In [244]:

%matplotlib inline

import pandas as pd
import numpy as np
import scipy
import sklearn
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D

In [4]:

names = ['name','landmass','zone','area','population','language','religion','bars','stripes','colours','red','green','blue','gold','white','black','orange','mainhue','circles','crosses','saltires','quarters','sunstars','crescent','triangle','icon','animate','text','topleft','botright']
dataset = pd.read_csv('flag.data',names=names,usecols=[*range(len(names))])

print(dataset.head(5))

             name  landmass  zone  area  population  language  religion  bars  \
0     Afghanistan         5     1   648          16        10         2     0   
1         Albania         3     1    29           3         6         6     0   
2         Algeria         4     1  2388          20         8         2     2   
3  American-Samoa         6     3     0           0         1         1     0   
4         Andorra         3     1     0           0         6         0     3   

   stripes  colours  ...  saltires  quarters  sunstars  crescent  triangle  \
0        3        5  ...         0         0         1         0         0   
1        0        3  ...         0         0         1         0         0   
2        0        3  ...         0         0         1         1         0   
3        0        5  ...         0         0         0         0         1   
4        0        3  ...         0         0         0         0         0   

   icon  animate text  topleft  botright  
0     1        0    0    black     green  
1     0        1    0      red       red  
2     0        0    0    green     white  
3     1        1    0     blue       red  
4     0        0    0     blue       red  

[5 rows x 30 columns]

In [22]:

#all possible religious tags
religions = ['catholic','other christian','muslim','buddhist',
             'hindu','ethnic','marxist','other']

#sum of flags tagged by respective religion
totalFlags = []

for religion in religions:
    index = religions.index(religion)
    religionCount = sum(dataset['religion'] == index)
    totalFlags.append(religionCount)
    
#reshape into dataframe
totalFlags = pd.DataFrame(np.array(totalFlags).reshape(1,len(religions)))
totalFlags.columns = religions

In [81]:

sns.set(style="whitegrid")
ax = sns.barplot(data=totalFlags)
ax.set(xlabel='religions', ylabel='amount')
sns.set(rc={'figure.figsize':(15,10)})
sns.set_palette(sns.hls_palette(8, l=.3, s=.8))

In [55]:

#we only care about these columns
columns = ['mainhue','bars','stripes','sunstars']
plotData = dataset[columns]
plotData.columns = columns

plotData

In [62]:

#represent the flag colors on plot
flagColors = ['green','red','blue','gold','white','orange','black','brown']
sns.set_palette(flagColors)

sns.relplot(x="stripes", y="sunstars", hue="mainhue", size="bars",sizes=(100, 500), 
            alpha=0.9,height=6, data=plotData, palette=flagColors)

Out[62]:

<seaborn.axisgrid.FacetGrid at 0x7feedb6a2908>

In [77]:

landMasses = ['N. America','S. America','Europe','Africa','Asia','Oceania']
columns = ['landmass','bars','stripes','circles','crosses','saltires','quarters','sunstars',
          'crescent','triangle','icon','animate','text']
flagFeatsByLandmass = dataset[columns]

for i in range(1 , len(landMasses) + 1):
    landMassLabel = landMasses[i - 1]
    flagFeatsByLandmass.loc[flagFeatsByLandmass['landmass'] == i,'landmass'] = landMassLabel
    
flagFeatsByLandmass = pd.melt(flagFeatsByLandmass,id_vars="landmass",
                              var_name="feats",value_name="amount")
flagFeatsByLandmass

Out[77]:

	landmass	feats	amount
0	Asia	bars	0
1	Europe	bars	0
2	Africa	bars	2
3	Oceania	bars	0
4	Europe	bars	3
5	Africa	bars	0
6	N. America	bars	0
7	N. America	bars	0
8	S. America	bars	0
9	S. America	bars	0
10	Oceania	bars	0
11	Europe	bars	0
12	N. America	bars	0
13	Asia	bars	0
14	Asia	bars	0
15	N. America	bars	3
16	Europe	bars	3
17	N. America	bars	0
18	Africa	bars	0
19	N. America	bars	0
20	Asia	bars	0
21	S. America	bars	0
22	Africa	bars	0
23	S. America	bars	0
24	N. America	bars	0
25	Asia	bars	0
26	Europe	bars	0
27	Africa	bars	0
28	Asia	bars	0
29	Africa	bars	0
...	...	...	...
2298	Africa	text	0
2299	Europe	text	0
2300	Europe	text	0
2301	Asia	text	0
2302	Asia	text	0
2303	Africa	text	0
2304	Asia	text	0
2305	Africa	text	0
2306	Oceania	text	0
2307	S. America	text	0
2308	Africa	text	0
2309	Asia	text	0
2310	N. America	text	0
2311	Oceania	text	0
2312	Asia	text	0
2313	Africa	text	0
2314	Europe	text	0
2315	S. America	text	0
2316	N. America	text	1
2317	N. America	text	0
2318	Asia	text	0
2319	Oceania	text	0
2320	Europe	text	0
2321	S. America	text	0
2322	Asia	text	0
2323	Oceania	text	0
2324	Europe	text	0
2325	Africa	text	0
2326	Africa	text	0
2327	Africa	text	0

2328 rows × 3 columns

In [78]:

sns.set_palette(sns.hls_palette(8, l=.3, s=.8))
g = sns.factorplot(x='amount', y='feats', hue='landmass', data=flagFeatsByLandmass, kind='bar',
                  size=10,aspect=2,ci=None)

In [166]:

landFeatsColor = dataset.drop(columns=['name','zone','area','population','language','religion',
                                     'red','green','blue','gold','white','black','orange',
                                      'topleft','botright','colours','bars','stripes',
                                      'crosses','saltires','circles','quarters','sunstars',
                                      'crescent','triangle','icon','animate','text'])

for i in range(1,7):
    landFeatsColor.loc[landFeatsColor['landmass'] == i,'landmass'] = landMasses[i - 1]


colors = ['red','green','blue','gold','white','black','orange']

mainColorsByLandmass = []

for land in landMasses:
    landMainhue = []
    landmass = landFeatsColor[landFeatsColor['landmass'] == land]

    for color in colors:
        landMainhue.append(sum(landmass['mainhue'] == color))
    
    mainColorsByLandmass.append(landMainhue)


mainColorsByLandmass = pd.DataFrame(np.array(mainColorsByLandmass).reshape(6,len(colors)))
mainColorsByLandmass.columns = colors
mainColorsByLandmass['landmass'] = landMasses
mainColorsByLandmass = pd.melt(mainColorsByLandmass,id_vars="landmass",
                               var_name="mainhue",value_name="amount")

h = sns.factorplot(x='landmass', y='amount', hue='mainhue', data=mainColorsByLandmass, 
                   kind='bar',size=10,aspect=2,ci=None)

In [137]:

land1 = dataset[dataset['landmass'] == 1]

#bars, stripes, colorDiff, crosses, saltires, quarters, sunstars, icon
columns = ['red','green','blue','gold','white','black','orange']
flagColors = []

for color in columns:
    flagColors.append(sum(land1[color]))

flagColors = pd.DataFrame(np.array(flagColors).reshape(1,len(columns)))
flagColors.columns = columns

flatui = ["#e50000", "#15b01a", "#0343df", "#dbb40c", "#ffffff", "#000000", "#f97306"]
sns.set_palette(flatui)

ax = sns.barplot(data=flagColors)
ax.set(xlabel='colors', ylabel='total')
sns.set(rc={'figure.figsize':(13,3)})

In [202]:

landMasses = ['N. America','S. America','Europe','Africa','Asia','Oceania']
labels = ['circles','crosses','saltires','quarters','crescent',
          'triangle','icon','animate','text']

radarData = []

for i in range(1 , len(landMasses) + 1):
    land = dataset[dataset['landmass'] == i]

    flagFeatures = []

    for feat in labels:
        flagFeatures.append(np.mean(land[feat]))
    
    radarData.append(flagFeatures)

radarData = pd.DataFrame(np.array(radarData).reshape(len(landMasses), len(labels)))
radarData = radarData.T
radarData.columns = landMasses

radarData

Out[202]:

	N. America	S. America	Europe	Africa	Asia	Oceania
0	0.193548	0.176471	0.028571	0.115385	0.358974	0.15
1	0.225806	0.058824	0.285714	0.038462	0.025641	0.40
2	0.193548	0.058824	0.028571	0.057692	0.025641	0.30
3	0.193548	0.411765	0.028571	0.057692	0.102564	0.40
4	0.000000	0.000000	0.000000	0.076923	0.179487	0.00
5	0.258065	0.117647	0.028571	0.134615	0.102564	0.25
6	0.290323	0.176471	0.257143	0.173077	0.333333	0.30
7	0.419355	0.176471	0.114286	0.134615	0.153846	0.30
8	0.161290	0.176471	0.028571	0.038462	0.102564	0.05

In [239]:

def radarPlot(row,color,land,labels):
    angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)
    angles = np.concatenate((angles,[angles[0]]))

    ax = plt.subplot(2,3,row+1, polar=True)
    
    ax.set_theta_offset(np.pi / 2)
    
    plt.xticks(angles[:-1], labels, color='grey', size=8)

    # Draw ylabels
    ax.set_rlabel_position(0)
    plt.yticks([0.10,0.20,0.30,0.40], 
               ["0.10","0.20","0.30","0.40"], color="grey", size=7)

    plt.ylim(0,0.50)

    stats = radarData[land].values
    stats = np.concatenate((stats,[stats[0]]))

    ax.plot(angles, stats, color=color,linewidth=2,linestyle='solid',label=land)
    ax.fill(angles, stats, color=color, alpha=0.4)

    title = land
    plt.title(title, size=11, color=color, y=1.1)

In [240]:

plotDpi=96
plt.figure(figsize=(1600/plotDpi, 1000/plotDpi), dpi=plotDpi)

colors = ['royalblue','navy','goldenrod','olivedrab','darkred','limegreen']

i = 0
for land in landMasses:
    color = colors[i]
    radarPlot(i,color,land,labels)
    i+=1