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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os, json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>zero_native</th>\n",
" <th>zero_self_con</th>\n",
" <th>zero_cot</th>\n",
" <th>zero_cot_self_con</th>\n",
" <th>few_native</th>\n",
" <th>few_self_con</th>\n",
" <th>few_cot</th>\n",
" <th>few_cot_self_con</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Baichuan-13B-Chat</td>\n",
" <td>18.3</td>\n",
" <td>20.4</td>\n",
" <td>28.6</td>\n",
" <td>37</td>\n",
" <td>24.1</td>\n",
" <td>26.7</td>\n",
" <td>18.200000</td>\n",
" <td>17.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Chinese-Alpaca-2-13B</td>\n",
" <td>37.7</td>\n",
" <td>37.7</td>\n",
" <td>49.7</td>\n",
" <td>49.7</td>\n",
" <td>48.6</td>\n",
" <td>48.6</td>\n",
" <td>50.500000</td>\n",
" <td>50.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GPT-3.5-turbo</td>\n",
" <td>66.6</td>\n",
" <td>66.8</td>\n",
" <td>69.6</td>\n",
" <td>72</td>\n",
" <td>68.3</td>\n",
" <td>68.3</td>\n",
" <td>70.900000</td>\n",
" <td>72.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LLaMA-2-13B</td>\n",
" <td>41.8</td>\n",
" <td>46.5</td>\n",
" <td>53.1</td>\n",
" <td>58.7</td>\n",
" <td>53.3</td>\n",
" <td>53</td>\n",
" <td>56.800000</td>\n",
" <td>61.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Qwen-7B-Chat</td>\n",
" <td>45.9</td>\n",
" <td>46</td>\n",
" <td>47.3</td>\n",
" <td>50.1</td>\n",
" <td>52.1</td>\n",
" <td>51</td>\n",
" <td>48.300000</td>\n",
" <td>49.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ChatGLM2-6B</td>\n",
" <td>24.8</td>\n",
" <td>24.7</td>\n",
" <td>36.6</td>\n",
" <td>36.5</td>\n",
" <td>37.6</td>\n",
" <td>37.6</td>\n",
" <td>40.500000</td>\n",
" <td>40.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Chinese-LLaMA-2-13B</td>\n",
" <td>29.4</td>\n",
" <td>29.4</td>\n",
" <td>37.8</td>\n",
" <td>37.8</td>\n",
" <td>40.4</td>\n",
" <td>40.4</td>\n",
" <td>28.800000</td>\n",
" <td>28.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>InternLM-7B</td>\n",
" <td>38.7</td>\n",
" <td>38.7</td>\n",
" <td>43.9</td>\n",
" <td>43.9</td>\n",
" <td>45.2</td>\n",
" <td>45.2</td>\n",
" <td>51.400000</td>\n",
" <td>51.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>LLaMA-2-7B</td>\n",
" <td>39.5</td>\n",
" <td>40</td>\n",
" <td>45.4</td>\n",
" <td>49.5</td>\n",
" <td>48.2</td>\n",
" <td>46.8</td>\n",
" <td>52.000000</td>\n",
" <td>55.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Baichuan2-13B-Chat</td>\n",
" <td>14.1</td>\n",
" <td>15.3</td>\n",
" <td>24.1</td>\n",
" <td>25.8</td>\n",
" <td>32.3</td>\n",
" <td>33.1</td>\n",
" <td>25.600000</td>\n",
" <td>27.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>GPT-4</td>\n",
" <td>/</td>\n",
" <td>/</td>\n",
" <td>/</td>\n",
" <td>/</td>\n",
" <td>/</td>\n",
" <td>/</td>\n",
" <td>88.700000</td>\n",
" <td>88.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>AquilaChat2-34B</td>\n",
" <td>36.63</td>\n",
" <td>36.63</td>\n",
" <td>44.83</td>\n",
" <td>44.83</td>\n",
" <td>46.65</td>\n",
" <td>46.65</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Yi-34B-Chat</td>\n",
" <td>57.75</td>\n",
" <td>59.14</td>\n",
" <td>65.11</td>\n",
" <td>68.79</td>\n",
" <td>68.16</td>\n",
" <td>68.37</td>\n",
" <td>78.090000</td>\n",
" <td>80.060000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>DevOps-Model-14B-Chat</td>\n",
" <td>30.69</td>\n",
" <td>30.59</td>\n",
" <td>55.77</td>\n",
" <td>63.63</td>\n",
" <td>63.85</td>\n",
" <td>61.96</td>\n",
" <td>41.150000</td>\n",
" <td>44.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Qwen-72B-Chat</td>\n",
" <td>70.41</td>\n",
" <td>70.50</td>\n",
" <td>72.38</td>\n",
" <td>72.56</td>\n",
" <td>70.32</td>\n",
" <td>70.32</td>\n",
" <td>70.130000</td>\n",
" <td>70.220000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Mistral-7B</td>\n",
" <td>29.27</td>\n",
" <td>29.27</td>\n",
" <td>46.30</td>\n",
" <td>46.30</td>\n",
" <td>47.22</td>\n",
" <td>47.22</td>\n",
" <td>45.580000</td>\n",
" <td>45.580000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Qwen-14B-Chat</td>\n",
" <td>43.78</td>\n",
" <td>47.81</td>\n",
" <td>56.58</td>\n",
" <td>59.40</td>\n",
" <td>62.09</td>\n",
" <td>59.70</td>\n",
" <td>49.060000</td>\n",
" <td>55.880000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>LLaMA-2-70B-Chat</td>\n",
" <td>25.29</td>\n",
" <td>25.29</td>\n",
" <td>57.97</td>\n",
" <td>58.06</td>\n",
" <td>52.97</td>\n",
" <td>52.97</td>\n",
" <td>58.550000</td>\n",
" <td>58.550000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>ERNIE-Bot-4.0</td>\n",
" <td>61.15</td>\n",
" <td>61.15</td>\n",
" <td>70.00</td>\n",
" <td>70.00</td>\n",
" <td>60.00</td>\n",
" <td>60.00</td>\n",
" <td>70.000000</td>\n",
" <td>70.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ChatGLM3-6B</td>\n",
" <td>43.38487973</td>\n",
" <td>43.38487973</td>\n",
" <td>44.58762887</td>\n",
" <td>44.58762887</td>\n",
" <td>42.09621993</td>\n",
" <td>42.09621993</td>\n",
" <td>43.470790</td>\n",
" <td>43.470790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>InternLM2-Chat-20B</td>\n",
" <td>56.35738832</td>\n",
" <td>56.35738832</td>\n",
" <td>26.18025751</td>\n",
" <td>26.18025751</td>\n",
" <td>60.48109966</td>\n",
" <td>60.48109966</td>\n",
" <td>45.103093</td>\n",
" <td>45.103093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>InternLM2-Chat-7B</td>\n",
" <td>49.74226804</td>\n",
" <td>49.74226804</td>\n",
" <td>56.18556701</td>\n",
" <td>56.18556701</td>\n",
" <td>48.19587629</td>\n",
" <td>48.19587629</td>\n",
" <td>49.742268</td>\n",
" <td>49.742268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>gemma_2b</td>\n",
" <td>26.46048</td>\n",
" <td>26.46048</td>\n",
" <td>33.41924</td>\n",
" <td>33.41924</td>\n",
" <td>26.6323</td>\n",
" <td>26.6323</td>\n",
" <td>37.542960</td>\n",
" <td>37.542960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>gemma_7b</td>\n",
" <td>25.08591</td>\n",
" <td>25.08591</td>\n",
" <td>50.85911</td>\n",
" <td>50.85911</td>\n",
" <td>30.24055</td>\n",
" <td>30.24055</td>\n",
" <td>51.557470</td>\n",
" <td>51.557470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>qwen1.5-14b-base</td>\n",
" <td>34.87973</td>\n",
" <td>34.87973</td>\n",
" <td>60.82474</td>\n",
" <td>60.82474</td>\n",
" <td>65.54983</td>\n",
" <td>65.54983</td>\n",
" <td>47.079040</td>\n",
" <td>47.079040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>qwen1.5-14b-chat</td>\n",
" <td>54.89691</td>\n",
" <td>56.4433</td>\n",
" <td>64.08935</td>\n",
" <td>67.09622</td>\n",
" <td>52.23368</td>\n",
" <td>53.52234</td>\n",
" <td>59.536080</td>\n",
" <td>64.175260</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name zero_native zero_self_con zero_cot \\\n",
"0 Baichuan-13B-Chat 18.3 20.4 28.6 \n",
"1 Chinese-Alpaca-2-13B 37.7 37.7 49.7 \n",
"2 GPT-3.5-turbo 66.6 66.8 69.6 \n",
"3 LLaMA-2-13B 41.8 46.5 53.1 \n",
"4 Qwen-7B-Chat 45.9 46 47.3 \n",
"5 ChatGLM2-6B 24.8 24.7 36.6 \n",
"6 Chinese-LLaMA-2-13B 29.4 29.4 37.8 \n",
"7 InternLM-7B 38.7 38.7 43.9 \n",
"8 LLaMA-2-7B 39.5 40 45.4 \n",
"9 Baichuan2-13B-Chat 14.1 15.3 24.1 \n",
"10 GPT-4 / / / \n",
"11 AquilaChat2-34B 36.63 36.63 44.83 \n",
"12 Yi-34B-Chat 57.75 59.14 65.11 \n",
"13 DevOps-Model-14B-Chat 30.69 30.59 55.77 \n",
"14 Qwen-72B-Chat 70.41 70.50 72.38 \n",
