Jekyll2020-08-12T13:32:49-05:00https://tejakummarikuntla.github.io/notes/feed.xmltejakummarikuntlaLearn | Innovate | Deliver.Road Accident Analysis2020-08-12T00:00:00-05:002020-08-12T00:00:00-05:00https://tejakummarikuntla.github.io/notes/eda/2020/08/12/EDA_Road_Accident_Analysis<!--
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">scipy</span>
<span class="kn">import</span> <span class="nn">sklearn</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
<span class="kn">from</span> <span class="nn">pandas.plotting</span> <span class="kn">import</span> <span class="n">scatter_matrix</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">model_selection</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">acci</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'acc.csv'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s1">'LSOA_of_Accident_Location'</span><span class="p">:</span> <span class="nb">str</span><span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">acci</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<thead>
<tr style="text-align: right;">
<th></th>
<th>Accident_Index</th>
<th>Police_Force</th>
<th>Accident_Severity</th>
<th>Number_of_Vehicles</th>
<th>Number_of_Casualties</th>
<th>Date</th>
<th>Day_of_Week</th>
<th>Time</th>
<th>Local_Authority_(District)</th>
<th>Local_Authority_(Highway)</th>
<th>...</th>
<th>Pedestrian_Crossing-Human_Control</th>
<th>Pedestrian_Crossing-Physical_Facilities</th>
<th>Light_Conditions</th>
<th>Weather_Conditions</th>
<th>Road_Surface_Conditions</th>
<th>Special_Conditions_at_Site</th>
<th>Carriageway_Hazards</th>
<th>Urban_or_Rural_Area</th>
<th>Did_Police_Officer_Attend_Scene_of_Accident</th>
<th>LSOA_of_Accident_Location</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>200501BS00001</td>
<td>1</td>
<td>2</td>
<td>1</td>
<td>1</td>
<td>4/1/2005</td>
<td>3.0</td>
<td>17:42</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>2.0</td>
<td>2.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002849</td>
</tr>
<tr>
<th>1</th>
<td>200501BS00002</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>5/1/2005</td>
<td>4.0</td>
<td>17:36</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>5.0</td>
<td>4.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002909</td>
</tr>
<tr>
<th>2</th>
<td>200501BS00003</td>
<td>1</td>
<td>3</td>
<td>2</td>
<td>1</td>
<td>6/1/2005</td>
<td>5.0</td>
<td>0:15</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>4.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002857</td>
</tr>
<tr>
<th>3</th>
<td>200501BS00004</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>7/1/2005</td>
<td>6.0</td>
<td>10:35</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002840</td>
</tr>
<tr>
<th>4</th>
<td>200501BS00005</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>10/1/2005</td>
<td>2.0</td>
<td>21:13</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>7.0</td>
<td>1.0</td>
<td>2.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002863</td>
</tr>
</tbody>
</table>
<p>5 rows × 28 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">casu</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'casuality.csv'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vehi</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'vehicle.csv'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">kclt</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'KCLT1.CSV'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">acci</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">acci</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Accident_Index</th>
<th>Police_Force</th>
<th>Accident_Severity</th>
<th>Number_of_Vehicles</th>
<th>Number_of_Casualties</th>
<th>Date</th>
<th>Day_of_Week</th>
<th>Time</th>
<th>Local_Authority_(District)</th>
<th>Local_Authority_(Highway)</th>
<th>...</th>
<th>Pedestrian_Crossing-Human_Control</th>
<th>Pedestrian_Crossing-Physical_Facilities</th>
<th>Light_Conditions</th>
<th>Weather_Conditions</th>
<th>Road_Surface_Conditions</th>
<th>Special_Conditions_at_Site</th>
<th>Carriageway_Hazards</th>
<th>Urban_or_Rural_Area</th>
<th>Did_Police_Officer_Attend_Scene_of_Accident</th>
<th>LSOA_of_Accident_Location</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>200501BS00001</td>
<td>1</td>
<td>2</td>
<td>1</td>
<td>1</td>
<td>4/1/2005</td>
<td>3.0</td>
<td>17:42</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>2.0</td>
<td>2.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002849</td>
</tr>
<tr>
<th>1</th>
<td>200501BS00002</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>5/1/2005</td>
<td>4.0</td>
<td>17:36</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>5.0</td>
<td>4.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002909</td>
</tr>
<tr>
<th>2</th>
<td>200501BS00003</td>
<td>1</td>
<td>3</td>
<td>2</td>
<td>1</td>
<td>6/1/2005</td>
<td>5.0</td>
<td>0:15</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>4.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002857</td>
</tr>
<tr>
<th>3</th>
<td>200501BS00004</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>7/1/2005</td>
<td>6.0</td>
<td>10:35</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002840</td>
</tr>
<tr>
<th>4</th>
<td>200501BS00005</td>
<td>1</td>
<td>3</td>
<td>1</td>
<td>1</td>
<td>10/1/2005</td>
<td>2.0</td>
<td>21:13</td>
<td>12.0</td>
<td>E09000020</td>
<td>...</td>
<td>0.0</td>
<td>0.0</td>
<td>7.0</td>
<td>1.0</td>
<td>2.0</td>
<td>0.0</td>
<td>0.0</td>
<td>1.0</td>
<td>1.0</td>
<td>E01002863</td>
</tr>
</tbody>
</table>
<p>5 rows × 28 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">casu</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<tr style="text-align: right;">
<th></th>
<th>Vehicle_Reference</th>
<th>Casualty_Reference</th>
<th>Casualty_Class</th>
<th>Sex_of_Casualty</th>
<th>Age_of_Casualty</th>
<th>Age_Band_of_Casualty</th>
<th>Casualty_Severity</th>
<th>Pedestrian_Location</th>
<th>Pedestrian_Movement</th>
<th>Car_Passenger</th>
<th>Bus_or_Coach_Passenger</th>
<th>Pedestrian_Road_Maintenance_Worker</th>
<th>Casualty_Type</th>
<th>Casualty_Home_Area_Type</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>713000.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.000000</td>
<td>712999.0</td>
<td>712999.000000</td>
<td>712999.000000</td>
</tr>
<tr>
<th>mean</th>
<td>1.478285</td>
<td>1.437173</td>
<td>1.496647</td>
<td>1.415758</td>
<td>33.241136</td>
<td>5.871056</td>
<td>2.867031</td>
<td>0.654889</td>
<td>0.456155</td>
<td>0.293881</td>
<td>0.090340</td>
<td>-1.0</td>
<td>7.635827</td>
<td>0.889673</td>
</tr>
<tr>
<th>std</th>
<td>0.619397</td>
<td>1.101567</td>
<td>0.701956</td>
<td>0.495390</td>
<td>18.632343</td>
<td>2.431920</td>
<td>0.372657</td>
<td>1.945577</td>
<td>1.620012</td>
<td>0.600751</td>
<td>0.565519</td>
<td>0.0</td>
<td>6.679438</td>
<td>1.084864</td>
</tr>
<tr>
<th>min</th>
<td>1.000000</td>
<td>1.000000</td>
<td>1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.0</td>
<td>0.000000</td>
<td>-1.000000</td>
</tr>
<tr>
<th>25%</th>
<td>1.000000</td>
<td>1.000000</td>
<td>1.000000</td>
<td>1.000000</td>
<td>19.000000</td>
<td>4.000000</td>
<td>3.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>-1.0</td>
<td>5.000000</td>
<td>1.000000</td>
</tr>
<tr>
<th>50%</th>
<td>1.000000</td>
<td>1.000000</td>
<td>1.000000</td>
<td>1.000000</td>
<td>30.000000</td>
<td>6.000000</td>
<td>3.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>-1.0</td>
<td>9.000000</td>
<td>1.000000</td>
</tr>
<tr>
<th>75%</th>
<td>2.000000</td>
<td>2.000000</td>
<td>2.000000</td>
<td>2.000000</td>
<td>45.000000</td>
<td>7.000000</td>
<td>3.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>-1.0</td>
<td>9.000000</td>
<td>1.000000</td>
</tr>
<tr>
<th>max</th>
<td>19.000000</td>
<td>68.000000</td>
<td>3.000000</td>
<td>2.000000</td>
<td>99.000000</td>
<td>11.000000</td>
<td>3.000000</td>
<td>10.000000</td>
<td>9.000000</td>
<td>2.000000</td>
<td>4.000000</td>
<td>-1.0</td>
<td>90.000000</td>
<td>3.000000</td>
</tr>
</tbody>
</table>
</div>
</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">casu</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
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<th></th>
<th>Accident_Index</th>
<th>Vehicle_Reference</th>
<th>Casualty_Reference</th>
<th>Casualty_Class</th>
<th>Sex_of_Casualty</th>
<th>Age_of_Casualty</th>
<th>Age_Band_of_Casualty</th>
<th>Casualty_Severity</th>
<th>Pedestrian_Location</th>
<th>Pedestrian_Movement</th>
<th>Car_Passenger</th>
<th>Bus_or_Coach_Passenger</th>
<th>Pedestrian_Road_Maintenance_Worker</th>
<th>Casualty_Type</th>
<th>Casualty_Home_Area_Type</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>200501BS00001</td>
<td>1</td>
<td>1.0</td>
<td>3.0</td>
<td>1.0</td>
<td>56.0</td>
<td>7.0</td>
<td>2.0</td>
<td>1.0</td>
<td>1.0</td>
<td>0.0</td>
<td>0.0</td>
<td>-1.0</td>
<td>0.0</td>
<td>1.0</td>
</tr>
<tr>
<th>1</th>
<td>200501BS00002</td>
<td>1</td>
<td>1.0</td>
<td>2.0</td>
<td>1.0</td>
<td>47.0</td>
<td>7.0</td>
<td>3.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>4.0</td>
<td>-1.0</td>
<td>11.0</td>
<td>1.0</td>
</tr>
<tr>
<th>2</th>
<td>200501BS00003</td>
<td>2</td>
<td>1.0</td>
<td>1.0</td>
<td>1.0</td>
<td>62.0</td>
<td>9.0</td>
<td>3.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>-1.0</td>
<td>9.0</td>
<td>1.0</td>
</tr>
<tr>
<th>3</th>
<td>200501BS00004</td>
<td>1</td>
<td>1.0</td>
<td>3.0</td>
<td>2.0</td>
<td>30.0</td>
<td>6.0</td>
<td>3.0</td>
<td>5.0</td>
<td>2.0</td>
<td>0.0</td>
<td>0.0</td>
<td>-1.0</td>
<td>0.0</td>
<td>1.0</td>
</tr>
<tr>
<th>4</th>
<td>200501BS00005</td>
<td>1</td>
<td>1.0</td>
<td>1.0</td>
<td>1.0</td>
<td>49.0</td>
<td>8.0</td>
<td>3.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>0.0</td>
<td>-1.0</td>
<td>3.0</td>
<td>-1.0</td>
</tr>
</tbody>
</table>
</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vehi</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Vehicle_Reference</th>
<th>Vehicle_Type</th>
<th>Towing_and_Articulation</th>
<th>Vehicle_Manoeuvre</th>
<th>Vehicle_Location-Restricted_Lane</th>
<th>Junction_Location</th>
<th>Skidding_and_Overturning</th>
<th>Hit_Object_in_Carriageway</th>
<th>Vehicle_Leaving_Carriageway</th>
<th>Hit_Object_off_Carriageway</th>
<th>...</th>
<th>Was_Vehicle_Left_Hand_Drive?</th>
<th>Journey_Purpose_of_Driver</th>
<th>Sex_of_Driver</th>
<th>Age_of_Driver</th>
<th>Age_Band_of_Driver</th>
<th>Engine_Capacity_(CC)</th>
<th>Propulsion_Code</th>
<th>Age_of_Vehicle</th>
<th>Driver_IMD_Decile</th>
<th>Driver_Home_Area_Type</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>...</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
<td>866223.000000</td>
</tr>
<tr>
<th>mean</th>
<td>1.559869</td>
<td>9.667678</td>
<td>0.032816</td>
<td>12.772120</td>
<td>0.123591</td>
<td>2.411406</td>
<td>0.226169</td>
<td>0.309478</td>
<td>0.378220</td>
<td>0.571697</td>
<td>...</td>
<td>0.951786</td>
<td>10.730304</td>
<td>1.386455</td>
<td>33.147417</td>
<td>5.695801</td>
<td>1339.853980</td>
<td>0.622576</td>
<td>4.089737</td>
<td>3.391802</td>
<td>0.795520</td>
</tr>
<tr>
<th>std</th>
<td>0.741835</td>
<td>7.631000</td>
<td>0.312365</td>
<td>6.155444</td>
<td>0.953635</td>
<td>3.100804</td>
<td>0.721344</td>
<td>1.623085</td>
<td>1.400388</td>
<td>2.099478</td>
<td>...</td>
<td>0.328831</td>
<td>6.420805</td>
<td>0.591698</td>
<td>19.068999</td>
<td>2.933082</td>
<td>1714.813295</td>
<td>1.133182</td>
<td>5.023302</td>
<td>3.714446</td>
<td>1.133638</td>
</tr>
<tr>
<th>min</th>
<td>1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>...</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
</tr>
<tr>
<th>25%</th>
<td>1.000000</td>
<td>9.000000</td>
<td>0.000000</td>
<td>7.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>...</td>
<td>1.000000</td>
<td>2.000000</td>
<td>1.000000</td>
<td>21.000000</td>
<td>5.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>-1.000000</td>
<td>1.000000</td>
</tr>
<tr>
<th>50%</th>
<td>1.000000</td>
<td>9.000000</td>
<td>0.000000</td>
<td>17.000000</td>
<td>0.000000</td>
<td>1.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>...</td>
<td>1.000000</td>
<td>15.000000</td>
<td>1.000000</td>
<td>33.000000</td>
<td>6.000000</td>
<td>1348.000000</td>
<td>1.000000</td>
<td>3.000000</td>
<td>3.000000</td>
<td>1.000000</td>
</tr>
<tr>
<th>75%</th>
<td>2.000000</td>
<td>9.000000</td>
<td>0.000000</td>
<td>18.000000</td>
<td>0.000000</td>
<td>5.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>...</td>
<td>1.000000</td>
<td>15.000000</td>
<td>2.000000</td>
<td>45.000000</td>
<td>7.000000</td>
<td>1799.000000</td>
<td>1.000000</td>
<td>8.000000</td>
<td>7.000000</td>
<td>1.000000</td>
</tr>
<tr>
<th>max</th>
<td>22.000000</td>
<td>90.000000</td>
<td>5.000000</td>
<td>18.000000</td>
<td>9.000000</td>
<td>8.000000</td>
<td>5.000000</td>
<td>12.000000</td>
<td>8.000000</td>
<td>10.000000</td>
<td>...</td>
<td>2.000000</td>
<td>15.000000</td>
<td>3.000000</td>
<td>99.000000</td>
<td>11.000000</td>
<td>99999.000000</td>
<td>10.000000</td>
<td>87.000000</td>
<td>10.000000</td>
<td>3.000000</td>
</tr>
</tbody>
</table>
<p>8 rows × 21 columns</p>
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<p><strong>What fraction of accidents occur in urban, rural and other (na) areas?</strong></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">urban_acci</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Urban_or_Rural_Area'</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">])</span>
<span class="n">rural_acci</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Urban_or_Rural_Area'</span><span class="p">]</span><span class="o">==</span><span class="mi">2</span><span class="p">])</span>
<span class="n">na_acci</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Urban_or_Rural_Area'</span><span class="p">]</span><span class="o">==</span><span class="mi">3</span><span class="p">])</span>
<span class="n">total_acci</span> <span class="o">=</span> <span class="n">urban_acci</span> <span class="o">+</span> <span class="n">rural_acci</span> <span class="o">+</span> <span class="n">na_acci</span>
<span class="n">urban_pct</span> <span class="o">=</span> <span class="n">urban_acci</span><span class="o">*</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">total_acci</span> <span class="o">*</span> <span class="mi">100</span>
<span class="n">rural_pct</span> <span class="o">=</span> <span class="n">rural_acci</span><span class="o">*</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">total_acci</span> <span class="o">*</span><span class="mi">100</span>
<span class="n">na_pct</span> <span class="o">=</span> <span class="n">na_acci</span><span class="o">*</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">total_acci</span> <span class="o">*</span> <span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Percentage of accidents occur in urban areas is </span><span class="si">{0:.0f}</span><span class="s2">%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">urban_pct</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Percentage of accidents occur in rural areas is </span><span class="si">{0:.0f}</span><span class="s2">%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rural_pct</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Percentage of accidents occur in other areas is </span><span class="si">{0:.0f}</span><span class="s2">%"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">na_pct</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'1'</span><span class="p">,</span> <span class="s1">'2'</span><span class="p">,</span> <span class="s1">'3'</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">urban_pct</span><span class="p">,</span> <span class="n">rural_pct</span><span class="p">,</span> <span class="n">na_pct</span><span class="p">]</span>
<span class="n">x_pos</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Percentage of accidents'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Percentage of accidents occured by area"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Percentage of accidents occur in urban areas is 81%
Percentage of accidents occur in rural areas is 19%
Percentage of accidents occur in other areas is 0%
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<p><strong>When is the most dangerous time to drive?</strong></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">acci</span><span class="p">[</span><span class="s1">'Hour'</span><span class="p">]</span> <span class="o">=</span> <span class="n">acci</span><span class="p">[</span><span class="s1">'Time'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">':'</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># print(acci['Hour'].describe())</span>
<span class="n">acci</span><span class="p">[</span><span class="s1">'Hour'</span><span class="p">]</span> <span class="o">=</span> <span class="n">acci</span><span class="p">[</span><span class="s1">'Hour'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="n">hour</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">num_of_fatal_acci</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">num_of_acci</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">24</span><span class="p">):</span>
<span class="n">hour</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">num_of_fatal_acci_hour</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[(</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Accident_Severity'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Hour'</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">)])</span>
<span class="n">num_of_acci_hour</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Hour'</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">])</span>
<span class="n">num_of_fatal_acci</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">num_of_fatal_acci_hour</span><span class="p">)</span>
<span class="n">num_of_acci</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">num_of_acci_hour</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">hour</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">num_of_fatal_acci</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">num_of_acci</span><span class="p">)</span>
<span class="n">normalized_num_of_fatal_acci</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">num_of_fatal_acci</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">num_of_acci</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
<span class="c1"># print(max(normalized_num_of_fatal_acci))</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">221</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">hour</span><span class="p">,</span> <span class="n">num_of_fatal_acci</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Number of fatal accidents'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Hour'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">222</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">hour</span><span class="p">,</span> <span class="n">num_of_acci</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Number of all accidents'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Hour'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax3</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">223</span><span class="p">)</span>
<span class="n">ax3</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">hour</span><span class="p">,</span> <span class="n">normalized_num_of_fatal_acci</span><span class="p">)</span>
<span class="n">ax3</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Percentage of fatal accidents in all accidents'</span><span class="p">)</span>
<span class="n">ax3</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Hour'</span><span class="p">)</span>
<span class="n">ax3</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"The most dangerous hour to drive, when most fatal accidents happend in all accidents, is </span><span class="si">{}</span><span class="s2"> o'clock"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">normalized_num_of_fatal_acci</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">normalized_num_of_fatal_acci</span><span class="p">))))</span>
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<pre>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
[43, 39, 36, 16, 14, 15, 23, 31, 35, 31, 42, 34, 51, 44, 56, 56, 56, 64, 62, 61, 50, 53, 54, 41]
[1460, 1082, 933, 559, 399, 536, 1222, 3121, 5663, 4144, 3827, 4443, 5351, 5433, 5369, 6882, 7090, 7645, 6085, 4845, 3814, 3029, 2500, 2282]
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<pre>The most dangerous hour to drive, when most fatal accidents happend in all accidents, is 2 o'clock
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#gender_fatal_car = vehi[['Accident_Index','Sex_of_Driver']]</span>
<span class="c1">#print (len(gender_fatal_car['Accident_Index']))</span>
<span class="n">temp</span> <span class="o">=</span> <span class="n">acci</span><span class="p">[[</span><span class="s1">'Accident_Index'</span><span class="p">,</span> <span class="s1">'Accident_Severity'</span><span class="p">]]</span>
<span class="c1">#print (len(temp))</span>
<span class="c1">#gender_fatal_car=gender_fatal_car.head().merge(temp.head(), on = 'Accident_Index', how = 'left')</span>
<span class="n">temp1</span><span class="o">=</span><span class="n">vehi</span><span class="p">[[</span><span class="s1">'Sex_of_Driver'</span><span class="p">]]</span>
<span class="c1">#print (temp.head())</span>
<span class="c1">#print (temp1['Sex_of_Driver'].head())</span>
<span class="n">x</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">temp</span><span class="p">,</span><span class="n">temp1</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[(</span><span class="n">x</span><span class="p">[</span><span class="s1">'Sex_of_Driver'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s1">'Accident_Severity'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)])</span>
<span class="n">f</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[(</span><span class="n">x</span><span class="p">[</span><span class="s1">'Sex_of_Driver'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s1">'Accident_Severity'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
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<pre>680 276
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<p><strong>What is the trend in the number of accidents that occur each year?</strong></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">acci</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">acci</span><span class="p">[</span><span class="s1">'Accident_Index'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)[:</span><span class="mi">4</span><span class="p">])</span>
<span class="n">acci</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">acci</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="c1"># print(acci['Year'].head())</span>
<span class="n">year</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">num_of_acci_year</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2005</span><span class="p">,</span> <span class="mi">2015</span><span class="p">):</span>
<span class="n">year</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">num_of_acci_year</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">]))</span>
<span class="c1"># print(year)</span>
<span class="c1"># print(num_of_acci_year)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">year</span><span class="p">,</span> <span class="n">num_of_acci_year</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Year'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Number of accidents'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Correlation between number of accidents and year'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># slope, intercept = np.polyfit(year, num_of_acci_year, 1)</span>
<span class="c1"># print("{:.1f}".format(slope))</span>
<span class="c1"># print(intercept)</span>
</pre></div>
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<p><strong>Do accidents in high-speed-limit areas have more casualties?</strong></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print(set(acci['Speed_limit']))</span>
<span class="n">speed_limit</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">num_casualty</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">num_acci</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ratio</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Speed_limit'</span><span class="p">]))):</span>
<span class="n">speed_limit</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">casualty</span> <span class="o">=</span> <span class="n">acci</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Speed_limit'</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">,</span> <span class="s1">'Number_of_Casualties'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">num_casualty</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">casualty</span><span class="p">)</span>
<span class="n">accident</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">acci</span><span class="p">[(</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Speed_limit'</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">)])</span>
<span class="n">num_acci</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">accident</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">casualty</span> <span class="o">/</span> <span class="n">accident</span>
<span class="n">ratio</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
<span class="c1"># print(speed_limit)</span>
<span class="c1"># print(num_casualty)</span>
<span class="c1"># print(num_acci)</span>
<span class="c1"># print(ratio)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">speed_limit</span><span class="p">,</span> <span class="n">ratio</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Speed limit'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Casualty per accident, average'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Correlation between casualty per accident and speed limit'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre><ipython-input-19-248ccd0298b6>:13: RuntimeWarning: invalid value encountered in longlong_scalars
r = casualty / accident
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<p><strong>How fast do the number of car accidents drop off with age?</strong> (Only consider car drivers who are legally allowed to drive in the UK: 17 years or older)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">age_acci</span> <span class="o">=</span> <span class="n">vehi</span><span class="p">[[</span><span class="s1">'Accident_Index'</span><span class="p">,</span> <span class="s1">'Age_of_Driver'</span><span class="p">,</span> <span class="s1">'Vehicle_Type'</span><span class="p">]]</span>
<span class="c1"># print(age_acci.head())</span>
<span class="c1"># print(max(age_acci['Age_of_Driver']))</span>
<span class="n">age</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">num_of_acci</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">age_acci</span><span class="p">[</span><span class="s1">'Age_of_Driver'</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
<span class="n">age</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">num_of_acci</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">age_acci</span><span class="p">[(</span><span class="n">age_acci</span><span class="p">[</span><span class="s1">'Age_of_Driver'</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">age_acci</span><span class="p">[</span><span class="s1">'Vehicle_Type'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">9</span><span class="p">)]))</span>
<span class="c1"># print(age)</span>
<span class="c1"># print(num_of_acci)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">num_of_acci</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">'Data'</span><span class="p">,</span> <span class="n">marker</span> <span class="o">=</span> <span class="s1">'o'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Age'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Number of car accidents'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Correlation between driver age and number of car accidents'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">accidents</span><span class="o">=</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Accident_Index'</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">25</span><span class="p">]</span>
<span class="n">LightConditions</span><span class="o">=</span><span class="n">acci</span><span class="p">[</span><span class="s1">'Light_Conditions'</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">25</span><span class="p">]</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">LightConditions</span><span class="p">,</span><span class="n">accidents</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Light_Conditions'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Accidents'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'no of accidents occurred as per light conditions'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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</div>Teja KummarikuntlaStack and Recursion2020-08-11T00:00:00-05:002020-08-11T00:00:00-05:00https://tejakummarikuntla.github.io/notes/2020/08/11/Untitled<!--
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<h1 id="Applications">Applications<a class="anchor-link" href="#Applications"> </a></h1><ul>
<li>In stack-oriented programming languages</li>
<li>Graph algorithms: depth-first search can be implemented with stacks (or with recursion)</li>
<li>Finding Euler-cycles in a graph</li>
<li>Finding strongly connecteed components in a graphh</li>
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</div>Title2020-08-11T00:00:00-05:002020-08-11T00:00:00-05:00https://tejakummarikuntla.github.io/notes/2020/08/11/Inversions-in-an-array<!--
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<h2 id="Number-of-Inversions-in-an-array">Number of Inversions in an array<a class="anchor-link" href="#Number-of-Inversions-in-an-array"> </a></h2><p>Inversion: <code>i < j and arr[i] > arr[j]</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">countInversions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nums</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">])</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="n">invCount</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="k">if</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">></span> <span class="n">nums</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
<span class="n">invCount</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">invCount</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
<span class="n">sol</span><span class="o">.</span><span class="n">countInversions</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
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<pre>(2,1)
(5,1)
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<pre>2</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">mergeSort</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
<span class="n">mid</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span>
<span class="n">leftArr</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[:</span><span class="n">mid</span><span class="p">]</span>
<span class="n">rightArr</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[</span><span class="n">mid</span><span class="p">:]</span>
<span class="n">mergeSort</span><span class="p">(</span><span class="n">leftArr</span><span class="p">)</span>
<span class="n">mergeSort</span><span class="p">(</span><span class="n">rightArr</span><span class="p">)</span>
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</div>Wiggle Sort2020-06-15T00:00:00-05:002020-06-15T00:00:00-05:00https://tejakummarikuntla.github.io/notes/problem%20solving/leetcode/2020/06/15/Wiggle-Sort<!--
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<h1 id="Problem-statement:">Problem statement:<a class="anchor-link" href="#Problem-statement:"> </a></h1><p>Given an unsorted array nums, reorder it in-place such that nums[0] <= nums[1] >= nums[2] <= nums[3]....
<a href="https://leetcode.com/problems/wiggle-sort/">URL</a></p>
<h2 id="Example:">Example:<a class="anchor-link" href="#Example:"> </a></h2>
<pre><code>Input: nums = [3,5,2,1,6,4]
Output: One possible answer is [3,5,1,6,2,4]</code></pre>
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<h1 id="Approach-1">Approach 1<a class="anchor-link" href="#Approach-1"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">wiggleSort</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nums</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">])</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="n">nums</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
<span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">return</span> <span class="n">nums</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">wiggleSort</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span>
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<pre>[1, 3, 2, 5, 4, 6]</pre>
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<p><strong>Worst case performance in Time: $O(nlogn)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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<h1 id="Approach-2">Approach 2<a class="anchor-link" href="#Approach-2"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">wiggleSort</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nums</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">])</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">),</span> <span class="mi">2</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span><span class="o">></span><span class="mi">0</span> <span class="ow">and</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
<span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">)</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">and</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]:</span>
<span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">return</span> <span class="n">nums</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">wiggleSort</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span>
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<pre>[3, 5, 1, 6, 2, 4]</pre>
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<p><strong>Worst case performance in Time: $O(n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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</div>Teja KummarikuntlaStacks and Queues2020-06-08T00:00:00-05:002020-06-08T00:00:00-05:00https://tejakummarikuntla.github.io/notes/data%20structures/2020/06/08/Stacks-and-Queues<!--
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<h1 id="Stack-and-Recursion">Stack and Recursion<a class="anchor-link" href="#Stack-and-Recursion"> </a></h1><ul>
<li>There aree several situvations when recursive methods are quite handy</li>
<li>For example: DFS, traversing a binary search tree, looking foir an item in a linkedlist</li>
<li>All the recursive algorithms can be transformed into a simple methhod with stacks
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<strong>Important: </strong>If we use recursion, the OS will use stacks anyways.
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<p>what does it all have to do with stacks? The recursive functions calls are pushed onto the stack until we bump into the base case</p>
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<li>We keep backtrcking: we know the base case so we know the subsolutions.</li>
<li>If there are too many function calls to be pushed onto the stack: The stack may get full, no more space left. </li>
<li>Called <code>Stack Overflow</code></li>
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<h1 id="Applications">Applications<a class="anchor-link" href="#Applications"> </a></h1><ul>
<li>In stack-oriented programming languages</li>
<li>Graph algorithms: depth-first search can be implemented with stacks (or with recursion)</li>
<li>Finding Euler-cycles in a graph</li>
<li>Finding strongly connecteed components in a graphh</li>
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<h1 id="Implementation-of-Stack">Implementation of Stack<a class="anchor-link" href="#Implementation-of-Stack"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="k">class</span> <span class="nc">Stack</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stack</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">def</span> <span class="nf">isEmpty</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stack</span> <span class="o">==</span> <span class="p">[]</span>
<span class="k">def</span> <span class="nf">push</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">element</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">element</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">pop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">isEmpty</span><span class="p">():</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">def</span> <span class="nf">peek</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">isEmpty</span><span class="p">():</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">def</span> <span class="nf">display</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">)):</span>
<span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stack</span> <span class="o">=</span> <span class="n">Stack</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stack</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
<span class="n">stack</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">stack</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
<span class="n">stack</span><span class="o">.</span><span class="n">peek</span><span class="p">()</span>
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<pre>20</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stack</span><span class="o">.</span><span class="n">display</span><span class="p">()</span>
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<pre>10
20
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<h2 id="Twin">Twin<a class="anchor-link" href="#Twin"> </a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="k">class</span> <span class="nc">Stack</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span>
<span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="o">=-</span><span class="mi">1</span>
<span class="k">def</span> <span class="nf">isFull</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="o">==</span><span class="mi">100</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="k">def</span> <span class="nf">isEmpty</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="o">==-</span><span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="k">def</span> <span class="nf">push</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isFull</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"stack overflow"</span><span class="p">)</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="o">+=</span><span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="p">]</span><span class="o">=</span><span class="n">data</span>
<span class="k">def</span> <span class="nf">pop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isEmpty</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Stack Underflow"</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">d</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="o">-=</span><span class="mi">1</span>
<span class="k">return</span> <span class="n">d</span>
<span class="k">def</span> <span class="nf">peek</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isEmpty</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Stack Underflow"</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">d</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">top</span><span class="p">]</span>
<span class="k">return</span> <span class="n">d</span>
<span class="k">def</span> <span class="nf">display</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isEmpty</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Stack Underflow"</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">temp</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">top</span>
<span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">temp</span><span class="o">>=</span><span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">temp</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">temp</span><span class="o">-=</span><span class="mi">1</span>
<span class="k">if</span> <span class="vm">__name__</span><span class="o">==</span><span class="s1">'__main__'</span><span class="p">:</span>
<span class="n">s</span><span class="o">=</span><span class="n">Stack</span><span class="p">()</span>
<span class="k">while</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"1.Push</span><span class="se">\n</span><span class="s2">"</span><span class="p">);</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"2.Pop</span><span class="se">\n</span><span class="s2">"</span><span class="p">);</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"3.Display the top element</span><span class="se">\n</span><span class="s2">"</span><span class="p">);</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"4.Display all stack elements</span><span class="se">\n</span><span class="s2">"</span><span class="p">);</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"5.Quit</span><span class="se">\n</span><span class="s2">"</span><span class="p">);</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Enter your choice : "</span><span class="p">);</span>
<span class="n">choice</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="nb">input</span><span class="p">())</span>
<span class="k">if</span> <span class="n">choice</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span>
<span class="n">value</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="nb">input</span><span class="p">())</span>
<span class="n">s</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">choice</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
<span class="n">d</span><span class="o">=</span><span class="n">s</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"poped value"</span><span class="p">,</span><span class="n">d</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">choice</span><span class="o">==</span><span class="mi">3</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"top of the element"</span><span class="p">,</span><span class="n">s</span><span class="o">.</span><span class="n">peek</span><span class="p">())</span>
<span class="k">elif</span> <span class="n">choice</span><span class="o">==</span><span class="mi">4</span><span class="p">:</span>
<span class="n">s</span><span class="o">.</span><span class="n">display</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<pre>1.Push
2.Pop
3.Display the top element
4.Display all stack elements
5.Quit
Enter your choice :
1
10
1.Push
2.Pop
3.Display the top element
4.Display all stack elements
5.Quit
Enter your choice :
1
20
1.Push
2.Pop
3.Display the top element
4.Display all stack elements
5.Quit
Enter your choice :
1
30
1.Push
2.Pop
3.Display the top element
4.Display all stack elements
5.Quit
Enter your choice :
4
30
20
10
1.Push
2.Pop
3.Display the top element
4.Display all stack elements
5.Quit
Enter your choice :
5
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<pre>
An exception has occurred, use %tb to see the full traceback.
<span class="ansi-red-intense-fg ansi-bold">SystemExit</span><span class="ansi-red-intense-fg ansi-bold">:</span> 0
</pre>
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</div>Teja KummarikuntlaFirst Missing Positive2020-05-29T00:00:00-05:002020-05-29T00:00:00-05:00https://tejakummarikuntla.github.io/notes/problem%20solving/leetcode/2020/05/29/First-missing-positive<!--
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<h2 id="Problem-Statement">Problem Statement<a class="anchor-link" href="#Problem-Statement"> </a></h2><p>Given an unsorted integer array, find the smallest missing positive integer.
<a href="https://leetcode.com/problems/first-missing-positive/">[URL]</a></p>
<h3 id="Example-1:">Example 1:<a class="anchor-link" href="#Example-1:"> </a></h3>
<pre><code>Input: [1,2,0]
Output: 3</code></pre>
<h3 id="Example-2:">Example 2:<a class="anchor-link" href="#Example-2:"> </a></h3>
<pre><code>Input: [3,4,-1,1]
Output: 2</code></pre>
<h3 id="Example-3:">Example 3:<a class="anchor-link" href="#Example-3:"> </a></h3>
<pre><code>Input: [7,8,9,11,12]
Output: 1</code></pre>
<p><div class="flash">
<svg class="octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 01-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 01-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg>
<strong>Note: </strong>Your algorithm should run in O(n) time and uses constant extra space.
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<h1 id="Approach-1">Approach 1<a class="anchor-link" href="#Approach-1"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">firstMissingPositive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nums</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
<span class="n">result</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">nums</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">nums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">result</span><span class="p">:</span>
<span class="n">result</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">result</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">firstMissingPositive</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">])</span>
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<pre>1</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/firstMissingPositive/approach1_sub.PNG" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(nlogn)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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</div>
</div>Teja KummarikuntlaIncreasing Triplet Subsequence2020-05-29T00:00:00-05:002020-05-29T00:00:00-05:00https://tejakummarikuntla.github.io/notes/problem%20solving/leetcode/2020/05/29/First-missing-positive-Copy1<!--
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<h2 id="Problem-Statement">Problem Statement<a class="anchor-link" href="#Problem-Statement"> </a></h2><p>Given an unsorted array return whether an increasing subsequence of length 3 exists or not in the array.
Formally the function should:</p>
<p>Return true if there exists i, j, k
such that <code>arr[i] < arr[j] < arr[k]</code> given <code>0 ≤ i < j < k ≤ n-1</code> else <code>return false</code>.
<a href="https://leetcode.com/problems/increasing-triplet-subsequence/">[URL]</a>
<div class="flash">
<svg class="octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info octicon octicon-info" viewBox="0 0 14 16" version="1.1" width="14" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M6.3 5.69a.942.942 0 01-.28-.7c0-.28.09-.52.28-.7.19-.18.42-.28.7-.28.28 0 .52.09.7.28.18.19.28.42.28.7 0 .28-.09.52-.28.7a1 1 0 01-.7.3c-.28 0-.52-.11-.7-.3zM8 7.99c-.02-.25-.11-.48-.31-.69-.2-.19-.42-.3-.69-.31H6c-.27.02-.48.13-.69.31-.2.2-.3.44-.31.69h1v3c.02.27.11.5.31.69.2.2.42.31.69.31h1c.27 0 .48-.11.69-.31.2-.19.3-.42.31-.69H8V7.98v.01zM7 2.3c-3.14 0-5.7 2.54-5.7 5.68 0 3.14 2.56 5.7 5.7 5.7s5.7-2.55 5.7-5.7c0-3.15-2.56-5.69-5.7-5.69v.01zM7 .98c3.86 0 7 3.14 7 7s-3.14 7-7 7-7-3.12-7-7 3.14-7 7-7z"></path></svg>
<strong>Note: </strong>Your algorithm should run in O(n) time complexity and O(1) space complexity.
</div></p>
<h3 id="Example-1:">Example 1:<a class="anchor-link" href="#Example-1:"> </a></h3>
<pre><code>Input: [1,2,3,4,5]
Output: true</code></pre>
<h3 id="Example-2:">Example 2:<a class="anchor-link" href="#Example-2:"> </a></h3>
<pre><code>Input: [5,4,3,2,1]
Output: false</code></pre>
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<h1 id="Approach-1">Approach 1<a class="anchor-link" href="#Approach-1"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">increasingTriplet</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nums</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">])</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="n">flagCount</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">nums</span><span class="p">)):</span>
<span class="k">if</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">increasingTriplet</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">])</span>
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<pre>1</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/firstMissingPositive/approach1_sub.PNG" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(nlogn)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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</div>
</div>Teja KummarikuntlaCount Negative Numbers in a Sorted Matrix2020-05-27T00:00:00-05:002020-05-27T00:00:00-05:00https://tejakummarikuntla.github.io/notes/problem%20solving/leetcode/2020/05/27/Count-Negative-Numbers-in-a-Sorted-Matrix<!--
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<h2 id="Problem-Statement">Problem Statement<a class="anchor-link" href="#Problem-Statement"> </a></h2><p>Given a <code>m * n</code> matrix grid which is sorted in non-increasing order both row-wise and column-wise.</p>
<p>Return the number of negative numbers in grid. <a href="https://leetcode.com/problems/count-negative-numbers-in-a-sorted-matrix/">[URL]</a></p>
<h3 id="Example-1:">Example 1:<a class="anchor-link" href="#Example-1:"> </a></h3>
<pre><code>Input: grid = [[4,3,2,-1],[3,2,1,-1],[1,1,-1,-2],[-1,-1,-2,-3]]
Output: 8
Explanation: There are 8 negatives number in the matrix.</code></pre>
<h3 id="Example-2:">Example 2:<a class="anchor-link" href="#Example-2:"> </a></h3>
<pre><code>Input: grid = [[3,2],[1,0]]
Output: 0</code></pre>
<h3 id="Example-3:">Example 3:<a class="anchor-link" href="#Example-3:"> </a></h3>
<pre><code>Input: grid = [[1,-1],[-1,-1]]
Output: 3</code></pre>
<h3 id="Example-4:">Example 4:<a class="anchor-link" href="#Example-4:"> </a></h3>
<pre><code>Input: grid = [[-1]]
Output: 1</code></pre>
<h3 id="Constraints:">Constraints:<a class="anchor-link" href="#Constraints:"> </a></h3>
<pre><code>1. m == grid.length
2. n == grid[i].length
3. 1 <= m, n <= 100
4. -100 <= grid[i][j] <= 100</code></pre>
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<h1 id="Approach-1">Approach 1<a class="anchor-link" href="#Approach-1"> </a></h1><p>Brute force</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">countNegatives</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grid</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">grid</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">res</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">res</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">countNegatives</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">],[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">]])</span>
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<pre>8</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/countNegatives/approach1_sub.PNG" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(m*n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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<h1 id="Approach-2">Approach 2<a class="anchor-link" href="#Approach-2"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">countNegatives</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grid</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="n">posCount</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">noOfElements</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">grid</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">posCount</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">noOfElements</span> <span class="o">-</span> <span class="n">posCount</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">countNegatives</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">],[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">]])</span>
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<pre>8</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/countNegatives/approach2_sub.PNG" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(m*n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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<h1 id="Approach-3">Approach 3<a class="anchor-link" href="#Approach-3"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">countNegatives</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grid</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="n">i</span> <span class="o">>=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="k">if</span> <span class="n">grid</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">count</span> <span class="o">+=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="n">j</span><span class="p">)</span>
<span class="n">i</span> <span class="o">-=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">count</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">countNegatives</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">],[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">]])</span>
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<pre>8</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/countNegatives/approach3_sub.PNG" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(m+n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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</div>Teja KummarikuntlaSet Matrix Zeroes2020-05-26T00:00:00-05:002020-05-26T00:00:00-05:00https://tejakummarikuntla.github.io/notes/problem%20solving/leetcode/2020/05/26/set-zeroes<!--
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<h2 id="Problem-Statement">Problem Statement<a class="anchor-link" href="#Problem-Statement"> </a></h2><p>Given a m x n matrix, if an element is 0, set its entire row and column to 0. Do it in-place.</p>
<h3 id="Example-1:">Example 1:<a class="anchor-link" href="#Example-1:"> </a></h3>
<pre><code>Input:
[
[1,1,1],
[1,0,1],
[1,1,1]
]
Output:
[
[1,0,1],
[0,0,0],
[1,0,1]
]</code></pre>
<h3 id="Example-2:">Example 2:<a class="anchor-link" href="#Example-2:"> </a></h3>
<pre><code>Input:
[
[0,1,2,0],
[3,4,5,2],
[1,3,1,5]
]
Output:
[
[0,0,0,0],
[0,4,5,0],
[0,3,1,0]
]</code></pre>
<h3 id="Follow-up:">Follow up:<a class="anchor-link" href="#Follow-up:"> </a></h3><ul>
<li>A straight forward solution using O(mn) space is probably a bad idea.</li>
<li>A simple improvement uses O(m + n) space, but still not the best solution.</li>
<li>Could you devise a constant space solution?
<a href="https://leetcode.com/problems/set-matrix-zeroes/">[URL]</a></li>
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<h1 id="Approach-1">Approach 1<a class="anchor-link" href="#Approach-1"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">setZeroes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">matrix</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Do not return anything, modify matrix in-place instead.</span>
<span class="sd"> """</span>
<span class="n">temp</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">matrixCopy</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">matrix</span><span class="p">:</span>
<span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">row</span><span class="p">:</span>
<span class="n">temp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">element</span><span class="p">)</span>
<span class="n">matrixCopy</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span>
<span class="n">temp</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">matrixCopy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">return</span> <span class="n">matrix</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">setZeroes</span><span class="p">([</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="p">])</span>
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<pre>[[1, 0, 1], [0, 0, 0], [1, 0, 1]]</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/setZeroes/Approach1_sub.png" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(m*n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(m*n)$</p>
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<p><strong>Is Inplace?</strong> : <code>False</code></p>
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<h1 id="Approach-2">Approach 2<a class="anchor-link" href="#Approach-2"> </a></h1>
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<ol>
<li>Traverse the original matrix and look for 0 entities</li>
<li>if found, record the i, j values using auxilary variables</li>
<li>Using sets for recording i, j vlues would be benifiting as we overcome duplicate row, column values ahead.</li>
<li>Finally, re iterate over the original matrix, for every cell make a check <code>if i in rows or j in columns</code>, update the values to 0.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">setZeroes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">matrix</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Do not return anything, modify matrix in-place instead.</span>
<span class="sd"> """</span>
<span class="n">rows</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="n">columns</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">rows</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">columns</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">rows</span> <span class="ow">or</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">return</span> <span class="n">matrix</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">setZeroes</span><span class="p">([</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="p">])</span>
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<pre>[[1, 0, 1], [0, 0, 0], [1, 0, 1]]</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/setZeroes/Approach2_sub.png" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(m*n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(m+n)$</p>
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<p><strong>Is Inplace?</strong> : <code>False</code></p>
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<h1 id="Approach-3">Approach 3<a class="anchor-link" href="#Approach-3"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#collapse-hide</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">setZeroes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">matrix</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Do not return anything, modify matrix in-place instead.</span>
<span class="sd"> """</span>
<span class="n">rowFlag</span><span class="p">,</span> <span class="n">colFlag</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">False</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">rowFlag</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="n">j</span> <span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">colFlag</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="k">if</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">if</span> <span class="n">rowFlag</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
<span class="n">matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">if</span> <span class="n">colFlag</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">matrix</span><span class="p">)):</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">return</span> <span class="n">matrix</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span> <span class="o">=</span> <span class="n">Solution</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sol</span><span class="o">.</span><span class="n">setZeroes</span><span class="p">([</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="p">])</span>
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<pre>[[1, 0, 1], [0, 0, 0], [1, 0, 1]]</pre>
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<p><img src="/notes/images/copied_from_nb/Images/Problem_solving/setZeroes/Approach3_sub.png" alt="" /></p>
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<p><strong>Worst case performance in Time: $O(m*n)$</strong></p>
<p><strong>Worst case performance in Space:</strong> $O(1)$</p>
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<p><strong>Is Inplace?</strong> : <code>True</code></p>
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</div>Teja KummarikuntlaGame of Life2020-05-20T00:00:00-05:002020-05-20T00:00:00-05:00https://tejakummarikuntla.github.io/notes/problem%20solving/leetcode/2020/05/20/Game-of-Life<!--
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<h1 id="Problem-Statement">Problem Statement<a class="anchor-link" href="#Problem-Statement"> </a></h1><p>According to the <a href="https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life">Wikipedia's article</a>: "The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970."</p>
<p>Given a board with m by n cells, each cell has an initial state live (1) or dead (0). Each cell interacts with its eight neighbors (horizontal, vertical, diagonal) using the following four rules (taken from the above Wikipedia article):</p>
<ol>
<li>Any live cell with fewer than two live neighbors dies, as if caused by under-population.</li>
<li>Any live cell with two or three live neighbors lives on to the next generation.</li>
<li>Any live cell with more than three live neighbors dies, as if by over-population..</li>
<li>Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.</li>
<li>Write a function to compute the next state (after one update) of the board given its current state. The next state is created by applying the above rules simultaneously to every cell in the current state, where births and deaths occur simultaneously.
<a href="https://leetcode.com/problems/game-of-life/">URL</a></li>
</ol>
<h2 id="Example:">Example:<a class="anchor-link" href="#Example:"> </a></h2>
<pre><code>Input:
[
[0,1,0],
[0,0,1],
[1,1,1],
[0,0,0]
]
Output:
[
[0,0,0],
[1,0,1],
[0,1,1],
[0,1,0]
]</code></pre>
<h2 id="Follow-up:">Follow up:<a class="anchor-link" href="#Follow-up:"> </a></h2><ol>
<li>Could you solve it in-place? Remember that the board needs to be updated at the same time: You cannot update some cells first and then use their updated values to update other cells.</li>
<li>In this question, we represent the board using a 2D array. In principle, the board is infinite, which would cause problems when the active area encroaches the border of the array. How would you address these problems?</li>
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<h1 id="Approach-1">Approach 1<a class="anchor-link" href="#Approach-1"> </a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">Solution</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">gameOfLife</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">board</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Do not return anything, modify board in-place instead.</span>
<span class="sd"> """</span>
<span class="n">rows</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">board</span><span class="p">)</span>
<span class="n">cols</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">board</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">board_copy</span> <span class="o">=</span> <span class="p">[[</span><span class="n">board</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cols</span><span class="p">)]</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="p">)]</span>
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</div>Teja Kummarikuntla