如何在图中表示布尔数据

Question

How can I represent below data in comprehensive graph? Tried to with group by() from Pandas but the result in not comprehensive. My objectif is to show what causes the most accidents between below combinations

pieton  bicyclette  camion_lourd  vehicule
0       0           1             1 
0       1           0             1
1       1           0             0
0       1           1             0
0       1           0             1
1       0           0             1
0       0           0             1
0       0           0             1
1       1           0             0
0       1           0             1

y = df.groupby(['pieton', 'bicyclette', 'camion_lourd', 'vehicule']).size()
y.unstack()

result:

Answer 1

Here are some visualizations that may help you:

#data analysis and wrangling
import pandas as pd
import numpy as np

# visualization
import matplotlib.pyplot as plt

columns = ['pieton', 'bicyclette', 'camion_lourd', 'vehicule']
df = pd.DataFrame([[0,0,1,1],[0,1,0,1],
                  [1,1,0,0],[0,1,1,0],
                  [1,0,0,1],[0,0,0,1],
                  [0,0,0,1],[1,1,0,0],
                  [0,1,0,1]], columns = columns)

You can start by seeing the proportion of accident per category:

# Set up a grid of plots
fig = plt.figure(figsize=(10,10)) 
fig_dims = (3, 2)


# Plot accidents depending on type
plt.subplot2grid(fig_dims, (0, 0))
df['pieton'].value_counts().plot(kind='bar', 
                                     title='Pieton')
plt.subplot2grid(fig_dims, (0, 1))
df['bicyclette'].value_counts().plot(kind='bar', 
                                     title='bicyclette')
plt.subplot2grid(fig_dims, (1, 0))
df['camion_lourd'].value_counts().plot(kind='bar', 
                                     title='camion_lourd')
plt.subplot2grid(fig_dims, (1, 1))
df['vehicule'].value_counts().plot(kind='bar', 
                                     title='vehicule')

Which gives:

Or if you prefer:

df.apply(pd.value_counts).plot(kind='bar', 
                                     title='all types')

But, more interestingly, I would do a comparison per pair. For example, for pedestrians:

pieton = {}
for col in columns:
    pieton[col] = np.sum(df.pieton[df[col] == 1])
pieton.pop('pieton', None)
plt.bar(range(len(pieton)), pieton.values(), align='center')
plt.xticks(range(len(pieton)), pieton.keys())
plt.title("Who got an accident with a pedestrian?")
plt.legend(loc='best')
plt.show()

Which gives:

The similar plot can be done for bicycles, trucks and cars, giving:

It would be interesting to have more data points, to be able to draw better conclusions. However, this still tells us to watch out for bicycles if you are driving!

Hope this helped!