import matplotlib.pyplot as plt

import numpy as np import osimport csv

lines=csv.reader(open("train.csv"))

train_content = []

head_row =next(lines)

for line in lines:

    train_content.append(line)

attribute_ids = []

for line in train_content:

    attributes = line[1].split()

    for a in attributes:

        attribute_ids.append(a)

lines=csv.reader(open("labels.csv"))

attribute_content = []

head_row =next(lines)

for line in lines:

    attribute_content.append(line)

id2name = {}

for line in attribute_content:

    if line[0] not in id2name:

        id2name.update({line[0]:line[1]})

def count_list(lt):

    d={}

    for i in lt:

        if (i in d.keys()):

            continue

        count = lt.count(i)

        d[i] = count

    return d

attribute_count = count_list(attribute_ids)

sorted_attribute= sorted(attribute_count.items(),key = lambda item :item[1],reverse = True)

for i in range(10):

    print (sorted_attribute[i][0],':   ',id2name[sorted_attribute[i][0]])

    print (sorted_attribute[i][1])

culture_count_dict = {}

tag_count_dict = {}

for i in range(1103):

    idx = str(i)

    if (id2name[idx][0:5] == 'tag::'):

        tag_count_dict.update({id2name[idx][5:]:attribute_count[idx]})

    else:

        culture_count_dict.update({id2name[idx][9:]:attribute_count[idx]})

import seaborn as sns

from wordcloud import WordCloud

culture_cloud = WordCloud(background_color='Black', colormap='Paired', width=1600, height=800, random_state=123).generate_from_frequencies(culture_count_dict)

tag_cloud = WordCloud(background_color='Black', colormap='Paired', width=1600, height=800, random_state=123).generate_from_frequencies(tag_count_dict)

plt.figure(figsize=(24,24))

plt.subplot(211)

plt.imshow(culture_cloud,interpolation='bilinear')

plt.axis('off')

plt.subplot(212)

plt.imshow(tag_cloud, interpolation='bilinear')

plt.axis('off')

plt.tight_layout()

plt.show()