVuACBxXL0SSEj7+SvNy0xzRdLpRqEHEkJBRyfRF/puZ9iyQwNAPtGv0bu73F0vb3xw8HeKPADkF32hBwAMh0IPAImj0ANA4ij0AJA4Cj0AJI5CDwCJi76PHgCmRet5Q82zmqXhzxui0AMYiTy38ZS4lWerUZ0jRKEHMBJ5buMpcSvPcWCNHgASR6EHgMRR6AEgcazRJ4CbSwPohUI/4Tod7OIG0wBasXQDAImj0ANA4ij0AJA41uiPoVarqVqtamdnR6VSSZVKRSsrK0WnBWDMJqURgkI/oFqtpkqlos3NTS0sLKher6tcLksSxR6YIpPUCMHSzYCq1areeustra+v69SpU1pfX9dbb72larVadGoA0BEz+gH97ne/05///Gf97Gc/O5jR/+AHP9CTJ0+KTg0IqvVKiq3yXnjr+dJ1vfbz6znGSVJ8s+CUUOgH9PWvf13r6+taXFyUJC0uLmp9fV0/+tGPCs4sHI5BQPqqoB93OeJPO+9yUbNIUOgH9OzZM73//vt6/fXXD2b077//vp49e1Z0akGEPAYx7IwQaNe+TzWv2c4+1Rtr9AN69dVXO67Rv/rqq0WnFkS1WtXm5qYWFxd18uRJLS4uanNz81jHINz94OvljQ8O/g4cV+s+1fqF3vrO6M1sRtKqpF1Js5IeuPvjLmMvSlrKvv22pFvu/iBQrlGoVCodZ7ypHIzd2dnRwsLCoccWFha0s7NTUEYAhpVn6eaupDV335UkM7tvZsvuvtdh7JK738zGzUj6NzO73O0XwyRaWVnRr3/9a333u9/VX/7yF33jG9/QtWvXklnDLpVKqtfrB8cgJKler6tUKhWST6e7FHGHImAwPQt9Vqxnm0U+s6vGrP1e29iLkt6RdFOS3H3PzLazsYUV+jxH/gc56l+r1fThhx/ql7/85aEZ/Xe+850kin2lUlG5XI7mE0ueuxRxMA/ord+Mfl5S+8x9T9IVtRV6d39sZsttY2c7/PxY5TnyP0ihaF3DlnSwhr2+vp5EoW++hvX19YOum2q1msRrA6ZVv0I/I+lp22OfqFHAj2hdjzezWUmnJf2vTmPNbFWNtX+dPXs2Z7rFm4Y17JWVFQo7kJA8XTenjxn7lqTLXdby5e633X3e3efPnDlzzH9i/Jpr2K2KXMMGgH76Ffo9NWb1rV7S0Vn+IWb2tqSNlA7CNjXXsLe2trS/v6+trS2Vy2VVKpWiUwOAjvot3Wzr6Ix+RtL9bj9gZlfV0oJpZhePU/Bj7bZgDRvApOlZ6JudM2bW2nkzL2lDOliHV0vr5ZKkvZYiP5uNH7jQx9xtwRo2gEmSp49+WdKqmTVPmLrWsu6+psYMfy0r6velI6cpXwqXLiZBp09j0uFfzPS+A+PTt9BnRf1ml+c2Wv6+K6nzxU0wVWL+NAZMI651AwCJo9ADQOIo9ACQOK5HD+CQWFubcXwUegCHcDA9PRR6ACOT5xfCi8+dHEMm041CD2AkOn0qOO79ZzEcCj2AqdR6Ymfz3rNSmvefpdADmEopFvRuaK8EgMRR6AEgcVOxdNPvyD9H/QGkLPlCz5F/ANMu+UIPjErb5bgPOjem6SAfJgOFHjgmCjomBYUeU6dT/zRFGymj0GPqUNQxbSj0BWN2CWDUKPQFo6gDGDUKPYBDni9d12s/v95njCTRojwpKPQADvnTzrtcjz4xXAIBABJHoQeAxFHoASBxFHoASByFHgASR6EHgMRR6AEgcX376M1sRtKqpF1Js5IeuPvjYccCAMYjzwlTdyWtufuuJJnZfTNbdve9IccCAMag59JNNkOfbRbuzK6kpWHGAhidWq2mubk5nThxQnNzc6rVakWnhJxG9d71m9HPS2qfje9JuiLp3hBjgVy47spgarWaKpWKNjc3tbCwoHq9rnK5LElaWVkpODv0Msr3rl+hn5H0tO2xT9RYfx9mLJAL110ZTLVa1ebmphYXFyVJi4uL2tzc1Pr6OoU+cqN876zXZXLN7Kqkd9z9Ustjb0v6trsvH3ds9tyqGgdudfbs2UtPnjw59PxrP38t1wv41//yr7nGtd/fsymVywTH9PpCvnfNIv7kxt90fP7ljQ/04nMn9dsfv5E/wQiFev9OnDihL774QidPnjx4bH9/X6dOndKXX36ZK8Yotnmn18f/vcOO896Z2SN3n+8Xu9+Mfk+NmXqrl3R05j7oWLn7bUm3JWl+fv7IFslbwPNKZafqJqbXF/K9O5jNvxvP6xuFUO9fqVRSvV4/mBVKUr1eV6lUyh1jFNs8pv0ztJjeu2769dFvSzrd9tiMpPtDjgUwApVKReVyWVtbW9rf39fW1pbK5bIqlUrRqaGPkb537t7zS41CPdvy/SNJM9nfZ9ue6zq219elS5ccQBh37tzxCxcu+Ne+9jW/cOGC37lzp+iUkNOg752kbe9TX9299xq91PskKDO7kRXytX5je5mfn/ft7e1Bfj8BwNQLtUYvb5zsdLPLcxt5xwIAisG1bgAgcRR6AEgchR4AEtf3YOxYkjD7o6QnfYZ9U9K/B/onQ8WKMaeQschp/LHIafyxJjmnl939TL9BURT6PMxsO8/R5XHGijGnkLHIafyxyGn8sVLPSWLpBgCSR6EHgMRNUqG/HWGsGHMKGYucxh+LnMYfK/WcJmeNHgBwPJM0owcAHAOFHgASR6EHgMT1vagZMCgze0GNm8Jf0Vf3KHiqxmWsH7j7/ysqt5jEuJ1izClWk7StopzRm9kLZva3ZvZTM/tF9vXT7LEXis4vFjFuJzN7XY2bwb8h6TM1bkiznf39DUm/MrP/FOjf+mGIOCFj5Y0zzu2UN68YcxpnnEFiTdp+Ht2MPtuAN9S4pv1e9qfUuC3hG5LeMbNr7v5/A/17P3T3/xlLnLyxIt5Ol929581Esx23Z15m9tc5/q0rkvrmFCpWyJwUaDsFziu6nGLcDzLR7ee9RFfoxc6WN1Z02ymz239IrjH/TdJ5SZ/2GHOpx3OjiBUyp1DbSQqXV4w5xbgfSHHu513FWOjZ2Sb3P6UkvWJm59z9D52eNLNzkl7JEecnkk67+6+6DTCz/5Ezp1CxQuYUajuFzCvGnGLcD6Q49/OuYiz07GyT+59SapzRd9fMzquxpPRUjQNVM9mf25KW+wVx94/M7HKfYQ/yJBQqVsicFGg7Bc4rupxi3A8y0e3nvUR3ZqyZvSjprhozzK4bMO8RbTO73KeAve7uH40rTqhYsW6ntvzms3yU5bjt7p/ljTENYtxOMeYUq0nZVtEV+qZJ2YBFi3E7TVLb2aBSfm0Sr2/SX183URb61N+MlF9fh26gT7KnXlLjl9ElSUG6gSLodBrZa8ubU8hYk/r68sZJ/fX1Et0afcRtg0HijPP1jbtQZKJrO0u90ynl1xdjS2TIvGiv7GHC34yUC6EUZ9tZ6p1OKb++GFsipThfX1cxFvrU34yUC6EUZ9tZ6p1OKb++GFsiQ+ZFe2WnJxN4M1IuhFKEbWcptx8GjhXd64uxJTJkXrRXRtY2GDBOsNcXW8tn2/jouoFCSfm1Sby+FEVX6JtSfzNSfn2huopCdiel3OkUUozbnPdueFEWena2yRWqhS1kK1zKLZ8hY8W4zSe1JTJkLNorxxQr9ZbPwLFCdU0F674KFSvWTqcY2ysDxoquCy9kLNore2Bni3OnzYTqKgrZfZV6p1OM7ZUx7gcxbnPaK4ccEzJWjDtbjDutFK6rKGT3VeqdTjG2V8a4H8S4zWmv7PQkO1vwOKFjhWphC9YKFypWpC2RUbZXBowVXUtkyFi0V4ZpPwwSK/WWz9CxsvFBuopCdiel3OkUUozbnPduONEV+iZ2tslFp9PkirHjDcOL9ubgki5Luirp+9nXVUmXbcCbXoeM5e6fufuv3P2fs69fUeQPs0A3TQ4VZ4C8J/ZG1aFihdzm43z/JnmbjytOdGv0MbZX5sw7yf7bY8Si04n2ymCxIt1OUe5TvURX6MXOlitWjDll6HQab04hY8XY8RbjdgoZi/bKIceEjJX6DpJ6e2XqnU4pt1fGuJ1CxqK9stOT7GxR5yQl3FYXY3te4FjRtVdGup2i3Kd6ia7rJsb2yixWdK2MMebUMp5OpwkVY8cbhhNdoW/qsoP8Xo2ZZ+6CEzpWl9jnAx3QDRIrtpzM7L+q8cnpY3f/p6LjpJ5T6FiYfFG2V0pdWxmfSPo024kLidUptqS9YeOEjBVLTmZ23sx+r8ZH9P8o6Q0z+022ZDb2OG2xvh8op6HixBrLzF7Pfu6Flsf+7pg5BYkVY04hY4XM6Qh3j/ZL0guS/kHS/5H0m5avnxYVi5wGivXDLo//pIg4qecU+PVdlvR6h8f/9hg5BYkVY06xvr72rxgPxra6qcZZdLtqnGH3QNKspMcFxiKn/Loti20XFCdkrBhzChnrRe9xzKagWDHmFDJWyJwOiXbpJvPIG0stH0n61N0/cvd/VuPgalGxyCm/F4/xM6OMEzJWjDmFjPWfzez51geyJYSe56WMOFaMOYWMFTKnQ2Kf0T81s2+5+28l/ZWZPe/uf1JjhvmwoFjk1IUdPVX7r8zsTR0+V2FWjXbOkcdJPafQsdrckvSRmX2qr7rVpMbywqBCxYoxp5CxQuZ0SLRdN9LBJQzuSrooySQ9UuOknm13/+9FxCKnnj/7ezV21n4+dvf/Peo4qecUOlaX+H+nbOlu2GWFULFizClkrJA5HcSMudC3s6xNMsSLDxWLnA6N7dmLP8C/GSRO6jmFjoV0TVShBwAMLvaDsQCAIVHoASBxFHoASByFHgASR6EHgMRR6AEgcf8fakV2Hsid1B4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc097de82b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prefer_df.boxplot(grid=False, rot=90,fontsize=15)\n", "plt.title('Preference')\n", "plt.savefig('preference.pdf', dpi=300)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0b4983898>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc0b4983048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# columns = \\\n", "# ['a0000', 'b0000', 'c0000',\n", "# 'a0011', 'b0011', 'c0011',\n", "# 'a1011', 'b1011', 'c1011', \n", "# 'a1110', 'b1110', 'c1110', \n", "# 'a1111', 'b1111', 'c1111', \n", "# ]\n", " \n", "# br = big_results[columns]\n", "columns = sorted(big_results.columns)\n", "br = big_results[columns]\n", "br.boxplot()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc0b4890ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "sns.set(style=\"ticks\")\n", "\n", "# Load the example tips dataset\n", "tips = sns.load_dataset(\"tips\")\n", "\n", "# Draw a nested boxplot to show bills by day and sex\n", "sns.boxplot(x=\"day\", y=\"total_bill\", hue=\"sex\", data=tips, palette=\"PRGn\")\n", "sns.despine(offset=10, trim=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "renamedict = {k: k[1:] for k in page1_df.columns}\n", "page1 = page1_df.rename(columns=renamedict)\n", "page1['story'] = 'a'\n", "renamedict = {k: k[1:] for k in page2_df.columns}\n", "page2 = page2_df.rename(columns=renamedict)\n", "page2['story'] = 'b'\n", "renamedict = {k: k[1:] for k in page3_df.columns}\n", "page3 = page3_df.rename(columns=renamedict)\n", "page3['story'] = 'c'" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "chara = page1.append(page2).append(page3)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Cannot use `hue` without `x` or `y`", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-8b50c36471b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"story\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchara\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"PRGn\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Projects/Public/PhD/stage3/openrgf/venvs/openrgf-venv/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, notch, ax, **kwargs)\u001b[0m\n\u001b[1;32m 2209\u001b[0m plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[1;32m 2210\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2211\u001b[0;31m width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[1;32m 2212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2213\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Projects/Public/PhD/stage3/openrgf/venvs/openrgf-venv/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[1;32m 439\u001b[0m width, dodge, fliersize, linewidth):\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 