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))
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]:
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]:
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]:
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
In [242]:
parallel_coordinates(radarData, 'landmass', colormap=plt.get_cmap("Set2"))
plt.show()
In [ ]: