Ukusebenza kwisakhono sokusebenzisa amaqela kunye nokubonwa kwedatha kwiPython

Hayi Habr!

Namhlanje siza kusebenza kwisakhono sokusebenzisa izixhobo zokuhlanganisa kunye nokubona idatha kwiPython. Kwezinikiweyo isethi yedatha kwiGithub Makhe sihlalutye iimpawu ezininzi kwaye sakhe iseti yokubonwayo.

Ngokwesithethe, ekuqaleni, makhe sichaze iinjongo:

• Idatha yeqela ngokwesini nangonyaka kwaye ubone i-dynamics iyonke yezinga lokuzalwa kwezini zombini;
• Fumana awona magama adumileyo kuwo onke amaxesha;
• Yahlula lonke ixesha kwidatha ibe ngamacandelo ali-10 kwaye nganye, fumana elona gama lidumileyo kwisini ngasinye. Kwigama ngalinye elifunyenweyo, yiba nomfanekiso ngqondweni wentshukumo yalo lonke ixesha;
• Kunyaka ngamnye, bala ukuba mangaphi amagama agubungela i-50% yabantu kwaye ube nombono (siya kubona iindidi zamagama ngonyaka ngamnye);
• Khetha iminyaka emi-4 kulo lonke ikhefu kwaye ubonise unyaka ngamnye ukusasazwa ngonobumba wokuqala egameni kunye nonobumba wokugqibela egameni;
• Yenza uluhlu lwabantu abaninzi abadumileyo (abongameli, iimvumi, abadlali, abalinganiswa befilimu) kwaye uvavanye impembelelo yabo kumandla wamagama. Yakha umfanekiso.

Amagama amancinci, ikhowudi eninzi!

Kwaye, masihambe.

Masiqokelele idatha ngokwesini nangonyaka kwaye sibonise uguquko olupheleleyo lwezinga lokuzalwa kuzo zombini izini:

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()

Masifumane awona magama adumileyo kwimbali:

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)

Masahlule lonke ixesha kwidatha ibe ngamacandelo ali-10 kwaye kwindawo nganye siya kufumana elona gama lidumileyo kwisini ngasinye. Kwigama ngalinye elifunyenweyo, sibona iintshukumo zalo lonke ixesha:

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()

Kunyaka ngamnye, sibala ukuba mangaphi amagama agubungela i-50% yabantu kwaye sibone le datha:

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()

Masikhethe iminyaka emi-4 kulo lonke ixesha kwaye sibonise unyaka ngamnye ukusasazwa ngonobumba wokuqala egameni kunye nonobumba wokugqibela egameni:

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()

Masenze uluhlu lwabantu abaninzi abadumileyo (abongameli, iimvumi, abadlali, abalinganiswa befilimu) kwaye sivavanye impembelelo yabo kumandla wamagama:

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()

Ngoqeqesho, unokongeza ixesha lobomi bosaziwayo kwimbonakalo ukusuka kumzekelo wokugqibela ukuze uvavanye ngokucacileyo impembelelo yabo kwi-dynamics yamagama.

Ngale nto, zonke iinjongo zethu zafezekiswa kwaye zazalisekiswa. Siye saphuhlisa isakhono sokusebenzisa izixhobo zokuhlanganisa kunye nokubona idatha kwiPython, kwaye siya kuqhubeka nokusebenza ngedatha. Wonke umntu unokwenza izigqibo ngokusekelwe kwidatha esele yenziwe, ebonwayo ngokwabo.

Ulwazi kumntu wonke!

umthombo: www.habr.com