442\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Projects/Public/PhD/stage3/openrgf/venvs/openrgf-venv/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhue\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Cannot use `hue` without `x` or `y`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;31m# No hue grouping with wide inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Cannot use `hue` without `x` or `y`" ] } ], "source": [ "sns.boxplot( hue=\"story\", data=chara, palette=\"PRGn\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" 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</tr>\n", " <tr>\n", " <th>226</th>\n", " <td>10.09</td>\n", " <td>2.00</td>\n", " <td>Female</td>\n", " <td>Yes</td>\n", " <td>Fri</td>\n", " <td>Lunch</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>227</th>\n", " <td>20.45</td>\n", " <td>3.00</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>228</th>\n", " <td>13.28</td>\n", " <td>2.72</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>229</th>\n", " <td>22.12</td>\n", " <td>2.88</td>\n", " <td>Female</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>230</th>\n", " <td>24.01</td>\n", " <td>2.00</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>231</th>\n", " <td>15.69</td>\n", " <td>3.00</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>11.61</td>\n", " <td>3.39</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>233</th>\n", " <td>10.77</td>\n", " <td>1.47</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>234</th>\n", " <td>15.53</td>\n", " <td>3.00</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>235</th>\n", " <td>10.07</td>\n", " <td>1.25</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>236</th>\n", " <td>12.60</td>\n", " <td>1.00</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>237</th>\n", " <td>32.83</td>\n", " <td>1.17</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>238</th>\n", " <td>35.83</td>\n", " <td>4.67</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>239</th>\n", " <td>29.03</td>\n", " <td>5.92</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>240</th>\n", " <td>27.18</td>\n", " <td>2.00</td>\n", " <td>Female</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>241</th>\n", " <td>22.67</td>\n", " <td>2.00</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>242</th>\n", " <td>17.82</td>\n", " <td>1.75</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sat</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>243</th>\n", " <td>18.78</td>\n", " <td>3.00</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Thur</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>244 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4\n", "5 25.29 4.71 Male No Sun Dinner 4\n", "6 8.77 2.00 Male No Sun Dinner 2\n", "7 26.88 3.12 Male No Sun Dinner 4\n", "8 15.04 1.96 Male No Sun Dinner 2\n", "9 14.78 3.23 Male No Sun Dinner 2\n", "10 10.27 1.71 Male No Sun Dinner 2\n", "11 35.26 5.00 Female No Sun Dinner 4\n", "12 15.42 1.57 Male No Sun Dinner 2\n", "13 18.43 3.00 Male No Sun Dinner 4\n", "14 14.83 3.02 Female No Sun Dinner 2\n", "15 21.58 3.92 Male No Sun Dinner 2\n", "16 10.33 1.67 Female No Sun Dinner 3\n", "17 16.29 3.71 Male No Sun Dinner 3\n", "18 16.97 3.50 Female No Sun Dinner 3\n", "19 20.65 3.35 Male No Sat Dinner 3\n", "20 17.92 4.08 Male No Sat Dinner 2\n", "21 20.29 2.75 Female No Sat Dinner 2\n", "22 15.77 2.23 Female No Sat Dinner 2\n", "23 39.42 7.58 Male No Sat Dinner 4\n", "24 19.82 3.18 Male No Sat Dinner 2\n", "25 17.81 2.34 Male No Sat Dinner 4\n", "26 13.37 2.00 Male No Sat Dinner 2\n", "27 12.69 2.00 Male No Sat Dinner 2\n", "28 21.70 4.30 Male No Sat Dinner 2\n", "29 19.65 3.00 Female No Sat Dinner 2\n", ".. ... ... ... ... ... ... ...\n", "214 28.17 6.50 Female Yes Sat Dinner 3\n", "215 12.90 1.10 Female Yes Sat Dinner 2\n", "216 28.15 3.00 Male Yes Sat Dinner 5\n", "217 11.59 1.50 Male Yes Sat Dinner 2\n", "218 7.74 1.44 Male Yes Sat Dinner 2\n", "219 30.14 3.09 Female Yes Sat Dinner 4\n", "220 12.16 2.20 Male Yes Fri Lunch 2\n", "221 13.42 3.48 Female Yes Fri Lunch 2\n", "222 8.58 1.92 Male Yes Fri Lunch 1\n", "223 15.98 3.00 Female No Fri Lunch 3\n", "224 13.42 1.58 Male Yes Fri Lunch 2\n", "225 16.27 2.50 Female Yes Fri Lunch 2\n", "226 10.09 2.00 Female Yes Fri Lunch 2\n", "227 20.45 3.00 Male No Sat Dinner 4\n", "228 13.28 2.72 Male No Sat Dinner 2\n", "229 22.12 2.88 Female Yes Sat Dinner 2\n", "230 24.01 2.00 Male Yes Sat Dinner 4\n", "231 15.69 3.00 Male Yes Sat Dinner 3\n", "232 11.61 3.39 Male No Sat Dinner 2\n", "233 10.77 1.47 Male No Sat Dinner 2\n", "234 15.53 3.00 Male Yes Sat Dinner 2\n", "235 10.07 1.25 Male No Sat Dinner 2\n", "236 12.60 1.00 Male Yes Sat Dinner 2\n", "237 32.83 1.17 Male Yes Sat Dinner 2\n", "238 35.83 4.67 Female No Sat Dinner 3\n", "239 29.03 5.92 Male No Sat Dinner 3\n", "240 27.18 2.00 Female Yes Sat Dinner 2\n", "241 22.67 2.00 Male Yes Sat Dinner 2\n", "242 17.82 1.75 Male No Sat Dinner 2\n", "243 18.78 3.00 Female No Thur Dinner 2\n", "\n", "[244 rows x 7 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }