diff --git a/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.ipynb b/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.ipynb
index f2d55a43cdb0ab22768d0082be08ba5c7c50ff47..5b14f94cd4d459685ac5bcf8605a6ff833cc68ae 100644
--- a/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.ipynb
+++ b/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
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- "execution_count": 40,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
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+ "execution_count": 3,
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@@ -1792,7 +1792,7 @@
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- "execution_count": 38,
+ "execution_count": 3,
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@@ -1805,7 +1805,7 @@
},
{
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+ "execution_count": 4,
"metadata": {
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},
@@ -3580,7 +3580,7 @@
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- "execution_count": 21,
+ "execution_count": 4,
"metadata": {},
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@@ -3593,1782 +3593,44 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 35,
"metadata": {
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- " <td>front</td>\n",
- " <td>93.7</td>\n",
- " <td>...</td>\n",
- " <td>90</td>\n",
- " <td>2bbl</td>\n",
- " <td>2.97</td>\n",
- " <td>3.23</td>\n",
- " <td>9.41</td>\n",
- " <td>68.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>37</td>\n",
- " <td>41</td>\n",
- " <td>5572.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>1</td>\n",
- " <td>118.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>93.7</td>\n",
- " <td>...</td>\n",
- " <td>90</td>\n",
- " <td>2bbl</td>\n",
- " <td>2.97</td>\n",
- " <td>3.23</td>\n",
- " <td>9.40</td>\n",
- " <td>68.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>31</td>\n",
- " <td>38</td>\n",
- " <td>6377.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>1</td>\n",
- " <td>118.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>turbo</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>93.7</td>\n",
- " <td>...</td>\n",
- " <td>98</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.03</td>\n",
- " <td>3.39</td>\n",
- " <td>7.60</td>\n",
- " <td>102.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>24</td>\n",
- " <td>30</td>\n",
- " <td>7957.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>1</td>\n",
- " <td>148.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>93.7</td>\n",
- " <td>...</td>\n",
- " <td>90</td>\n",
- " <td>2bbl</td>\n",
- " <td>2.97</td>\n",
- " <td>3.23</td>\n",
- " <td>9.40</td>\n",
- " <td>68.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>31</td>\n",
- " <td>38</td>\n",
- " <td>6229.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>1</td>\n",
- " <td>148.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>93.7</td>\n",
- " <td>...</td>\n",
- " <td>90</td>\n",
- " <td>2bbl</td>\n",
- " <td>2.97</td>\n",
- " <td>3.23</td>\n",
- " <td>9.40</td>\n",
- " <td>68.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>31</td>\n",
- " <td>38</td>\n",
- " <td>6692.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1</td>\n",
- " <td>148.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>93.7</td>\n",
- " <td>...</td>\n",
- " <td>90</td>\n",
- " <td>2bbl</td>\n",
- " <td>2.97</td>\n",
- " <td>3.23</td>\n",
- " <td>9.40</td>\n",
- " <td>68.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>31</td>\n",
- " <td>38</td>\n",
- " <td>7609.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>-1</td>\n",
- " <td>110.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>wagon</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>103.3</td>\n",
- " <td>...</td>\n",
- " <td>122</td>\n",
- " <td>2bbl</td>\n",
- " <td>3.34</td>\n",
- " <td>3.46</td>\n",
- " <td>8.50</td>\n",
- " <td>88.0</td>\n",
- " <td>5000.0</td>\n",
- " <td>24</td>\n",
- " <td>30</td>\n",
- " <td>8921.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>3</td>\n",
- " <td>145.0</td>\n",
- " <td>dodge</td>\n",
- " <td>gas</td>\n",
- " <td>turbo</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>95.9</td>\n",
- " <td>...</td>\n",
- " <td>156</td>\n",
- " <td>mfi</td>\n",
- " <td>3.60</td>\n",
- " <td>3.90</td>\n",
- " <td>7.00</td>\n",
- " <td>145.0</td>\n",
- " <td>5000.0</td>\n",
- " <td>19</td>\n",
- " <td>24</td>\n",
- " <td>12964.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>2</td>\n",
- " <td>137.0</td>\n",
- " <td>honda</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>86.6</td>\n",
- " <td>...</td>\n",
- " <td>92</td>\n",
- " <td>1bbl</td>\n",
- " <td>2.91</td>\n",
- " <td>3.41</td>\n",
- " <td>9.60</td>\n",
- " <td>58.0</td>\n",
- " <td>4800.0</td>\n",
- " <td>49</td>\n",
- " <td>54</td>\n",
- " <td>6479.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>175</th>\n",
- " <td>-1</td>\n",
- " <td>65.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>102.4</td>\n",
- " <td>...</td>\n",
- " <td>122</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.31</td>\n",
- " <td>3.54</td>\n",
- " <td>8.70</td>\n",
- " <td>92.0</td>\n",
- " <td>4200.0</td>\n",
- " <td>27</td>\n",
- " <td>32</td>\n",
- " <td>9988.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>176</th>\n",
- " <td>-1</td>\n",
- " <td>65.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>102.4</td>\n",
- " <td>...</td>\n",
- " <td>122</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.31</td>\n",
- " <td>3.54</td>\n",
- " <td>8.70</td>\n",
- " <td>92.0</td>\n",
- " <td>4200.0</td>\n",
- " <td>27</td>\n",
- " <td>32</td>\n",
- " <td>10898.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>177</th>\n",
- " <td>-1</td>\n",
- " <td>65.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>102.4</td>\n",
- " <td>...</td>\n",
- " <td>122</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.31</td>\n",
- " <td>3.54</td>\n",
- " <td>8.70</td>\n",
- " <td>92.0</td>\n",
- " <td>4200.0</td>\n",
- " <td>27</td>\n",
- " <td>32</td>\n",
- " <td>11248.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>178</th>\n",
- " <td>3</td>\n",
- " <td>197.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>102.9</td>\n",
- " <td>...</td>\n",
- " <td>171</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.27</td>\n",
- " <td>3.35</td>\n",
- " <td>9.30</td>\n",
- " <td>161.0</td>\n",
- " <td>5200.0</td>\n",
- " <td>20</td>\n",
- " <td>24</td>\n",
- " <td>16558.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>179</th>\n",
- " <td>3</td>\n",
- " <td>197.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>102.9</td>\n",
- " <td>...</td>\n",
- " <td>171</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.27</td>\n",
- " <td>3.35</td>\n",
- " <td>9.30</td>\n",
- " <td>161.0</td>\n",
- " <td>5200.0</td>\n",
- " <td>19</td>\n",
- " <td>24</td>\n",
- " <td>15998.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>180</th>\n",
- " <td>-1</td>\n",
- " <td>90.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.5</td>\n",
- " <td>...</td>\n",
- " <td>171</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.27</td>\n",
- " <td>3.35</td>\n",
- " <td>9.20</td>\n",
- " <td>156.0</td>\n",
- " <td>5200.0</td>\n",
- " <td>20</td>\n",
- " <td>24</td>\n",
- " <td>15690.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>181</th>\n",
- " <td>-1</td>\n",
- " <td>122.0</td>\n",
- " <td>toyota</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>wagon</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.5</td>\n",
- " <td>...</td>\n",
- " <td>161</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.27</td>\n",
- " <td>3.35</td>\n",
- " <td>9.20</td>\n",
- " <td>156.0</td>\n",
- " <td>5200.0</td>\n",
- " <td>19</td>\n",
- " <td>24</td>\n",
- " <td>15750.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>182</th>\n",
- " <td>2</td>\n",
- " <td>122.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>diesel</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>97</td>\n",
- " <td>idi</td>\n",
- " <td>3.01</td>\n",
- " <td>3.40</td>\n",
- " <td>23.00</td>\n",
- " <td>52.0</td>\n",
- " <td>4800.0</td>\n",
- " <td>37</td>\n",
- " <td>46</td>\n",
- " <td>7775.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>183</th>\n",
- " <td>2</td>\n",
- " <td>122.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>9.00</td>\n",
- " <td>85.0</td>\n",
- " <td>5250.0</td>\n",
- " <td>27</td>\n",
- " <td>34</td>\n",
- " <td>7975.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>184</th>\n",
- " <td>2</td>\n",
- " <td>94.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>diesel</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>97</td>\n",
- " <td>idi</td>\n",
- " <td>3.01</td>\n",
- " <td>3.40</td>\n",
- " <td>23.00</td>\n",
- " <td>52.0</td>\n",
- " <td>4800.0</td>\n",
- " <td>37</td>\n",
- " <td>46</td>\n",
- " <td>7995.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>185</th>\n",
- " <td>2</td>\n",
- " <td>94.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>9.00</td>\n",
- " <td>85.0</td>\n",
- " <td>5250.0</td>\n",
- " <td>27</td>\n",
- " <td>34</td>\n",
- " <td>8195.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>186</th>\n",
- " <td>2</td>\n",
- " <td>94.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>9.00</td>\n",
- " <td>85.0</td>\n",
- " <td>5250.0</td>\n",
- " <td>27</td>\n",
- " <td>34</td>\n",
- " <td>8495.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>187</th>\n",
- " <td>2</td>\n",
- " <td>94.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>diesel</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>97</td>\n",
- " <td>idi</td>\n",
- " <td>3.01</td>\n",
- " <td>3.40</td>\n",
- " <td>23.00</td>\n",
- " <td>68.0</td>\n",
- " <td>4500.0</td>\n",
- " <td>37</td>\n",
- " <td>42</td>\n",
- " <td>9495.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>188</th>\n",
- " <td>2</td>\n",
- " <td>94.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>97.3</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>10.00</td>\n",
- " <td>100.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>26</td>\n",
- " <td>32</td>\n",
- " <td>9995.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>189</th>\n",
