In [1]:

    

import pandas as pd


    

In [13]:

    

df = pd.read_csv('District and vehicles.tsv', sep='\t')


    

In [14]:

    

df.shape


    

    
Out[14]:





(138, 3)

In [7]:

    

df = df[df['Vehicles'] != 'null']


    

In [8]:

    

df = df[df['District'] != 'null']


    

In [11]:

    

df2 = df


    

In [12]:

    

df2.shape


    

    
Out[12]:





(71, 3)

In [16]:

    

df2.columns


    

    
Out[16]:





Index(['District', 'Vehicles', 'count_sum'], dtype='object')

In [18]:

    

df2['District']


    

    
Out[18]:





4               khulna
5               magura
6              gazipur
12           madaripur
22             comilla
23               bogra
30          chittagong
31        khagrachhari
33             barisal
34              sylhet
35               dhaka
36             tangail
37            rajshahi
44          chittagong
45        brahmanbaria
46           netrakona
50               dhaka
54              khulna
60               bogra
61            rajshahi
62          patuakhali
67               dhaka
68             rangpur
69               dhaka
70         narayanganj
82            rajshahi
85           gopalganj
87         narayanganj
88     chapainawabganj
89           madaripur
            ...       
103          dinajpuri
104          manikganj
105         nilphamari
106              dhaka
108            barisal
110      dhaka,barisal
111         shariatpur
112           dinajpur
114         chittagong
115        moulvibazar
116          chuadanga
117            jessore
118            gazipur
119          manikganj
120              dhaka
121           faridpur
123             khulna
124            tangail
125            jessore
126          rangamati
127          joypurhat
128          sunamganj
129              dhaka
130           dinajpur
132              bogra
133           chandpur
134           rajshahi
135            comilla
136           habiganj
137           bagerhat
Name: District, dtype: object

In [25]:

    

df_vehicles = pd.read_csv('vehiclecollision.tsv', sep='\t')


    

In [26]:

    

df_vehicles = df_vehicles[df_vehicles['Vehicle'] != 'null']


    

In [28]:

    

df_vehicles = df_vehicles[df_vehicles['Location'] != 'null']


    

In [29]:

    

import numpy as np
df_vehicles = df_vehicles[df_vehicles['Location'] != np.NaN]


    

In [32]:

    

df_vehicles = df_vehicles[df_vehicles['Vehicle'] != 'NaN']


    

In [33]:

    

df_vehicles = df_vehicles[df_vehicles['Location'] != 'NaN']


    

In [34]:

    

df_vehicles


    

    
Out[34]:






  

    

      

      
Vehicle
      
Location
      
count
    
  
  

    

      
0
      
ambulance
      
dhaka
      
1.0
    
    

      
1
      
bus
      
barisal
      
6.0
    
    

      
2
      
bus
      
bogra
      
2.0
    
    

      
3
      
bus
      
brahmanbaria
      
2.0
    
    

      
4
      
bus
      
chandpur
      
1.0
    
    

      
5
      
bus
      
chittagong
      
7.0
    
    

      
6
      
bus
      
comilla
      
3.0
    
    

      
7
      
bus
      
dhaka
      
13.0
    
    

      
9
      
bus
      
faridpur
      
1.0
    
    

      
10
      
bus
      
gazipur
      
1.0
    
    

      
11
      
bus
      
gopalganj
      
1.0
    
    

      
12
      
bus
      
jessore
      
1.0
    
    

      
13
      
bus
      
joypurhat
      
1.0
    
    

      
14
      
bus
      
khulna
      
3.0
    
    

      
15
      
bus
      
madaripur
      
1.0
    
    

      
16
      
bus
      
magura
      
1.0
    
    

      
17
      
bus
      
manikganj
      
2.0
    
    

      
18
      
bus
      
moulvibazar
      
1.0
    
    

      
19
      
bus
      
mymensingh
      
1.0
    
    

      
20
      
bus
      
narayanganj
      
1.0
    
    

      
21
      
bus
      
netrakona
      
2.0
    
    

      
22
      
bus
      
patuakhali
      
1.0
    
    

      
23
      
bus
      
rajshahi
      
6.0
    
    

      
24
      
bus
      
rangamati
      
1.0
    
    

      
25
      
bus
      
rangpur
      
2.0
    
    

      
26
      
bus
      
shariatpur
      
2.0
    
    

      
27
      
bus
      
sunamganj
      
1.0
    
    

      
28
      
bus
      
tangail
      
1.0
    
    

      
29
      
bus,bus
      
chittagong
      
1.0
    
    

      
30
      
bus,bus
      
comilla
      
1.0
    
    

      
...
      
...
      
...
      
...
    
    

      
105
      
microbus
      
chuadanga
      
1.0
    
    

      
106
      
microbus
      
dhaka
      
3.0
    
    

      
107
      
microbus
      
rajshahi
      
1.0
    
    

      
109
      
truck
      
NaN
      
NaN
    
    

      
110
      
truck
      
chittagong
      
2.0
    
    

      
111
      
truck
      
dhaka
      
4.0
    
    

      
112
      
truck
      
dhaka,barisal
      
1.0
    
    

      
113
      
truck
      
dinajpur
      
1.0
    
    

      
114
      
truck
      
gazipur
      
1.0
    
    

      
115
      
truck
      
jessore
      
1.0
    
    

      
116
      
truck
      
khagrachhari
      
2.0
    
    

      
117
      
truck
      
madaripur
      
1.0
    
    

      
118
      
truck
      
moulvibazar
      
1.0
    
    

      
119
      
truck
      
rajshahi
      
1.0
    
    

      
120
      
truck,bus
      
bogra
      
1.0
    
    

      
121
      
jeep,bus
      
NaN
      
NaN
    
    

      
122
      
jeep,bus
      
dinajpuri
      
1.0
    
    

      
123
      
jeep,bus
      
manikganj
      
1.0
    
    

      
124
      
jeep,bus
      
nilphamari
      
1.0
    
    

      
125
      
jeep,bus
      
sirajganj
      
1.0
    
    

      
126
      
rickshaw,bus
      
NaN
      
NaN
    
    

      
127
      
rickshaw,bus
      
dhaka
      
1.0
    
    

      
128
      
rickshaw,bus
      
rajshahi
      
2.0
    
    

      
129
      
rickshaw,bus
      
sylhet
      
1.0
    
    

      
130
      
rickshaw,bus
      
tangail
      
2.0
    
    

      
131
      
tempo,truck
      
NaN
      
NaN
    
    

      
132
      
tempo,truck
      
dhaka
      
1.0
    
    

      
133
      
tempo,truck
      
tempo
      
NaN
    
    

      
134
      
tempo,truck
      
khulna
      
1.0
    
    

      
135
      
caravan,bus
      
tangail
      
1.0
    
  

132 rows × 3 columns

In [ ]: