数据分析-26-120年奥运会数据分析（包含代码数据）

0. 代码数据下载

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1. 项目背景

本项目是对120年来的奥运会数据集(夏季奥运会)的简单分析。
主要探讨的是以下三个方面：

1. 奥运会里的男性与女性运动员

2. 奥运会历年来的Top

3. 中国的奥运会历史

2. 项目分析

1. 数据说明

该数据集包含两个文件：

• athlete_events.csv：参赛运动员基本生物数据和奖牌结果

• noc_regions.csv：国家奥委会3个字母的代码与对应国家信息

文件athlete_events.csv中包含15个字段，具体信息如下：

字段名称	字段含义
ID	给每个运动员的唯一ID
Name	运动员名字
Sex	性别
Age	年龄
Height	身高
Weight	体重
Team	所代表的国家队
NOC	国家奥委会3个字母的代码
Games	年份与季节
Year	比赛年份
Season	比赛季节
City	举办城市
Sport	运动类别
Event	比赛项目
Medal	奖牌

文件noc_regions.csv中包含3个字段，具体信息如下：

字段名称	字段含义
NOC	国家奥委会3个字母的代码
Region	国家
Notes	地区

2. 数据处理

a. 准备数据处理的包

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.graph_objs import *
import plotly.graph_objs as go
import colorlover as cl
from plotly.offline import init_notebook_mode, iplot

f_p = 'athlete_events.csv'
athlete_events = pd.read_csv(f_p)

b. 读取前五行数据

athlete_events.head()

c. 数据大小

athlete_events.shape

(271116, 15)

d. 数据中各个字段的空值的个数

athlete_events.isnull().sum()

e. 字段信息

athlete_events.info()

f. 数据统计

athlete_events.describe()

3. 词云（通过创建词云展示奥运会的热门运动项目，字体越大代表越热门）

print(' Total of',athlete_events['Sport'].nunique(),'unique sports were played. \n \n Following is the list:\n \n', athlete_events['Sport'].unique())

from wordcloud import WordCloud, STOPWORDS
stopwords = set(STOPWORDS)

def show_wordcloud(data, title = None):
    wordcloud = WordCloud(
        background_color='black',
        stopwords=stopwords,
        max_words=200,
        max_font_size=40, 
        scale=3,
        random_state=1 # chosen at random by flipping a coin; it was heads
).generate(str(data))

    fig = plt.figure(1, figsize=(15, 15))
    plt.axis('off')
    if title: 
        fig.suptitle(title, fontsize=20)
        fig.subplots_adjust(top=2.3)

    plt.imshow(wordcloud)
    plt.show()

show_wordcloud(athlete_events['Sport'])

4. 饼图 查看奥运会男女参赛人数的比例

fig = {
  "data": [
    {
      "values": athlete_events['Sex'].value_counts(),
      "labels": [
        "Male",
        "Female",
      ],
        'marker': {'colors': ['rgb(175, 49, 35)',
                                  'rgb(177, 180, 34)']},
      "name": "Sex Ratio of Participants",
      "hoverinfo":"label+percent+name",
      "hole": .4,
      "type": "pie"
    }],
     "layout": {
        "title":"Sex Ratio of Participants"
     }
}
iplot(fig, filename='donut')

5. 金牌数最多的前20个国家

df_medals=athlete_events.loc[athlete_events['Medal']=='Gold']

cnt_srs = df_medals['Team'].value_counts().head(20)

trace = go.Bar(
    x=cnt_srs.index,
    y=cnt_srs.values,
    marker=dict(
        color="blue",
        #colorscale = 'Blues',
        reversescale = True
    ),
)

layout = go.Layout(
    title='Top 20 countries with Maximum Gold Medals'
)

data = [trace]
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename="medal")  

6. 最受欢迎的运动

cnt_srs = athlete_events['Sport'].value_counts()

trace = go.Bar(
    x=cnt_srs.index,
    y=cnt_srs.values,
    marker=dict(
        color=cnt_srs.values,
        colorscale = 'Picnic',
        reversescale = True
    ),
)

layout = go.Layout(
    title='Most Popular Sport'
)

data = [trace]
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename="sport")

7. 美国最好的10项体育运动

df_usa=athlete_events.loc[(athlete_events['Team']=='United States')]
df_usa_medal=df_usa.loc[df_usa['Medal']=='Gold']

medal_map = {'Gold':1}
df_usa_medal['Medal'] = df_usa_medal['Medal'].map(medal_map)

df_usa_sport=df_usa_medal.groupby(['Sport'],as_index=False)['Medal'].agg('sum')

df_usa_sport=df_usa_sport.sort_values(['Medal'],ascending=False)

df_usa_sport=df_usa_sport.head(10)

colors = ['#91BBF4', '#91F4F4', '#F79981', '#F7E781', '#C0F781','rgb(32,155,160)', 'rgb(253,93,124)', 'rgb(28,119,139)', 'rgb(182,231,235)', 'rgb(35,154,160)']

n_phase = len(df_usa_sport['Sport'])
plot_width = 200

# height of a section and difference between sections 
section_h = 100
section_d = 10

# multiplication factor to calculate the width of other sections
unit_width = plot_width / max(df_usa_sport['Medal'])

# width of each funnel section relative to the plot width
phase_w = [int(value * unit_width) for value in df_usa_sport['Medal']]

height = section_h * n_phase + section_d * (n_phase - 1)

# list containing all the plot shapes
shapes = []

# list containing the Y-axis location for each section's name and value text
label_y = []

for i in range(n_phase):
        if (i == n_phase-1):
                points = [phase_w[i] / 2, height, phase_w[i] / 2, height - section_h]
        else:
                points = [phase_w[i] / 2, height, phase_w[i+1] / 2, height - section_h]

        path = 'M {0} {1} L {2} {3} L -{2} {3} L -{0} {1} Z'.format(*points)

        shape = {
                'type': 'path',
                'path': path,
                'fillcolor': colors[i],
                'line': {
                    'width': 1,
                    'color': colors[i]
                }
        }
        shapes.append(shape)
        
        # Y-axis location for this section's details (text)
        label_y.append(height - (section_h / 2))

        height = height - (section_h + section_d)
        
label_trace = go.Scatter(
    x=[-200]*n_phase,
    y=label_y,
    mode='text',
    text=df_usa_sport['Sport'],
    textfont=dict(
        color='rgb(200,200,200)',
        size=15
    )
)
 
# For phase values
value_trace = go.Scatter(
    x=[-350]*n_phase,
    y=label_y,
    mode='text',
    text=df_usa_sport['Medal'],
    textfont=dict(
        color='rgb(200,200,200)',
        size=12
    )
)

data = [label_trace, value_trace]
 
layout = go.Layout(
    title="<b>Top 10 Sports in which USA is best</b>",
    titlefont=dict(
        size=12,
        color='rgb(203,203,203)'
    ),
    shapes=shapes,
    height=600,
    width=800,
    showlegend=False,
    paper_bgcolor='rgba(44,58,71,1)',
    plot_bgcolor='rgba(44,58,71,1)',
    xaxis=dict(
        showticklabels=False,
        zeroline=False,
    ),
    yaxis=dict(
        showticklabels=False,
        zeroline=False
    )
)
 
fig = go.Figure(data=data, layout=layout)
iplot(fig)

---

关注公众号：『AI学习星球』
回复：奥运会数据分析 即可获取数据下载。
算法学习、4对1辅导、论文辅导或核心期刊可以通过公众号或?v：codebiubiubiu滴滴我