- " <td>3</td>\n",
- " <td>122.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>convertible</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>94.5</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>8.50</td>\n",
- " <td>90.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>24</td>\n",
- " <td>29</td>\n",
- " <td>11595.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>190</th>\n",
- " <td>3</td>\n",
- " <td>256.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>two</td>\n",
- " <td>hatchback</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>94.5</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>8.50</td>\n",
- " <td>90.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>24</td>\n",
- " <td>29</td>\n",
- " <td>9980.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>191</th>\n",
- " <td>0</td>\n",
- " <td>122.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>100.4</td>\n",
- " <td>...</td>\n",
- " <td>136</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>8.50</td>\n",
- " <td>110.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>19</td>\n",
- " <td>24</td>\n",
- " <td>13295.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>192</th>\n",
- " <td>0</td>\n",
- " <td>122.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>diesel</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>100.4</td>\n",
- " <td>...</td>\n",
- " <td>97</td>\n",
- " <td>idi</td>\n",
- " <td>3.01</td>\n",
- " <td>3.40</td>\n",
- " <td>23.00</td>\n",
- " <td>68.0</td>\n",
- " <td>4500.0</td>\n",
- " <td>33</td>\n",
- " <td>38</td>\n",
- " <td>13845.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>193</th>\n",
- " <td>0</td>\n",
- " <td>122.0</td>\n",
- " <td>volkswagen</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>wagon</td>\n",
- " <td>fwd</td>\n",
- " <td>front</td>\n",
- " <td>100.4</td>\n",
- " <td>...</td>\n",
- " <td>109</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.19</td>\n",
- " <td>3.40</td>\n",
- " <td>9.00</td>\n",
- " <td>88.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>25</td>\n",
- " <td>31</td>\n",
- " <td>12290.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>194</th>\n",
- " <td>-2</td>\n",
- " <td>103.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.3</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>9.50</td>\n",
- " <td>114.0</td>\n",
- " <td>5400.0</td>\n",
- " <td>23</td>\n",
- " <td>28</td>\n",
- " <td>12940.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>195</th>\n",
- " <td>-1</td>\n",
- " <td>74.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>wagon</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.3</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>9.50</td>\n",
- " <td>114.0</td>\n",
- " <td>5400.0</td>\n",
- " <td>23</td>\n",
- " <td>28</td>\n",
- " <td>13415.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>196</th>\n",
- " <td>-2</td>\n",
- " <td>103.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.3</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>9.50</td>\n",
- " <td>114.0</td>\n",
- " <td>5400.0</td>\n",
- " <td>24</td>\n",
- " <td>28</td>\n",
- " <td>15985.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>197</th>\n",
- " <td>-1</td>\n",
- " <td>74.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>wagon</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.3</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>9.50</td>\n",
- " <td>114.0</td>\n",
- " <td>5400.0</td>\n",
- " <td>24</td>\n",
- " <td>28</td>\n",
- " <td>16515.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>198</th>\n",
- " <td>-2</td>\n",
- " <td>103.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.3</td>\n",
- " <td>...</td>\n",
- " <td>130</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.62</td>\n",
- " <td>3.15</td>\n",
- " <td>7.50</td>\n",
- " <td>162.0</td>\n",
- " <td>5100.0</td>\n",
- " <td>17</td>\n",
- " <td>22</td>\n",
- " <td>18420.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>199</th>\n",
- " <td>-1</td>\n",
- " <td>74.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>wagon</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>104.3</td>\n",
- " <td>...</td>\n",
- " <td>130</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.62</td>\n",
- " <td>3.15</td>\n",
- " <td>7.50</td>\n",
- " <td>162.0</td>\n",
- " <td>5100.0</td>\n",
- " <td>17</td>\n",
- " <td>22</td>\n",
- " <td>18950.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>200</th>\n",
- " <td>-1</td>\n",
- " <td>95.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>109.1</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>9.50</td>\n",
- " <td>114.0</td>\n",
- " <td>5400.0</td>\n",
- " <td>23</td>\n",
- " <td>28</td>\n",
- " <td>16845.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>201</th>\n",
- " <td>-1</td>\n",
- " <td>95.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>109.1</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>8.70</td>\n",
- " <td>160.0</td>\n",
- " <td>5300.0</td>\n",
- " <td>19</td>\n",
- " <td>25</td>\n",
- " <td>19045.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>202</th>\n",
- " <td>-1</td>\n",
- " <td>95.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>std</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>109.1</td>\n",
- " <td>...</td>\n",
- " <td>173</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.58</td>\n",
- " <td>2.87</td>\n",
- " <td>8.80</td>\n",
- " <td>134.0</td>\n",
- " <td>5500.0</td>\n",
- " <td>18</td>\n",
- " <td>23</td>\n",
- " <td>21485.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>203</th>\n",
- " <td>-1</td>\n",
- " <td>95.0</td>\n",
- " <td>volvo</td>\n",
- " <td>diesel</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>109.1</td>\n",
- " <td>...</td>\n",
- " <td>145</td>\n",
- " <td>idi</td>\n",
- " <td>3.01</td>\n",
- " <td>3.40</td>\n",
- " <td>23.00</td>\n",
- " <td>106.0</td>\n",
- " <td>4800.0</td>\n",
- " <td>26</td>\n",
- " <td>27</td>\n",
- " <td>22470.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>204</th>\n",
- " <td>-1</td>\n",
- " <td>95.0</td>\n",
- " <td>volvo</td>\n",
- " <td>gas</td>\n",
- " <td>turbo</td>\n",
- " <td>four</td>\n",
- " <td>sedan</td>\n",
- " <td>rwd</td>\n",
- " <td>front</td>\n",
- " <td>109999.1</td>\n",
- " <td>...</td>\n",
- " <td>141</td>\n",
- " <td>mpfi</td>\n",
- " <td>3.78</td>\n",
- " <td>3.15</td>\n",
- " <td>9.50</td>\n",
- " <td>114.0</td>\n",
- " <td>5400.0</td>\n",
- " <td>19</td>\n",
- " <td>25</td>\n",
- " <td>22625.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>203 rows × 26 columns</p>\n",
- "</div>"
- ],
"text/plain": [
- " symboling normalized-losses make fuel-type aspiration \\\n",
- "0 3 122.0 alfa-romero gas std \n",
- "1 3 122.0 alfa-romero gas std \n",
- "2 1 122.0 alfa-romero gas std \n",
- "3 2 164.0 audi gas std \n",
- "4 2 164.0 audi gas std \n",
- "5 2 122.0 audi gas std \n",
- "6 1 158.0 audi gas std \n",
- "7 1 122.0 audi gas std \n",
- "8 1 158.0 audi gas turbo \n",
- "9 0 122.0 audi gas turbo \n",
- "10 2 192.0 bmw gas std \n",
- "11 0 192.0 bmw gas std \n",
- "12 0 188.0 bmw gas std \n",
- "13 0 188.0 bmw gas std \n",
- "14 1 122.0 bmw gas std \n",
- "15 0 122.0 bmw gas std \n",
- "16 0 122.0 bmw gas std \n",
- "17 0 122.0 bmw gas std \n",
- "18 2 121.0 chevrolet gas std \n",
- "19 1 98.0 chevrolet gas std \n",
- "20 0 81.0 chevrolet gas std \n",
- "21 1 118.0 dodge gas std \n",
- "22 1 118.0 dodge gas std \n",
- "23 1 118.0 dodge gas turbo \n",
- "24 1 148.0 dodge gas std \n",
- "25 1 148.0 dodge gas std \n",
- "26 1 148.0 dodge gas std \n",
- "28 -1 110.0 dodge gas std \n",
- "29 3 145.0 dodge gas turbo \n",
- "30 2 137.0 honda gas std \n",
- ".. ... ... ... ... ... \n",
- "175 -1 65.0 toyota gas std \n",
- "176 -1 65.0 toyota gas std \n",
- "177 -1 65.0 toyota gas std \n",
- "178 3 197.0 toyota gas std \n",
- "179 3 197.0 toyota gas std \n",
- "180 -1 90.0 toyota gas std \n",
- "181 -1 122.0 toyota gas std \n",
- "182 2 122.0 volkswagen diesel std \n",
- "183 2 122.0 volkswagen gas std \n",
- "184 2 94.0 volkswagen diesel std \n",
- "185 2 94.0 volkswagen gas std \n",
- "186 2 94.0 volkswagen gas std \n",
- "187 2 94.0 volkswagen diesel turbo \n",
- "188 2 94.0 volkswagen gas std \n",
- "189 3 122.0 volkswagen gas std \n",
- "190 3 256.0 volkswagen gas std \n",
- "191 0 122.0 volkswagen gas std \n",
- "192 0 122.0 volkswagen diesel turbo \n",
- "193 0 122.0 volkswagen gas std \n",
- "194 -2 103.0 volvo gas std \n",
- "195 -1 74.0 volvo gas std \n",
- "196 -2 103.0 volvo gas std \n",
- "197 -1 74.0 volvo gas std \n",
- "198 -2 103.0 volvo gas turbo \n",
- "199 -1 74.0 volvo gas turbo \n",
- "200 -1 95.0 volvo gas std \n",
- "201 -1 95.0 volvo gas turbo \n",
- "202 -1 95.0 volvo gas std \n",
- "203 -1 95.0 volvo diesel turbo \n",
- "204 -1 95.0 volvo gas turbo \n",
- "\n",
- " num-of-doors body-style drive-wheels engine-location wheel-base \\\n",
- "0 two convertible rwd front 88.6 \n",
- "1 two convertible rwd front 88.6 \n",
- "2 two hatchback rwd front 94.5 \n",
- "3 four sedan fwd front 99.8 \n",
- "4 four sedan 4wd front 99.4 \n",
- "5 two sedan fwd front 99.8 \n",
- "6 four sedan fwd front 105.8 \n",
- "7 four wagon fwd front 105.8 \n",
- "8 four sedan fwd front 105.8 \n",
- "9 two hatchback 4wd front 99.5 \n",
- "10 two sedan rwd front 101.2 \n",
- "11 four sedan rwd front 101.2 \n",
- "12 two sedan rwd front 101.2 \n",
- "13 four sedan rwd front 101.2 \n",
- "14 four sedan rwd front 103.5 \n",
- "15 four sedan rwd front 103.5 \n",
- "16 two sedan rwd front 103.5 \n",
- "17 four sedan rwd front 110.0 \n",
- "18 two hatchback fwd front 88.4 \n",
- "19 two hatchback fwd front 94.5 \n",
- "20 four sedan fwd front 94.5 \n",
- "21 two hatchback fwd front 93.7 \n",
- "22 two hatchback fwd front 93.7 \n",
- "23 two hatchback fwd front 93.7 \n",
- "24 four hatchback fwd front 93.7 \n",
- "25 four sedan fwd front 93.7 \n",
- "26 four sedan fwd front 93.7 \n",
- "28 four wagon fwd front 103.3 \n",
- "29 two hatchback fwd front 95.9 \n",
- "30 two hatchback fwd front 86.6 \n",
- ".. ... ... ... ... ... \n",
- "175 four hatchback fwd front 102.4 \n",
- "176 four sedan fwd front 102.4 \n",
