【kaggle】Celebrity Death

来源：互联网发布：宁波淘宝美工培训编辑：程序博客网时间：2024/06/09 17:08

数据集：celebrity_deaths_2016.csv
https://www.kaggle.com/hugodarwood/celebrity-deaths

读取数据集：

# -*-coding:utf-8-*-import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom matplotlib.gridspec import GridSpec##################### Does the number of celebrities death is highest in 2016 ?# Is there something interesting in the number of deaths by month?# Does most of the celebrity die during their young age or old age ?# What would be the main causes of death?# What would be the main causes of death for each age category?####################death = pd.read_csv("../DataSet/celebrity_deaths_2016.csv")print death.head()# Q1:Does the number of celebrities death is highest in 2016 ?death_by_year = death.groupby('death_year')['name'].count()plt.figure()death_by_year.plot(kind='bar')  # 柱状图plt.title('Number of deaths every year')plt.show()########################################### Q2:Is there something interesting in the number of deaths by month?death_by_month = death.groupby('death_month')['name'].count().sort_values()plt.figure()death_by_month.plot(kind='line')  # 折线图plt.title('Number of deaths every month')plt.show()########################################### Q3:Does most of the celebrity die during their young age or old age ?fig = plt.figure()ax = fig.add_subplot(111)ax.boxplot(death['age'])  # 箱线图plt.show()########################################### Q4:What would be the main causes of death?def group_deathcause(cause):    mod_cause = ''    cause = str(cause)    if 'cancer' in cause:        mod_cause = 'cancer'    elif 'heart' in cause or 'cardiac' in cause:        mod_cause = 'heart disease'    else:        mod_cause = cause    return mod_causedeath['cause_of_death'].fillna('', inplace=True)death['cause_of_death'] = death.apply(lambda row: group_deathcause(row['cause_of_death']), axis=1)death_cause = death.groupby('cause_of_death')['name'].count().sort_values(ascending=False)comp = death_cause.ix[1:20]y = death_cause.ix[21:1].sum()comp['others'] = yplt.figure()plt.pie(comp, labels=comp.index, autopct='%1.1f%%', startangle=310)  # 饼图plt.tight_layout()plt.axis('equal')plt.title('composition of known cause of death', y=1.08, fontweight='bold')plt.show()# -------------------death['cause_of_death'].fillna('unknown', inplace=True)death_cause = death.groupby('cause_of_death')['name'].count().sort_values(ascending=False)print death_cause.head(20)########################################### Q5:What would be the main causes of death for each age category?def age_categorizer(age):    category = ""    if (age < 18):        category = "child"    elif (age < 30):        category = "young"    elif (age < 60):        category = "adult"    else:        category = "old"    return categorydeath["age_category"] = death.apply(lambda row: age_categorizer(row["age"]), axis=1)age_category_rep = death.groupby(["age_category", "cause_of_death"])["name"].count().sort_values(ascending=False)f = plt.figure(figsize=(8, 15))the_grid = GridSpec(4, 1)for cat in [("child", 0, 0), ("young", 1, 0), ("adult", 2, 0), ("old", 3, 0)]:    x = age_category_rep[cat[0]][1:10]    y = age_category_rep[cat[0]][11:].sum()    plt.subplot(the_grid[cat[1], cat[2]], aspect=1)    x["others"] = y    plt.pie(x, labels=x.index, autopct='%1.1f%%', startangle=10)    plt.axis('equal')    plt.title(cat[0], y=1.08, fontweight="bold")    plt.tight_layout()f.suptitle("Composition of known cause of death for every category", y=1.03)plt.show()

output：

– age birth_year cause_of_death death_month death_year 0 85 1931 natural causes January 2006 1 49 1967 murdered January 2006 2 64 1952 Alzheimer’s disease January 2006 3 86 1930 Alzheimer’s disease January 2006 4 82 1934 cancer January 2006 – famous_for name nationality 0 American businessman Frank Cary American 1 American musician Bryan Harvey American 2 American baseball playe Paul Lindblad American 3 American politician Charles Porter American 4 Cuban nightclub Ofelia Fox Cuban