DH-data-process/load_and_plot.py at main · panuscha/DH-data-process · GitHub

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import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter

file_path = 'data/b_only_subset.csv'

df = pd.read_csv(file_path, delimiter=';', dtype='str')

year_tags = {'years': [],
            'figures': [],
            'descriptions': [],
            'genres': []}
figures = {}
descriptions = {}
genres = {}

for index, row in df.iterrows():
    fig = []
    descr = []
    gen = []

    if not pd.isnull(row['figures']) :
        fig  = row['figures'].split('$')[:-1]
        for f in fig:
            if not f in figures.keys():
                figures[f] = 1
            else:
                figures[f] += 1


    if not pd.isnull(row['description']):
        descr  = row['description'].split('$')[:-1]
        for d in descr:
            if not d in descriptions.keys():
                descriptions[d] = 1
            else:
                descriptions[d] += +1

    if not pd.isnull(row['genre']) :
        gen  = row['genre'].split('$')[:-1]
        for g in gen:
            if not g in genres.keys():
                genres[g] = 1
            else:
                genres[g] += 1

    year_tags['years'].append(row['year'])
    year_tags['figures'].append(fig)
    year_tags['descriptions'].append(descr)
    year_tags['genres'].append(gen)


# sorted_genres_desc = dict(sorted(genres.items(), key=lambda x: x[1], reverse=True))

# first_x = 30
# figures_keys = list(sorted_genres_desc.keys())[:first_x]
# frequencies = list(sorted_genres_desc.values())[:first_x]
# frequencies = [ x/len(df.index) for x in frequencies]

# # Plotting the frequencies
# plt.bar(figures_keys, frequencies)
# plt.xlabel('Genre')
# plt.ylabel('Proportion')
# plt.title('Genre Proportion')
# plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility if needed

# sorted_figures_desc = dict(sorted(figures.items(), key=lambda x: x[1], reverse=True))

# first_x = 30
# figures_keys = list(sorted_figures_desc.keys())[:first_x]
# frequencies = list(sorted_figures_desc.values())[:first_x]
# frequencies = [ x/len(df.index) for x in frequencies]

# # Plotting the frequencies
# plt.bar(figures_keys, frequencies)
# plt.xlabel('Figures')
# plt.ylabel('Proportion')
# plt.title('Figures Propotion')
# plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility if needed

# sorted_descriptions_desc = dict(sorted(descriptions.items(), key=lambda x: x[1], reverse=True))

# first_x = 30
# descriptions_keys  = list(sorted_descriptions_desc.keys())[:first_x]
# frequencies = list(sorted_descriptions_desc.values())[:first_x]
# frequencies = [ x/len(df.index) for x in frequencies]

# # Plotting the frequencies
# plt.bar(descriptions_keys, frequencies)
# plt.xlabel('Descriptions')
# plt.ylabel('Proportion')
# plt.title('Descriptions Proportions')
# plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility if needed

# data =  pd.DataFrame.from_dict(year_tags)

# most_common_genre = data.groupby('years')['genres'].apply(lambda x: Counter(str(topic) for topics in x for topic in topics).most_common(1)[0][0] if any(x) > 0 else None)

# plt.bar(most_common_genre.index, most_common_genre.values)
# plt.xlabel('Year')
# plt.ylabel('Most Common Genre')
# plt.title('Most Common Genre per Year')
# plt.xticks(list(most_common_genre.index))
# plt.xticks(rotation=45)


# data =  pd.DataFrame.from_dict(year_tags)

# # Filter out rows where 'descriptions' is empty
# data_filtered = data[data['descriptions'].apply(lambda x: len(x) > 0)]

# most_common_description = data_filtered.groupby('years')['descriptions'].apply(lambda x: Counter(topic for topics in x for topic in topics).most_common(1)[0][0] if len(x) > 0 else None)

# plt.bar(most_common_description.index, most_common_description.values)
# plt.xlabel('Year')
# plt.ylabel('Most Common Description')
# plt.title('Most Common Description per Year')
# plt.xticks(list(most_common_description.index))
# plt.xticks(rotation=45)


data =  pd.DataFrame.from_dict(year_tags)

# Filter out rows where 'figures' is empty
data_filtered = data[data['figures'].apply(lambda x: len(x) > 0)]

most_common_figure = data_filtered.groupby('years')['figures'].apply(lambda x: Counter(str(topic) for topics in x for topic in topics).most_common(1)[0][0] if len(x) > 0 else None)

plt.bar(most_common_figure.index, most_common_figure.values)
plt.xlabel('Year')
plt.ylabel('Most Common Figure')
plt.title('Most Common Figure per Year')
plt.xticks(list(most_common_figure.index))
plt.xticks(rotation=45)


plt.show()