- "177 four hatchback fwd front 102.4 \n",
- "178 two hatchback rwd front 102.9 \n",
- "179 two hatchback rwd front 102.9 \n",
- "180 four sedan rwd front 104.5 \n",
- "181 four wagon rwd front 104.5 \n",
- "182 two sedan fwd front 97.3 \n",
- "183 two sedan fwd front 97.3 \n",
- "184 four sedan fwd front 97.3 \n",
- "185 four sedan fwd front 97.3 \n",
- "186 four sedan fwd front 97.3 \n",
- "187 four sedan fwd front 97.3 \n",
- "188 four sedan fwd front 97.3 \n",
- "189 two convertible fwd front 94.5 \n",
- "190 two hatchback fwd front 94.5 \n",
- "191 four sedan fwd front 100.4 \n",
- "192 four sedan fwd front 100.4 \n",
- "193 four wagon fwd front 100.4 \n",
- "194 four sedan rwd front 104.3 \n",
- "195 four wagon rwd front 104.3 \n",
- "196 four sedan rwd front 104.3 \n",
- "197 four wagon rwd front 104.3 \n",
- "198 four sedan rwd front 104.3 \n",
- "199 four wagon rwd front 104.3 \n",
- "200 four sedan rwd front 109.1 \n",
- "201 four sedan rwd front 109.1 \n",
- "202 four sedan rwd front 109.1 \n",
- "203 four sedan rwd front 109.1 \n",
- "204 four sedan rwd front 109999.1 \n",
- "\n",
- " ... engine-size fuel-system bore stroke compression-ratio \\\n",
- "0 ... 130 mpfi 3.47 2.68 9.00 \n",
- "1 ... 130 mpfi 3.47 2.68 9.00 \n",
- "2 ... 152 mpfi 2.68 3.47 9.00 \n",
- "3 ... 109 mpfi 3.19 3.40 10.00 \n",
- "4 ... 136 mpfi 3.19 3.40 8.00 \n",
- "5 ... 136 mpfi 3.19 3.40 8.50 \n",
- "6 ... 136 mpfi 3.19 3.40 8.50 \n",
- "7 ... 136 mpfi 3.19 3.40 8.50 \n",
- "8 ... 131 mpfi 3.13 3.40 8.30 \n",
- "9 ... 131 mpfi 3.13 3.40 7.00 \n",
- "10 ... 108 mpfi 3.50 2.80 8.80 \n",
- "11 ... 108 mpfi 3.50 2.80 8.80 \n",
- "12 ... 164 mpfi 3.31 3.19 9.00 \n",
- "13 ... 164 mpfi 3.31 3.19 9.00 \n",
- "14 ... 164 mpfi 3.31 3.19 9.00 \n",
- "15 ... 209 mpfi 3.62 3.39 8.00 \n",
- "16 ... 209 mpfi 3.62 3.39 8.00 \n",
- "17 ... 209 mpfi 3.62 3.39 8.00 \n",
- "18 ... 61 2bbl 2.91 3.03 9.50 \n",
- "19 ... 90 2bbl 3.03 3.11 9.60 \n",
- "20 ... 90 2bbl 3.03 3.11 9.60 \n",
- "21 ... 90 2bbl 2.97 3.23 9.41 \n",
- "22 ... 90 2bbl 2.97 3.23 9.40 \n",
- "23 ... 98 mpfi 3.03 3.39 7.60 \n",
- "24 ... 90 2bbl 2.97 3.23 9.40 \n",
- "25 ... 90 2bbl 2.97 3.23 9.40 \n",
- "26 ... 90 2bbl 2.97 3.23 9.40 \n",
- "28 ... 122 2bbl 3.34 3.46 8.50 \n",
- "29 ... 156 mfi 3.60 3.90 7.00 \n",
- "30 ... 92 1bbl 2.91 3.41 9.60 \n",
- ".. ... ... ... ... ... ... \n",
- "175 ... 122 mpfi 3.31 3.54 8.70 \n",
- "176 ... 122 mpfi 3.31 3.54 8.70 \n",
- "177 ... 122 mpfi 3.31 3.54 8.70 \n",
- "178 ... 171 mpfi 3.27 3.35 9.30 \n",
- "179 ... 171 mpfi 3.27 3.35 9.30 \n",
- "180 ... 171 mpfi 3.27 3.35 9.20 \n",
- "181 ... 161 mpfi 3.27 3.35 9.20 \n",
- "182 ... 97 idi 3.01 3.40 23.00 \n",
- "183 ... 109 mpfi 3.19 3.40 9.00 \n",
- "184 ... 97 idi 3.01 3.40 23.00 \n",
- "185 ... 109 mpfi 3.19 3.40 9.00 \n",
- "186 ... 109 mpfi 3.19 3.40 9.00 \n",
- "187 ... 97 idi 3.01 3.40 23.00 \n",
- "188 ... 109 mpfi 3.19 3.40 10.00 \n",
- "189 ... 109 mpfi 3.19 3.40 8.50 \n",
- "190 ... 109 mpfi 3.19 3.40 8.50 \n",
- "191 ... 136 mpfi 3.19 3.40 8.50 \n",
- "192 ... 97 idi 3.01 3.40 23.00 \n",
- "193 ... 109 mpfi 3.19 3.40 9.00 \n",
- "194 ... 141 mpfi 3.78 3.15 9.50 \n",
- "195 ... 141 mpfi 3.78 3.15 9.50 \n",
- "196 ... 141 mpfi 3.78 3.15 9.50 \n",
- "197 ... 141 mpfi 3.78 3.15 9.50 \n",
- "198 ... 130 mpfi 3.62 3.15 7.50 \n",
- "199 ... 130 mpfi 3.62 3.15 7.50 \n",
- "200 ... 141 mpfi 3.78 3.15 9.50 \n",
- "201 ... 141 mpfi 3.78 3.15 8.70 \n",
- "202 ... 173 mpfi 3.58 2.87 8.80 \n",
- "203 ... 145 idi 3.01 3.40 23.00 \n",
- "204 ... 141 mpfi 3.78 3.15 9.50 \n",
- "\n",
- " horsepower peak-rpm city-mpg highway-mpg price \n",
- "0 111.0 5000.0 21 27 13495.000000 \n",
- "1 111.0 5000.0 21 27 16500.000000 \n",
- "2 154.0 5000.0 19 26 16500.000000 \n",
- "3 102.0 5500.0 24 30 13950.000000 \n",
- "4 115.0 5500.0 18 22 17450.000000 \n",
- "5 110.0 5500.0 19 25 15250.000000 \n",
- "6 110.0 5500.0 19 25 17710.000000 \n",
- "7 110.0 5500.0 19 25 18920.000000 \n",
- "8 140.0 5500.0 17 20 23875.000000 \n",
- "9 160.0 5500.0 16 22 13207.129353 \n",
- "10 101.0 5800.0 23 29 16430.000000 \n",
- "11 101.0 5800.0 23 29 16925.000000 \n",
- "12 121.0 4250.0 21 28 20970.000000 \n",
- "13 121.0 4250.0 21 28 21105.000000 \n",
- "14 121.0 4250.0 20 25 24565.000000 \n",
- "15 182.0 5400.0 16 22 30760.000000 \n",
- "16 182.0 5400.0 16 22 41315.000000 \n",
- "17 182.0 5400.0 15 20 36880.000000 \n",
- "18 48.0 5100.0 47 53 5151.000000 \n",
- "19 70.0 5400.0 38 43 6295.000000 \n",
- "20 70.0 5400.0 38 43 6575.000000 \n",
- "21 68.0 5500.0 37 41 5572.000000 \n",
- "22 68.0 5500.0 31 38 6377.000000 \n",
- "23 102.0 5500.0 24 30 7957.000000 \n",
- "24 68.0 5500.0 31 38 6229.000000 \n",
- "25 68.0 5500.0 31 38 6692.000000 \n",
- "26 68.0 5500.0 31 38 7609.000000 \n",
- "28 88.0 5000.0 24 30 8921.000000 \n",
- "29 145.0 5000.0 19 24 12964.000000 \n",
- "30 58.0 4800.0 49 54 6479.000000 \n",
- ".. ... ... ... ... ... \n",
- "175 92.0 4200.0 27 32 9988.000000 \n",
- "176 92.0 4200.0 27 32 10898.000000 \n",
- "177 92.0 4200.0 27 32 11248.000000 \n",
- "178 161.0 5200.0 20 24 16558.000000 \n",
- "179 161.0 5200.0 19 24 15998.000000 \n",
- "180 156.0 5200.0 20 24 15690.000000 \n",
- "181 156.0 5200.0 19 24 15750.000000 \n",
- "182 52.0 4800.0 37 46 7775.000000 \n",
- "183 85.0 5250.0 27 34 7975.000000 \n",
- "184 52.0 4800.0 37 46 7995.000000 \n",
- "185 85.0 5250.0 27 34 8195.000000 \n",
- "186 85.0 5250.0 27 34 8495.000000 \n",
- "187 68.0 4500.0 37 42 9495.000000 \n",
- "188 100.0 5500.0 26 32 9995.000000 \n",
- "189 90.0 5500.0 24 29 11595.000000 \n",
- "190 90.0 5500.0 24 29 9980.000000 \n",
- "191 110.0 5500.0 19 24 13295.000000 \n",
- "192 68.0 4500.0 33 38 13845.000000 \n",
- "193 88.0 5500.0 25 31 12290.000000 \n",
- "194 114.0 5400.0 23 28 12940.000000 \n",
- "195 114.0 5400.0 23 28 13415.000000 \n",
- "196 114.0 5400.0 24 28 15985.000000 \n",
- "197 114.0 5400.0 24 28 16515.000000 \n",
- "198 162.0 5100.0 17 22 18420.000000 \n",
- "199 162.0 5100.0 17 22 18950.000000 \n",
- "200 114.0 5400.0 23 28 16845.000000 \n",
- "201 160.0 5300.0 19 25 19045.000000 \n",
- "202 134.0 5500.0 18 23 21485.000000 \n",
- "203 106.0 4800.0 26 27 22470.000000 \n",
- "204 114.0 5400.0 19 25 22625.000000 \n",
- "\n",
- "[203 rows x 26 columns]"
+ "symboling 205\n",
+ "normalized-losses 205\n",
+ "make 205\n",
+ "fuel-type 205\n",
+ "aspiration 205\n",
+ "num-of-doors 205\n",
+ "body-style 205\n",
+ "drive-wheels 205\n",
+ "engine-location 205\n",
+ "wheel-base 205\n",
+ "length 205\n",
+ "width 205\n",
+ "height 205\n",
+ "curb-weight 205\n",
+ "engine-type 205\n",
+ "num-of-cylinders 205\n",
+ "engine-size 205\n",
+ "fuel-system 205\n",
+ "bore 205\n",
+ "stroke 205\n",
+ "compression-ratio 205\n",
+ "horsepower 205\n",
+ "peak-rpm 205\n",
+ "city-mpg 205\n",
+ "highway-mpg 205\n",
+ "price 205\n",
+ "dtype: int64"
]
},
- "execution_count": 37,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -5377,13 +3639,15 @@
"#2 fill with mean then drop NaN\n",
"auto_mean = pd.read_csv('automobile.csv', na_values=[\"?\"])\n",
"auto_mean = auto_mean.fillna(auto_mean.mean())\n",
- "auto_mean = auto_mean.dropna()\n",
- "auto_mean"
+ "mode_num_door = auto_mean['num-of-doors'].mode()\n",
+ "auto_mean = auto_mean.fillna(value={'num-of-doors': mode_num_door[0]})\n",
+ "#auto_mean = auto_mean.dropna()\n",
+ "auto_mean.count()"
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 6,
"metadata": {
"scrolled": true
},
@@ -5420,7 +3684,7 @@
"dtype: int64"
]
},
- "execution_count": 33,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -5434,7 +3698,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 7,
"metadata": {
"scrolled": true
},
@@ -7209,7 +5473,7 @@
"[205 rows x 26 columns]"
]
},
- "execution_count": 30,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -7222,14 +5486,14 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
- "<matplotlib.figure.Figure at 0x29379cb6860>"
+ "<matplotlib.figure.Figure at 0x1d481fb8208>"
]
},
"metadata": {},
@@ -7237,10 +5501,239 @@
}
],
"source": [
+ "#3\n",
"sns.boxplot(y='price', data=auto_mean)\n",
"plt.show()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x1d482f4cba8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "price_kolom = auto_mean['price']\n",
+ "mean = price_kolom.mean()\n",
+ "std = price_kolom.std()\n",
+ "\n",
+ "batas_bawah = mean - 2 * std\n",
+ "batas_atas = mean + 2 * std\n",
+ "\n",
+ "final_price_without_outlier = [x for x in price_kolom if(x > batas_bawah)]\n",
+ "final_price_without_outlier = [x for x in final_price_without_outlier if(x < batas_atas)]\n",
+ "sns.boxplot(y=final_price_without_outlier)\n",
+ "final_price_without_outlier "
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.pdf b/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..56eed3c454c0b6968c71f3d9e68e7a4217df6b79
Binary files /dev/null and b/Lab_dsa_3/Soal/Jawaban_Lab3_AriefTritomo_1506689061.pdf differ
diff --git a/Lab_dsa_3/Soal/Tutorial_Lab3_AriefTritomo_1506689061.pdf b/Lab_dsa_3/Soal/Tutorial_Lab3_AriefTritomo_1506689061.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..559575d902dcf1d743933ad62238b3d1b07fcba9
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diff --git a/Lab_dsa_3/Soal/codejawaban.py b/Lab_dsa_3/Soal/codejawaban.py
new file mode 100644
index 0000000000000000000000000000000000000000..e93efc0d1a41aba429bd9539b7bc91b5e5e03b16
--- /dev/null
+++ b/Lab_dsa_3/Soal/codejawaban.py
@@ -0,0 +1,140 @@
+
+# coding: utf-8
+
+# In[2]:
+
+
+import numpy as np
+import pandas as pd
+import scipy.stats as stats
+import matplotlib.pyplot as plt
+import random
+import math
+import seaborn as sns
+
+
+# In[3]:
+
+
+#1
+auto = pd.read_csv('automobile.csv', na_values=["?"])
+auto.isnull().sum()
+
+
+# In[4]:
+
+
+#2 delete row
+auto = auto.dropna()
+auto
+
+
+# In[5]:
+
+
+#2 fill with mean then drop NaN
+auto_mean = pd.read_csv('automobile.csv', na_values=["?"])
+auto_mean = auto_mean.fillna(auto_mean.mean())
+auto_mean = auto_mean.dropna()
+auto_mean
+
+
+# In[6]:
+
+
+#2 fill with median
+auto_median = pd.read_csv('automobile.csv', na_values=["?"])
+auto_median = auto_median.fillna(auto_median.median())
+auto_median.isnull().sum()
+
+
+# In[7]:
+
+
+#2 fill with mode
+auto_mode = pd.read_csv('automobile.csv', na_values=["?"])
+auto_mode.fillna(auto_mode.mode())
+
+
+# In[8]:
+
+
+#3
+sns.boxplot(y='price', data=auto_mean)
+plt.show()
+
+
+# In[9]:
+
+
+#4 Handle Outlier
+price_kolom = auto_mean['price']
+mean = price_kolom.mean()
+std = price_kolom.std()
+
+batas_bawah = mean - 2 * std
+batas_atas = mean + 2 * std
+
+final_price_without_outlier = [x for x in price_kolom if(x > batas_bawah)]
+final_price_without_outlier = [x for x in final_price_without_outlier if(x < batas_atas)]
+final_price_without_outlier
+
+
+# In[23]:
+
+
+#5 pearson
+auto_mean.corr(method='pearson').style.format("{:.2}").background_gradient(cmap=plt.get_cmap('coolwarm'), axis=1)
+
+
+# In[24]:
+
+
+#5 spearman
+auto_mean.corr(method="spearman").style.format("{:.2}").background_gradient(cmap=plt.get_cmap('coolwarm'), axis=1)
+
+
+# In[80]:
+
+
+#5 linear Regression
+from sklearn.linear_model import LinearRegression
+feature_cols = ['wheel-base','length','width','curb-weight','engine-size','horsepower','normalized-losses','height','stroke','bore']
+x = auto_mean[feature_cols]
+y = auto_mean['price']
+linreg = LinearRegression()
+linreg.fit(x,y)
+print(feature_cols,linreg.intercept_)
+print(feature_cols,linreg.coef_)
+
+
+# In[56]:
+
+
+from sklearn import metrics
+y_pred = linreg.predict(x)
+np.sqrt(metrics.mean_squared_error(y, y_pred))
+
+
+# In[33]:
+
+
+# Logistic Regression
+from sklearn.linear_model import LogisticRegression
+from sklearn.cross_validation import train_test_split
+logreg = LogisticRegression()
+average_price = np.mean(auto_mean['price'])
+temp = auto_mean['price'] >= average_price
+feature_cols = ['wheel-base','length','width','curb-weight','engine-size','horsepower','normalized-losses','height','stroke','bore']
+x = auto_mean[feature_cols]
+y = temp
+X_train, X_test, y_train, y_test = train_test_split(x, y)
+logreg.fit(X_train, y_train)
+logreg.score(X_test, y_test)
+
+
+# In[57]:
+
+
+y_pred
+
diff --git a/lab_dsa_2/.ipynb_checkpoints/lab2_arief-checkpoint.ipynb b/lab_dsa_2/.ipynb_checkpoints/lab2_arief-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a0b40a6651a736d0b0d217fa0dd3fb013511c1e5
--- /dev/null
+++ b/lab_dsa_2/.ipynb_checkpoints/lab2_arief-checkpoint.ipynb
@@ -0,0 +1,440 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import scipy.stats as stats\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random\n",
+ "import math"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "43.002372"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.random.seed(10)\n",
+ "population_ages1 = stats.poisson.rvs(loc=18, mu=35, size=150000)\n",
+ "population_ages2 = stats.poisson.rvs(loc=18, mu=10, size=100000)\n",
+ "population_ages = np.concatenate((population_ages1, population_ages2))\n",
+ "\n",
+ "population_ages.mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42.388\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0.614372000000003"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.random.seed(6)\n",
+ "sample_ages = np.random.choice(a= population_ages, size=500)\n",
+ "print(sample_ages.mean())\n",
+ "population_ages.mean() - sample_ages.mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[7, 1, 14, 3, 12]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#optional_blue\n",
+ "c = list(range(0, 15))\n",
+ "random.sample(c, 5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": true
+ },
+ "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>City</th>\n",
+ " <th>Colors Reported</th>\n",
+ " <th>Shape Reported</th>\n",
+ " <th>State</th>\n",
+ " <th>Time</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>833</th>\n",
+ " <td>Oklahoma City</td>\n",
+ " <td>NaN</td>\n",
+ " <td>LIGHT</td>\n",
+ " <td>OK</td>\n",
+ " <td>7/15/1963 22:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15130</th>\n",
+ " <td>Lowell</td>\n",
+ " <td>NaN</td>\n",
+ " <td>RECTANGLE</td>\n",
+ " <td>IN</td>\n",
+ " <td>11/16/1999 18:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8081</th>\n",
+ " <td>Lebanon</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>OR</td>\n",
+ " <td>5/6/1995 23:30</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " City Colors Reported Shape Reported State Time\n",
+ "833 Oklahoma City NaN LIGHT OK 7/15/1963 22:00\n",
+ "15130 Lowell NaN RECTANGLE IN 11/16/1999 18:00\n",
+ "8081 Lebanon NaN NaN OR 5/6/1995 23:30"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "link = 'http://bit.ly/uforeports'\n",
+ "ufo = pd.read_csv(link)\n",
+ "ufo.sample(n=3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "79.65384615384616\n"
+ ]
+ }
+ ],
+ "source": [
+ "#optional_blue\n",
+ "nilai_DSA = [60,65,67,70,90,94,75,78,98,92,80,81.5,85]\n",
+ "print(np.mean(nilai_DSA))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "84.67857142857143\n"
+ ]
+ }
+ ],
+ "source": [
+ "#optional_blue\n",
+ "nilai_DSA = [60,65,67,70,90,94,75,78,98,92,80,81.5,85,150]\n",
+ "print(np.mean(nilai_DSA))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "80.0\n",
+ "80.75\n"
+ ]
+ }
+ ],
+ "source": [
+ "#optional_blue\n",
+ "nilai_DSA = [60,65,67,70,90,94,75,78,98,92,80,81.5,85]\n",
+ "print(np.median(nilai_DSA))\n",
+ "nilai_DSA = [60,65,67,70,90,94,75,78,98,92,80,81.5,85,150]\n",
+ "print(np.median(nilai_DSA))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "789.0833333333334\n",
+ "769.5\n",
+ "425.1862255399261\n"
+ ]
+ }
+ ],
+ "source": [
+ "#optional_blue\n",
+ "friends = [109,1017,1127,418,625,957,89,950,946,797,981,125,455,731,1640,485,1309,472,1132,1773,906,531,742,621]\n",
+ "print(np.mean(friends))\n",
+ "print(np.median(friends))\n",
+ "print(np.std(friends))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x248b1a691d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,0,'None')"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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LUh9nFPgnfLaqflxVp4BfHN/26vGve4B/A65kc17ITJvQWl8eWbqQ/AWjSP/9edZMXmtk8mqlmfjvn1bVR6Y8m7TmfIWvNqrqB4wurXvzxM1fZeGCfa8F/nWZpzkG/G6SZwEk2Z7kOdOeVVoLBl/dfID/f/30twBvSHIv8NvAH5zvwVX1T4xOAX0tyX8w+mc5L1mjWaWp8mqZktSEr/AlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJv4PFsyxeorkq3kAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x248b1dd0cc0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#optional_blue\n",
+ "y_pos = range(len(friends))\n",
+ "plt.bar(y_pos, friends)\n",
+ "plt.plot((0,25), (789,789), 'b-')\n",
+ "plt.plot((0,25), (789+425,789+425), 'g-')\n",
+ "plt.plot((0,25), (789-425,789-425), 'r-')\n",
+ "plt.xlabel(plt.show())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x248b1e7fa58>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "z_scores = []\n",
+ "m = np.mean(friends)\n",
+ "s = np.std(friends)\n",
+ "for friend in friends:\n",
+ " z = (friend - m)/s\n",
+ " z_scores.append(z)\n",
+ "plt.bar(y_pos, z_scores)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x248b1efd048>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(y_pos, z_scores)\n",
+ "plt.plot((0,25), (1,1), 'g-')\n",
+ "plt.plot((0,25), (0,0), 'b-')\n",
+ "plt.plot((0,25), (-1,-1), 'r-')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[109,\n",
+ " 1017,\n",
+ " 1127,\n",
+ " 418,\n",
+ " 625,\n",
+ " 957,\n",
+ " 89,\n",
+ " 950,\n",
+ " 946,\n",
+ " 797,\n",
+ " 981,\n",
+ " 125,\n",
+ " 455,\n",
+ " 731,\n",
+ " 1640,\n",
+ " 485,\n",
+ " 1309,\n",
+ " 472,\n",
+ " 1132,\n",
+ " 1773,\n",
+ " 906,\n",
+ " 531,\n",
+ " 742,\n",
+ " 621]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "friends = [109, 1017, 1127, 418, 625, 957,89,950,946,797,981,\n",
