<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>YearMonth</th>
      <th>Districts</th>
      <th>Distress call</th>
      <th>Rainfall</th>
      <th>Sex Ratio</th>
      <th>Literacy rate</th>
      <th>Employment percentage</th>
      <th>Poverty rate</th>
      <th>Mobile Phone Availability</th>
      <th>Road Length(National Highway)</th>
      <th>...</th>
      <th>Road Length(Other Roads)</th>
      <th>Road Length(Village Roads)</th>
      <th>Total Commercial Vehicles</th>
      <th>Total Non-Commercial Vehicles</th>
      <th>Two Wheelers(Motercycles/Scooters/Mopeds)</th>
      <th>Four Wheelers(Jeeps/motor cars/LCV/AV)</th>
      <th>Three Wheelers(Auto Rikshaws/Tricycle Auto)</th>
      <th>Heavy Vehicles(Lorries,Tractors,Road Rollers,Stage Carriage)</th>
      <th>Service Vehicles(Ambulances/Fire Fighters/School Buses)</th>
      <th>Public Transport(Motor cabs/Maxi cabs/Omni buses/ordinary Taxi/Mini Bus)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2019-01-01</td>
      <td>Ariyalur</td>
      <td>0.005639</td>
      <td>0.022560</td>
      <td>0.718750</td>
      <td>0.120638</td>
      <td>0.000000</td>
      <td>0.315789</td>
      <td>0.668237</td>
      <td>0.000000</td>
      <td>...</td>
      <td>0.206809</td>
      <td>0.202968</td>
      <td>0.000000</td>
      <td>0.003955</td>
      <td>0.010462</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.024372</td>
      <td>0.000000</td>
      <td>0.000664</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2019-01-01</td>
      <td>Chennai</td>
      <td>0.577068</td>
      <td>0.060302</td>
      <td>0.447917</td>
      <td>0.932357</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.710099</td>
      <td>0.031549</td>
      <td>...</td>
      <td>0.000000</td>
      <td>0.000000</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>2</th>
      <td>2019-01-01</td>
      <td>Coimbatore</td>
      <td>0.136591</td>
      <td>0.021275</td>
      <td>0.562500</td>
      <td>0.665231</td>
      <td>0.417526</td>
      <td>0.052632</td>
      <td>0.963893</td>
      <td>0.138390</td>
      <td>...</td>
      <td>0.776251</td>
      <td>0.561034</td>
      <td>0.295213</td>
      <td>0.441844</td>
      <td>0.454476</td>
      <td>0.374018</td>
      <td>0.225556</td>
      <td>0.631878</td>
      <td>0.600991</td>
      <td>0.262395</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2019-01-01</td>
      <td>Cuddalore</td>
      <td>0.020677</td>
      <td>0.269454</td>
      <td>0.427083</td>
      <td>0.409306</td>
      <td>0.154639</td>
      <td>0.315789</td>
      <td>0.813187</td>
      <td>0.052025</td>
      <td>...</td>
      <td>0.586680</td>
      <td>0.523696</td>
      <td>0.083851</td>
      <td>0.110639</td>
      <td>0.127675</td>
      <td>0.035181</td>
      <td>0.066970</td>
      <td>0.203138</td>
      <td>0.278810</td>
      <td>0.080110</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2019-01-01</td>
      <td>Dharmapuri</td>
      <td>0.013158</td>
      <td>0.017010</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.149485</td>
      <td>0.157895</td>
      <td>0.510727</td>
      <td>0.090773</td>
      <td>...</td>
      <td>0.402335</td>
      <td>0.388224</td>
      <td>0.044401</td>
      <td>0.063507</td>
      <td>0.077685</td>
      <td>0.013080</td>
      <td>0.027873</td>
      <td>0.119629</td>
      <td>0.198513</td>
      <td>0.000000</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 21 columns</p>
</div>

