Прорабатываем навык использования группировки и визуализации данных в Python

import numpy as np import pandas as pd import matplotlib.pyplot as plt years = np.arange(1880, 2011, 3) datalist = 'https://raw.githubusercontent.com/wesm/pydata-book/2nd-edition/datasets/babynames/yob{year}.txt' dataframes = [] for year in years: dataset = datalist.format(year=year) dataframe = pd.read_csv(dataset, names=['name', 'sex', 'count']) dataframes.append(dataframe.assign(year=year)) result = pd.concat(dataframes) sex = result.groupby('sex') births_men = sex.get_group('M').groupby('year', as_index=False) births_women = sex.get_group('F').groupby('year', as_index=False) births_men_list = births_men.aggregate(np.sum)['count'].tolist() births_women_list = births_women.aggregate(np.sum)['count'].tolist() fig, ax = plt.subplots() fig.set_size_inches(25,15) index = np.arange(len(years)) stolb1 = ax.bar(index, births_men_list, 0.4, color='c', label='Мужчины') stolb2 = ax.bar(index + 0.4, births_women_list, 0.4, alpha=0.8, color='r', label='Женщины') ax.set_title('Рождаемость по полу и годам') ax.set_xlabel('Года') ax.set_ylabel('Рождаемость') ax.set_xticklabels(years) ax.set_xticks(index + 0.4) ax.legend(loc=9) fig.tight_layout() plt.show()

years = np.arange(1880, 2011) dataframes = [] for year in years: dataset = datalist.format(year=year) dataframe = pd.read_csv(dataset, names=['name', 'sex', 'count']) dataframes.append(dataframe) result = pd.concat(dataframes) names = result.groupby('name', as_index=False).sum().sort_values('count', ascending=False) names.head(10)

years = np.arange(1880, 2011) part_size = int((years[years.size - 1] - years[0]) / 10) + 1 parts = {} def GetPart(year): return int((year - years[0]) / part_size) for year in years: index = GetPart(year) r = years[0] + part_size * index, min(years[years.size - 1], years[0] + part_size * (index + 1)) parts[index] = str(r[0]) + '-' + str(r[1]) dataframe_parts = [] dataframes = [] for year in years: dataset = datalist.format(year=year) dataframe = pd.read_csv(dataset, names=['name', 'sex', 'count']) dataframe_parts.append(dataframe.assign(years=parts[GetPart(year)])) dataframes.append(dataframe.assign(year=year)) result_parts = pd.concat(dataframe_parts) result = pd.concat(dataframes) result_parts_sums = result_parts.groupby(['years', 'sex', 'name'], as_index=False).sum() result_parts_names = result_parts_sums.iloc[result_parts_sums.groupby(['years', 'sex'], as_index=False).apply(lambda x: x['count'].idxmax())] result_sums = result.groupby(['year', 'sex', 'name'], as_index=False).sum() for groupName, groupLabels in result_parts_names.groupby(['name', 'sex']).groups.items(): group = result_sums.groupby(['name', 'sex']).get_group(groupName) fig, ax = plt.subplots(1, 1, figsize=(18,10)) ax.set_xlabel('Года') ax.set_ylabel('Рождаемость') label = group['name'] ax.plot(group['year'], group['count'], label=label.aggregate(np.max), color='b', ls='-') ax.legend(loc=9, fontsize=11) plt.show()

dataframe = pd.DataFrame({'year': [], 'count': []}) years = np.arange(1880, 2011) for year in years: dataset = datalist.format(year=year) csv = pd.read_csv(dataset, names=['name', 'sex', 'count']) names = csv.groupby('name', as_index=False).aggregate(np.sum) names['sum'] = names.sum()['count'] names['percent'] = names['count'] / names['sum'] * 100 names = names.sort_values(['percent'], ascending=False) names['cum_perc'] = names['percent'].cumsum() names_filtered = names[names['cum_perc'] <= 50] dataframe = dataframe.append(pd.DataFrame({'year': [year], 'count': [names_filtered.shape[0]]})) fig, ax1 = plt.subplots(1, 1, figsize=(22,13)) ax1.set_xlabel('Года', fontsize = 12) ax1.set_ylabel('Разнообразие имен', fontsize = 12) ax1.plot(dataframe['year'], dataframe['count'], color='r', ls='-') ax1.legend(loc=9, fontsize=12) plt.show()

from string import ascii_lowercase, ascii_uppercase fig_first, ax_first = plt.subplots(1, 1, figsize=(14,10)) fig_last, ax_last = plt.subplots(1, 1, figsize=(14,10)) index = np.arange(len(ascii_uppercase)) years = [1944, 1978, 1991, 2003] colors = ['r', 'g', 'b', 'y'] n = 0 for year in years: dataset = datalist.format(year=year) csv = pd.read_csv(dataset, names=['name', 'sex', 'count']) names = csv.groupby('name', as_index=False).aggregate(np.sum) count = names.shape[0] dataframe = pd.DataFrame({'letter': [], 'frequency_first': [], 'frequency_last': []}) for letter in ascii_uppercase: countFirst = (names[names.name.str.startswith(letter)].count()['count']) countLast = (names[names.name.str.endswith(letter.lower())].count()['count']) dataframe = dataframe.append(pd.DataFrame({ 'letter': [letter], 'frequency_first': [countFirst / count * 100], 'frequency_last': [countLast / count * 100]})) ax_first.bar(index + 0.3 * n, dataframe['frequency_first'], 0.3, alpha=0.5, color=colors[n], label=year) ax_last.bar(index + bar_width * n, dataframe['frequency_last'], 0.3, alpha=0.5, color=colors[n], label=year) n += 1 ax_first.set_xlabel('Буква алфавита') ax_first.set_ylabel('Частота, %') ax_first.set_title('Первая буква в имени') ax_first.set_xticks(index) ax_first.set_xticklabels(ascii_uppercase) ax_first.legend() ax_last.set_xlabel('Буква алфавита') ax_last.set_ylabel('Частота, %') ax_last.set_title('Последняя буква в имени') ax_last.set_xticks(index) ax_last.set_xticklabels(ascii_uppercase) ax_last.legend() fig_first.tight_layout() fig_last.tight_layout() plt.show()

celebrities = {'Frank': 'M', 'Britney': 'F', 'Madonna': 'F', 'Bob': 'M'} dataframes = [] for year in years: dataset = datalist.format(year=year) dataframe = pd.read_csv(dataset, names=['name', 'sex', 'count']) dataframes.append(dataframe.assign(year=year)) result = pd.concat(dataframes) for celebrity, sex in celebrities.items(): names = result[result.name == celebrity] dataframe = names[names.sex == sex] fig, ax = plt.subplots(1, 1, figsize=(16,8)) ax.set_xlabel('Года', fontsize = 10) ax.set_ylabel('Рождаемость', fontsize = 10) ax.plot(dataframe['year'], dataframe['count'], label=celebrity, color='r', ls='-') ax.legend(loc=9, fontsize=12) plt.show()