+ " 125, 455, 731, 1640, 485, 1309, 472, 1132, 1773, 906, 531, 742, 621]\n",
+ "friends"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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
+}
diff --git a/lab_dsa_2/lab2_arief.ipynb b/lab_dsa_2/lab2_arief.ipynb
index ed467f40b7c17db6dcc2ce041b7626d6190ea143..a0b40a6651a736d0b0d217fa0dd3fb013511c1e5 100644
--- a/lab_dsa_2/lab2_arief.ipynb
+++ b/lab_dsa_2/lab2_arief.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {
"scrolled": true
},
@@ -28,7 +28,7 @@
"43.002372"
]
},
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -44,7 +44,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -60,7 +60,7 @@
"0.614372000000003"
]
},
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -74,16 +74,16 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[4, 3, 0, 5, 7]"
+ "[7, 1, 14, 3, 12]"
]
},
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -96,42 +96,78 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
- "ename": "URLError",
- "evalue": "<urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mTimeoutError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m 1317\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[1;32m-> 1318\u001b[1;33m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[0;32m 1319\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m 1238\u001b[0m \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m 1284\u001b[0m \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m 1233\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1234\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m 1025\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 963\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 965\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 935\u001b[0m self.sock = self._create_connection(\n\u001b[1;32m--> 936\u001b[1;33m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[0;32m 937\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m 723\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 724\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 725\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m 712\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 713\u001b[1;33m \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 714\u001b[0m \u001b[1;31m# Break explicitly a reference cycle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001b[1;31mURLError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-10-0d34d4396123>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mlink\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'http://bit.ly/uforeports'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mufo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlink\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mufo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 708\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 709\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 431\u001b[0m \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 432\u001b[0m filepath_or_buffer, _, compression = get_filepath_or_buffer(\n\u001b[1;32m--> 433\u001b[1;33m filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[0;32m 434\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compression'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression)\u001b[0m\n\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 191\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Encoding'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gzip'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m 524\u001b[0m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 528\u001b[0m \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m 542\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 543\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 544\u001b[1;33m '_open', req)\n\u001b[0m\u001b[0;32m 545\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 505\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 506\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m 1344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1345\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1346\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1347\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1348\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m 1318\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m 1319\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1320\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1321\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1322\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mURLError\u001b[0m: <urlopen error [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond>"
- ]
+ "data": {
+ "text/html": [
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+ " .dataframe tbody tr th {\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>City</th>\n",
+ " <th>Colors Reported</th>\n",
+ " <th>Shape Reported</th>\n",
+ " <th>State</th>\n",
+ " <th>Time</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>833</th>\n",
+ " <td>Oklahoma City</td>\n",
+ " <td>NaN</td>\n",
+ " <td>LIGHT</td>\n",
+ " <td>OK</td>\n",
+ " <td>7/15/1963 22:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15130</th>\n",
+ " <td>Lowell</td>\n",
+ " <td>NaN</td>\n",
+ " <td>RECTANGLE</td>\n",
+ " <td>IN</td>\n",
+ " <td>11/16/1999 18:00</td>\n",
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+ " <tr>\n",
+ " <th>8081</th>\n",
+ " <td>Lebanon</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>OR</td>\n",
+ " <td>5/6/1995 23:30</td>\n",
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+ ],
+ "text/plain": [
+ " City Colors Reported Shape Reported State Time\n",
+ "833 Oklahoma City NaN LIGHT OK 7/15/1963 22:00\n",
+ "15130 Lowell NaN RECTANGLE IN 11/16/1999 18:00\n",
+ "8081 Lebanon NaN NaN OR 5/6/1995 23:30"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
@@ -142,7 +178,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -161,7 +197,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -180,7 +216,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -202,7 +238,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -225,14 +261,14 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0xae98dd8>"
+ "<matplotlib.figure.Figure at 0x248b1a691d0>"
]
},
"metadata": {},
@@ -244,7 +280,7 @@
"Text(0.5,0,'None')"
]
},
- "execution_count": 15,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
@@ -252,7 +288,7 @@
"data": {
"image/png": 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LUh9nFPgnfLaqflxVp4BfHN/26vGve4B/A65kc17ITJvQWl8eWbqQ/AWjSP/9edZMXmtk8mqlmfjvn1bVR6Y8m7TmfIWvNqrqB4wurXvzxM1fZeGCfa8F/nWZpzkG/G6SZwEk2Z7kOdOeVVoLBl/dfID/f/30twBvSHIv8NvAH5zvwVX1T4xOAX0tyX8w+mc5L1mjWaWp8mqZktSEr/AlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJv4PFsyxeorkq3kAAAAASUVORK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0xb1cc588>"
+ "<matplotlib.figure.Figure at 0x248b1dd0cc0>"
]
},
"metadata": {},
@@ -271,14 +307,14 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x8919a90>"
+ "<matplotlib.figure.Figure at 0x248b1e7fa58>"
]
},
"metadata": {},
@@ -298,14 +334,14 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0xb2b8128>"
+ "<matplotlib.figure.Figure at 0x248b1efd048>"
]
},
"metadata": {},
@@ -322,7 +358,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -354,7 +390,7 @@
" 621]"
]
},
- "execution_count": 18,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -365,6 +401,13 @@
"friends"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,