   YearMonth   Districts  Distress call  Rainfall  Sex Ratio  Literacy rate  \
0 2019-01-01    Ariyalur       0.005639  0.022560   0.718750       0.120638   
1 2019-01-01     Chennai       0.577068  0.060302   0.447917       0.932357   
2 2019-01-01  Coimbatore       0.136591  0.021275   0.562500       0.665231   
3 2019-01-01   Cuddalore       0.020677  0.269454   0.427083       0.409306   
4 2019-01-01  Dharmapuri       0.013158  0.017010   0.000000       0.000000   

   Employment percentage  Poverty rate  Mobile Phone Availability  \
0               0.000000      0.315789                   0.668237   
1               1.000000      1.000000                   0.710099   
2               0.417526      0.052632                   0.963893   
3               0.154639      0.315789                   0.813187   
4               0.149485      0.157895                   0.510727   

   Road Length(National Highway)  ...  Road Length(Other Roads)  \
0                       0.000000  ...                  0.206809   
1                       0.031549  ...                  0.000000   
2                       0.138390  ...                  0.776251   
3                       0.052025  ...                  0.586680   
4                       0.090773  ...                  0.402335   

   Road Length(Village Roads)  Total Commercial Vehicles  \
0                    0.202968                   0.000000   
1                    0.000000                   1.000000   
2                    0.561034                   0.295213   
3                    0.523696                   0.083851   
4                    0.388224                   0.044401   

   Total Non-Commercial Vehicles  Two Wheelers(Motercycles/Scooters/Mopeds)  \
0                       0.003955                                   0.010462   
1                       1.000000                                   1.000000   
2                       0.441844                                   0.454476   
3                       0.110639                                   0.127675   
4                       0.063507                                   0.077685   

   Four Wheelers(Jeeps/motor cars/LCV/AV)  \
0                                0.000000   
1                                1.000000   
2                                0.374018   
3                                0.035181   
4                                0.013080   

   Three Wheelers(Auto Rikshaws/Tricycle Auto)  \
0                                     0.000000   
1                                     1.000000   
2                                     0.225556   
3                                     0.066970   
4                                     0.027873   

   Heavy Vehicles(Lorries,Tractors,Road Rollers,Stage Carriage)  \
0                                           0.024372              
1                                           1.000000              
2                                           0.631878              
3                                           0.203138              
4                                           0.119629              

   Service Vehicles(Ambulances/Fire Fighters/School Buses)  \
0                                           0.000000         
1                                           1.000000         
2                                           0.600991         
3                                           0.278810         
4                                           0.198513         

   Public Transport(Motor cabs/Maxi cabs/Omni buses/ordinary Taxi/Mini Bus)  
0                                           0.000664                         
1                                           1.000000                         
2                                           0.262395                         
3                                           0.080110                         
4                                           0.000000                         

[5 rows x 21 columns]

Index(['YearMonth', 'Districts', 'Distress call', 'Rainfall', 'Sex Ratio',
       'Literacy rate', 'Employment percentage', 'Poverty rate',
       'Mobile Phone Availability', 'Road Length(National Highway)',
       'Road Length(State Highway)', 'Road Length(Other Roads)',
       'Road Length(Village Roads)', 'Total Commercial Vehicles',
       'Total Non-Commercial Vehicles',
       'Two Wheelers(Motercycles/Scooters/Mopeds)',
       'Four Wheelers(Jeeps/motor cars/LCV/AV)',
       'Three Wheelers(Auto Rikshaws/Tricycle Auto)',
       'Heavy Vehicles(Lorries,Tractors,Road Rollers,Stage Carriage)',
       'Service Vehicles(Ambulances/Fire Fighters/School Buses)',
       'Public Transport(Motor cabs/Maxi cabs/Omni buses/ordinary Taxi/Mini Bus)'],
      dtype='object')

