solveig_master/plot.ipynb

Angle

In [56]:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'category': [
        'white-collar',
        'white-collar',
        'white-collar',
        'blue-collar',
        'blue-collar',
        'blue-collar',
        'casual',
        'casual',
        'casual',
    ],
    'angle': [
        'high angle',
        'eye-level',
        'low angle',
        'high angle',
        'eye-level',
        'low angle',
        'high angle',
        'eye-level',
        'low angle',
    ],
    'count': [
        2,
        13,
        7,
        5,
        8,
        5,
        6,
        3,
        1,
    ],
    'pct': [
            2/22*100,
            13/22*100,
            7/22*100,
            5/18*100,
            8/18*100,
            5/18*100,
            6/10*100,
            3/10*100,
            1/10*100,
        ]
})

df

Out [56]:

	category	angle	count	pct
0	white-collar	high angle	2	9.090909
1	white-collar	eye-level	13	59.090909
2	white-collar	low angle	7	31.818182
3	blue-collar	high angle	5	27.777778
4	blue-collar	eye-level	8	44.444444
5	blue-collar	low angle	5	27.777778
6	casual	high angle	6	60.000000
7	casual	eye-level	3	30.000000
8	casual	low angle	1	10.000000

In [57]:

import matplotlib.pyplot as plt

g = sns.catplot(
    data=df, kind="bar",
    x="category", y="pct", hue="angle",
    palette="Blues", alpha=.6, height=6
)
g.despine(left=True)
sns.set_style("ticks",{'axes.grid' : True})
g.set_axis_labels("", "percent of total images in category (%)")
g.legend.set_title("")
g.savefig('plots/angle.png')

Contact

In [58]:

import pandas as pd

df = pd.DataFrame({
    'category': [
        'white-collar',
        'white-collar',
        'blue-collar',
        'blue-collar',
        'casual',
        'casual',
    ],
    'contact': [
        'demand structure',
        'offer structure',
        'demand structure',
        'offer structure',
        'demand structure',
        'offer structure',
    ],
    'count': [
        14,
        8,
        0,
        18,
        1,
        9,
    ],
    'pct': [
            14/22*100,
            8/22*100,
            0/18*100,
            18/18*100,
            1/10*100,
            9/10*100,
        ]
})

df

Out [58]:

	category	contact	count	pct
0	white-collar	demand structure	14	63.636364
1	white-collar	offer structure	8	36.363636
2	blue-collar	demand structure	0	0.000000
3	blue-collar	offer structure	18	100.000000
4	casual	demand structure	1	10.000000
5	casual	offer structure	9	90.000000

In [59]:

g = sns.catplot(
    data=df, kind="bar",
    x="category", y="pct", hue="contact",
    palette="Blues", alpha=.6, height=6
)
g.despine(left=True)
sns.set_style("ticks",{'axes.grid' : True})
g.set_axis_labels("", "percent of total images in category (%)")
g.legend.set_title("")
g.savefig('plots/contact.png')

Point-of-view

In [60]:

df = pd.DataFrame({
    'category': [
        'white-collar',
        'white-collar',
        'blue-collar',
        'blue-collar',
        'casual',
        'casual',
    ],
    'poit_of_view': [
        'frontal',
        'oblique',
        'frontal',
        'oblique',
        'frontal',
        'oblique',
    ],
    'count': [
        16,
        2,
        4,
        14,
        3,
        7,
    ],
    'pct': [
            16/22*100,
            2/22*100,
            4/18*100,
            14/18*100,
            3/10*100,
            7/10*100,
        ]
})

df

Out [60]:

	category	poit_of_view	count	pct
0	white-collar	frontal	16	72.727273
1	white-collar	oblique	2	9.090909
2	blue-collar	frontal	4	22.222222
3	blue-collar	oblique	14	77.777778
4	casual	frontal	3	30.000000
5	casual	oblique	7	70.000000

In [61]:

g = sns.catplot(
    data=df, kind="bar",
    x="category", y="pct", hue="poit_of_view",
    palette="Blues", alpha=.6, height=6
)
g.despine(left=True)
sns.set_style("ticks",{'axes.grid' : True})
g.set_axis_labels("", "percent of total images in category (%)")
g.legend.set_title("")
g.savefig('plots/point-of-view.png')

Distance

In [62]:

import pandas as pd

df = pd.DataFrame({
    'category': [
        'white-collar',
        'white-collar',
        'white-collar',
        'blue-collar',
        'blue-collar',
        'blue-collar',
        'casual',
        'casual',
        'casual',
    ],
    'distance': [
        'long shot',
        'medium shot',
        'close-up',
        'long shot',
        'medium shot',
        'close-up',
        'long shot',
        'medium shot',
        'close-up',
    ],
    'count': [
        6,
        16,
        0,
        15,
        3,
        0,
        3,
        5,
        2,
    ],
    'pct': [
            6/22*100,
            16/22*100,
            0/22*100,
            15/18*100,
            3/18*100,
            0/18*100,
            3/10*100,
            5/10*100,
            2/10*100,
        ]
})

df

Out [62]:

	category	distance	count	pct
0	white-collar	long shot	6	27.272727
1	white-collar	medium shot	16	72.727273
2	white-collar	close-up	0	0.000000
3	blue-collar	long shot	15	83.333333
4	blue-collar	medium shot	3	16.666667
5	blue-collar	close-up	0	0.000000
6	casual	long shot	3	30.000000
7	casual	medium shot	5	50.000000
8	casual	close-up	2	20.000000

In [63]:

g = sns.catplot(
    data=df, kind="bar",
    x="category", y="pct", hue="distance",
    palette="Blues", alpha=.6, height=6
)
g.despine(left=True)
sns.set_style("ticks",{'axes.grid' : True})
g.set_axis_labels("", "percent of total images in category (%)")
g.legend.set_title("")
g.savefig('plots/distance.png')

In [ ]: