Ka shaqaynta xirfada isticmaalka kooxaynta iyo xog ururinta ee Python

Haye Habr!

Maanta waxaan ka shaqayn doonaa xirfada isticmaalka qalabyada kooxaynta iyo sawirida xogta Python. In la bixiyo xogta ku jirta Github Aan falanqeyno dhowr astaamood oo aan dhisno muuqaal muuqaal ah.

Sida dhaqanku qabo, bilawga, aynu qeexno yoolalka:

• Xog kooxeed lab iyo dhedig iyo sanadka oo sawir dhaqdhaqaaqa guud ee heerka dhalashada labada jinsi;
• Soo hel magacyada ugu caansan wakhti kasta;
• U qaybi wakhtiga oo dhan xogta 10 qaybood oo mid walba, hel magaca ugu caansan jinsi kasta. Magac kasta oo la helo, sawir dhaqdhaqaaqiisa mar kasta;
• Sannad kasta, xisaabi inta magac ee daboolaya 50% dadka oo sawir (waxaan arki doonaa noocyada kala duwan ee magacyada sannad kasta);
• Ka dooro 4 sano inta u dhaxaysa oo dhan oo muuji sannad kasta qaybinta xarafka ugu horreeya ee magaca iyo xarafka dambe ee magaca;
• Samee liis dhowr qof oo caan ah (madaxweyne, heesaa, jilayaal, jilayaasha filimada) oo qiimee saamaynta ay ku leeyihiin dhaqdhaqaaqa magacyada. Dhis muuqaal.

Erayada ka yar, kood badan!

Oo, aan tagno.

Aynu xogta u ururino lab iyo dhedig iyo sanadka oo aynu sawirno dhaqdhaqaaqa guud ee heerka dhalashada labada jinsi:

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()

Aynu helno magacyada ugu caansan taariikhda:

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)

Aynu u qaybinno wakhtiga oo dhan xogta 10 qaybood mid kastana waxaan heli doonaa magaca ugu caansan ee jinsi kasta. Magac kasta oo la helo, waxaan u aragnaa dhaqdhaqaaqiisa mar walba:

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()

Sannad kasta, waxaan xisaabinnaa inta magac ee daboolaya 50% dadka oo aan sawirno xogtan:

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()

Aynu ka dooranno 4 sano inta u dhaxaysa oo dhan oo aan muujino sannad kasta qaybinta xarafka ugu horreeya ee magaca iyo xarafka dambe ee magaca:

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()

Aynu samayno liis dhowr qof oo caan ah (madaxweyne, heesaa, jilayaal, jilayaasha filimada) oo aynu qiimayno saamaynta ay ku leeyihiin dhaqdhaqaaqa magacyada:

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()

Tababarka, waxaad ku dari kartaa wakhtiga nolosha ee caanka ah ee muuqaalka laga bilaabo tusaalaha ugu dambeeya si aad si cad u qiimeyso saameynta ay ku leeyihiin dhaqdhaqaaqa magacyada.

Taasna, dhammaan yoolkeennii waa lagu gaadhay oo la fuliyay. Waxaan horumarinay xirfada adeegsiga aaladaha kooxaynta iyo sawirida xogta Python, waxaanan sii wadi doonaa inaan ku shaqayno xogta. Qof kastaa wuxuu samayn karaa gabagabo ku salaysan xogta diyaarsan ee la arkay lafteeda.

Aqoon qof walba!

Source: www.habr.com