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Road Length(National Highway)</th>
      <th>Road Length(State Highway)</th>
      <th>Road Length(Other Roads)</th>
      <th>Road Length(Village Roads)</th>
      <th>Total Commercial Vehicles</th>
      <th>Total Non-Commercial Vehicles</th>
      <th>Two Wheelers(Motercycles/Scooters/Mopeds)</th>
      <th>Four Wheelers(Jeeps/motor cars/LCV/AV)</th>
      <th>Three Wheelers(Auto Rikshaws/Tricycle Auto)</th>
      <th>Heavy Vehicles(Lorries,Tractors,Road Rollers,Stage Carriage)</th>
      <th>Service Vehicles(Ambulances/Fire Fighters/School Buses)</th>
      <th>Public Transport(Motor cabs/Maxi cabs/Omni buses/ordinary Taxi/Mini Bus)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Principal Component 1</th>
      <td>0.020568</td>
      <td>0.021291</td>
      <td>-0.063002</td>
      <td>-0.070446</td>
      <td>0.364187</td>
      <td>0.364096</td>
      <td>0.363680</td>
      <td>0.360032</td>
      <td>0.352518</td>
      <td>0.317969</td>
      <td>0.332910</td>
      <td>0.356239</td>
    </tr>
    <tr>
      <th>Principal Component 2</th>
      <td>0.272597</td>
      <td>0.530596</td>
      <td>0.537688</td>
      <td>0.529671</td>
      <td>0.013297</td>
      <td>0.010413</td>
      <td>0.023671</td>
      <td>-0.065714</td>
      <td>-0.080490</td>
      <td>0.199443</td>
      <td>0.140057</td>
      <td>-0.058857</td>
    </tr>
  </tbody>
</table>
</div>

                       Road Length(National Highway)  \
Principal Component 1                       0.020568   
Principal Component 2                       0.272597   

                       Road Length(State Highway)  Road Length(Other Roads)  \
Principal Component 1                    0.021291                 -0.063002   
Principal Component 2                    0.530596                  0.537688   

                       Road Length(Village Roads)  Total Commercial Vehicles  \
Principal Component 1                   -0.070446                   0.364187   
Principal Component 2                    0.529671                   0.013297   

                       Total Non-Commercial Vehicles  \
Principal Component 1                       0.364096   
Principal Component 2                       0.010413   

                       Two Wheelers(Motercycles/Scooters/Mopeds)  \
Principal Component 1                                   0.363680   
Principal Component 2                                   0.023671   

                       Four Wheelers(Jeeps/motor cars/LCV/AV)  \
Principal Component 1                                0.360032   
Principal Component 2                               -0.065714   

                       Three Wheelers(Auto Rikshaws/Tricycle Auto)  \
Principal Component 1                                     0.352518   
Principal Component 2                                    -0.080490   

                       Heavy Vehicles(Lorries,Tractors,Road Rollers,Stage Carriage)  \
Principal Component 1                                           0.317969              
Principal Component 2                                           0.199443              

                       Service Vehicles(Ambulances/Fire Fighters/School Buses)  \
Principal Component 1                                           0.332910         
Principal Component 2                                           0.140057         

                       Public Transport(Motor cabs/Maxi cabs/Omni buses/ordinary Taxi/Mini Bus)  
Principal Component 1                                           0.356239                         
Principal Component 2                                          -0.058857                         

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Sex Ratio</th>
      <th>Literacy rate</th>
      <th>Employment percentage</th>
      <th>Poverty rate</th>
      <th>Mobile Phone Availability</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Principal Component 1</th>
      <td>0.210003</td>
      <td>0.543155</td>
      <td>0.623573</td>
      <td>0.502832</td>
      <td>0.138557</td>
    </tr>
    <tr>
      <th>Principal Component 2</th>
      <td>-0.718349</td>
      <td>-0.405498</td>
      <td>0.330659</td>
      <td>0.216623</td>
      <td>0.404086</td>
    </tr>
  </tbody>
</table>
</div>

                       Sex Ratio  Literacy rate  Employment percentage  \
Principal Component 1   0.210003       0.543155               0.623573   
Principal Component 2  -0.718349      -0.405498               0.330659   

                       Poverty rate  Mobile Phone Availability  
Principal Component 1      0.502832                   0.138557  
Principal Component 2      0.216623                   0.404086  

Vizly: AI-powered data analysis

PCA Analysis of Road Accident Data

What are the top contributing features for each principal component?

Principal Component 1

Principal Component 2

Perform PCA on the following parameters that the distress call data depends on Sex Ratio, Literacy rate ,Employment percentage, Poverty rate, Mobile Phone Availability

Principal Component 1

Principal Component 2