"15 Mistral-7B 29.27 29.27 46.30 \n",
"16 Qwen-14B-Chat 43.78 47.81 56.58 \n",
"17 LLaMA-2-70B-Chat 25.29 25.29 57.97 \n",
"18 ERNIE-Bot-4.0 61.15 61.15 70.00 \n",
"19 ChatGLM3-6B 43.38487973 43.38487973 44.58762887 \n",
"20 InternLM2-Chat-20B 56.35738832 56.35738832 26.18025751 \n",
"21 InternLM2-Chat-7B 49.74226804 49.74226804 56.18556701 \n",
"22 gemma_2b 26.46048 26.46048 33.41924 \n",
"23 gemma_7b 25.08591 25.08591 50.85911 \n",
"24 qwen1.5-14b-base 34.87973 34.87973 60.82474 \n",
"25 qwen1.5-14b-chat 54.89691 56.4433 64.08935 \n",
"\n",
" zero_cot_self_con few_native few_self_con few_cot few_cot_self_con \n",
"0 37 24.1 26.7 18.200000 17.800000 \n",
"1 49.7 48.6 48.6 50.500000 50.500000 \n",
"2 72 68.3 68.3 70.900000 72.500000 \n",
"3 58.7 53.3 53 56.800000 61.000000 \n",
"4 50.1 52.1 51 48.300000 49.800000 \n",
"5 36.5 37.6 37.6 40.500000 40.500000 \n",
"6 37.8 40.4 40.4 28.800000 28.800000 \n",
"7 43.9 45.2 45.2 51.400000 51.400000 \n",
"8 49.5 48.2 46.8 52.000000 55.200000 \n",
"9 25.8 32.3 33.1 25.600000 27.700000 \n",
"10 / / / 88.700000 88.700000 \n",
"11 44.83 46.65 46.65 NaN NaN \n",
"12 68.79 68.16 68.37 78.090000 80.060000 \n",
"13 63.63 63.85 61.96 41.150000 44.010000 \n",
"14 72.56 70.32 70.32 70.130000 70.220000 \n",
"15 46.30 47.22 47.22 45.580000 45.580000 \n",
"16 59.40 62.09 59.70 49.060000 55.880000 \n",
"17 58.06 52.97 52.97 58.550000 58.550000 \n",
"18 70.00 60.00 60.00 70.000000 70.000000 \n",
"19 44.58762887 42.09621993 42.09621993 43.470790 43.470790 \n",
"20 26.18025751 60.48109966 60.48109966 45.103093 45.103093 \n",
"21 56.18556701 48.19587629 48.19587629 49.742268 49.742268 \n",
"22 33.41924 26.6323 26.6323 37.542960 37.542960 \n",
"23 50.85911 30.24055 30.24055 51.557470 51.557470 \n",
"24 60.82474 65.54983 65.54983 47.079040 47.079040 \n",
"25 67.09622 52.23368 53.52234 59.536080 64.175260 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"./data/network_en_mc.csv\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Naive</th>\n",
" <th>SC</th>\n",
" <th>CoT</th>\n",
" <th>CoT+SC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57.75</td>\n",
" <td>59.14</td>\n",
" <td>65.11</td>\n",
" <td>68.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>70.41</td>\n",
" <td>70.50</td>\n",
" <td>72.38</td>\n",
" <td>72.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>66.60</td>\n",
" <td>66.80</td>\n",
" <td>69.60</td>\n",
" <td>72.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>61.15</td>\n",
" <td>61.15</td>\n",
" <td>70.00</td>\n",
" <td>70.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>54.90</td>\n",
" <td>56.44</td>\n",
" <td>64.09</td>\n",
" <td>67.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>34.88</td>\n",
" <td>34.88</td>\n",
" <td>60.82</td>\n",
" <td>60.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>30.69</td>\n",
" <td>30.59</td>\n",
" <td>55.77</td>\n",
" <td>63.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>43.78</td>\n",
" <td>47.81</td>\n",
" <td>56.58</td>\n",
" <td>59.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>41.80</td>\n",
" <td>46.50</td>\n",
" <td>53.10</td>\n",
" <td>58.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>56.36</td>\n",
" <td>56.36</td>\n",
" <td>26.18</td>\n",
" <td>26.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>25.29</td>\n",
" <td>25.29</td>\n",
" <td>57.97</td>\n",
" <td>58.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>49.74</td>\n",
" <td>49.74</td>\n",
" <td>56.19</td>\n",
" <td>56.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>39.50</td>\n",
" <td>40.00</td>\n",
" <td>45.40</td>\n",
" <td>49.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>45.90</td>\n",
" <td>46.00</td>\n",
" <td>47.30</td>\n",
" <td>50.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>25.09</td>\n",
" <td>25.09</td>\n",
" <td>50.86</td>\n",
" <td>50.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>38.70</td>\n",
" <td>38.70</td>\n",
" <td>43.90</td>\n",
" <td>43.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>37.70</td>\n",
" <td>37.70</td>\n",
" <td>49.70</td>\n",
" <td>49.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>29.27</td>\n",
" <td>29.27</td>\n",
" <td>46.30</td>\n",
" <td>46.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>36.63</td>\n",
" <td>36.63</td>\n",
" <td>44.83</td>\n",
" <td>44.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>43.38</td>\n",
" <td>43.38</td>\n",
" <td>44.59</td>\n",
" <td>44.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>24.80</td>\n",
" <td>24.70</td>\n",
" <td>36.60</td>\n",
" <td>36.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>29.40</td>\n",
" <td>29.40</td>\n",
" <td>37.80</td>\n",
" <td>37.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>26.46</td>\n",
" <td>26.46</td>\n",
" <td>33.42</td>\n",
" <td>33.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>18.30</td>\n",
" <td>20.40</td>\n",
" <td>28.60</td>\n",
" <td>37.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>14.10</td>\n",
" <td>15.30</td>\n",
" <td>24.10</td>\n",
" <td>25.80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Naive SC CoT CoT+SC\n",
"0 NaN NaN NaN NaN\n",
"1 57.75 59.14 65.11 68.79\n",
"2 70.41 70.50 72.38 72.56\n",
"3 66.60 66.80 69.60 72.00\n",
"4 61.15 61.15 70.00 70.00\n",
"5 54.90 56.44 64.09 67.10\n",
"6 34.88 34.88 60.82 60.82\n",
"7 30.69 30.59 55.77 63.63\n",
"8 43.78 47.81 56.58 59.40\n",
"9 41.80 46.50 53.10 58.70\n",
"10 56.36 56.36 26.18 26.18\n",
"11 25.29 25.29 57.97 58.06\n",
"12 49.74 49.74 56.19 56.19\n",
"13 39.50 40.00 45.40 49.50\n",
"14 45.90 46.00 47.30 50.10\n",
"15 25.09 25.09 50.86 50.86\n",
"16 38.70 38.70 43.90 43.90\n",
"17 37.70 37.70 49.70 49.70\n",
"18 29.27 29.27 46.30 46.30\n",
"19 36.63 36.63 44.83 44.83\n",
"20 43.38 43.38 44.59 44.59\n",
"21 24.80 24.70 36.60 36.50\n",
"22 29.40 29.40 37.80 37.80\n",
"23 26.46 26.46 33.42 33.42\n",
"24 18.30 20.40 28.60 37.00\n",
"25 14.10 15.30 24.10 25.80"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def process_mc_df(df):\n",
" # 将name列重命名为Model\n",
" df = df.rename(columns={\"name\": \"Model\"})\n",
" # 将zero_naive, zero_self_con, zero_cot, zero_cot_self_con, few_naive, few_self_con, few_cot, few_cot_self_con列重新组织成MultiIndex，一层为Zeroshot, Fewshot,一层为Naive, Self-Consistency, CoT, CoT+Self-Consistency\n",
" df = df.set_index(\"Model\")\n",
" # df = df.stack().unstack()\n",
" df.columns = pd.MultiIndex.from_tuples([(\"Zeroshot\", \"Naive\"), (\"Zeroshot\", \"SC\"), (\"Zeroshot\", \"CoT\"), (\"Zeroshot\", \"CoT+SC\"), (\"Fewshot\", \"Naive\"), (\"Fewshot\", \"SC\"), (\"Fewshot\", \"CoT\"), (\"Fewshot\", \"CoT+SC\")])\n",
" # 将除了Model列之外的列的value转换为数值型，失败的为NaN\n",
" df = df.apply(pd.to_numeric, errors=\"coerce\")\n",
" # 显示小数点后两位\n",
" df = df.round(2)\n",
" # 给每一行添加一列BestScore\n",
" df[\"BestScore\"] = df.max(axis=1)\n",
" # 根据BestScore给df排序\n",
" df = df.sort_values(by=\"BestScore\", ascending=False)\n",
" # \n",
" df = df.reset_index()\n",
" return df\n",
"\n",
"processed = process_mc_df(df)\n",
"processed.columns\n",
"processed['Zeroshot']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "opencompass",
"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",
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"nbformat": 4,
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|