【毕业设计】奥运会数据分析与可视化 - python 大数据

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1 简介

Hi，大家好，这里是丹成学长的毕设系列文章！

对毕设有任何疑问都可以问学长哦!

这两年开始，各个学校对毕设的要求越来越高，难度也越来越大… 毕业设计耗费时间，耗费精力，甚至有些题目即使是专业的老师或者硕士生也需要很长时间，所以一旦发现问题，一定要提前准备，避免到后面措手不及，草草了事。

为了大家能够顺利以及最少的精力通过毕设，学长分享优质毕业设计项目，今天要分享的新项目是

奥运会数据集分析

学长这里给一个题目综合评分(每项满分5分)

• 难度系数：4分
• 工作量：4分
• 创新点：3分

选题指导, 项目分享：

https://gitee.com/yaa-dc/BJH/blob/master/gg/cc/README.md

2 导入包+基本的数据处理

from plotly import __version__
print (__version__)
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.graph_objs import *
import colorlover as cl
# import seaborn as sns
# color = sns.color_palette()
f_p = '/home/kesci/input/olympic/athlete_events.csv'
athlete_events = pd.read_csv(f_p)

3 生成奥运会运动项目的词云

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

def show_wordcloud(data, title = None):
    wordcloud = WordCloud(
        background_color='white',
        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'], title = "往届奥运比赛项目词云")

4 查看参赛者的男女基本信息

fig = {
  "data": [{
      "values": athlete_events['Sex'].value_counts(),
      "labels": ["男性", "女性",],
      "marker": {'colors': cl.scales['5']['div']['PuOr']},
      "name": "参赛者的男女比例",
      "hoverinfo":"label+percent+name",
      "hole": .4,
      "type": "pie"
  }],"layout": {"title":"参赛者的男女比例"}
}
iplot(fig, filename='donut')

5 在120年来Top 20得金牌最多的国家

# 根据奖牌类型分组，分别计算每个国家的不同奖牌数并给予这列数值'Medal_Count'的列名。
country_medal = athlete_events.groupby(by = ['Medal']).Team.value_counts().reset_index(name = 'Medal_Count')
# 筛选出金牌类型的df,根据字段Medal_Count降序排列国家，选出前20个记录。
top20_country_medal = country_medal[country_medal.Medal == 'Gold'].sort_values(by = ['Medal_Count'], ascending = False).head(20)

# 绘制柱状图
trace = Bar(
    x = top20_country_medal.Team,
    y = top20_country_medal.Medal_Count,
    marker = dict(color = cl.scales['11']['div']['PuOr'], reversescale = True)
    )
layout = Layout(title = "Top 20 的金牌数最多的国家") # 设置layout

data = [trace]
fig = Figure(data = data, layout = layout)
iplot(fig)

6 中国历届奥运会运动员获得奖牌人数

china = athlete_events[athlete_events.Team == 'China']

china_medal = china.groupby(by = 'Year').Medal.value_counts().reset_index(name = "medal_count")

y0 = china_medal[china_medal.Medal == 'Gold'].medal_count
x0 = china_medal[china_medal.Medal == 'Gold'].Year
y1 = china_medal[china_medal.Medal == 'Silver'].medal_count
x1 = china_medal[china_medal.Medal == 'Silver'].Year
y2 = china_medal[china_medal.Medal == 'Bronze'].medal_count
x2 = china_medal[china_medal.Medal == 'Bronze'].Year

x = china_medal.Year

trace0 = Bar(
    x = x0, 
    y = y0, 
    name = 'Gold',
    text = y0,
    textposition = 'auto',
    marker=dict(
        color='gold',
        line=dict(
            color='rgb(8,48,107)',
            width=1.5),
        ),
    opacity=0.6
    )
    
trace1 = Bar(
    x = x1,
    y = y1,
    name = 'Silver',
    text = y1,
    textposition = 'auto',
    marker=dict(
        color = 'silver',
        line=dict(
            color='rgb(8,48,107)',
            width=1.5),
        ),
    opacity=0.6
    )
    
trace2 = Bar(
    x = x2,
    y = y2,
    name = 'Bronze',
    text = y2,
    textposition = 'auto',
    marker=dict(
        color='olive',
        line=dict(
            color='rgb(8,48,107)',
            width=1.5),
        ),
    opacity=0.6
    )
    
data = [trace0, trace1, trace2]
layout = Layout(
    barmode='group',
    width=800,
    hovermode='closest',
    title = '中国历届奥运会运动员获得 金牌 银牌 铜牌 的人数'
    )
fig = Figure(data = data, layout = layout)
iplot(fig)

7 Top 10 中国的强项运动项目

# 根据运动项目group,分别计算group内的值之合
china_sports = china_gold.groupby(by = 'Sport',as_index=False).Medal.agg('sum') 
# 按从大到小的顺序排序
china_sports = china_sports.sort_values(['Medal'], ascending= False)
# 选出前10的运动项目
top10_china_sports = china_sports.head(10)

# 定义颜色盘
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 = top10_china_sports.Sport.shape[0]
plot_width = 200 # 绘制宽度
section_h = 100  # section的高度
section_d = 15   # sections之间的间隔

# 用来计算其他section的宽度的乘系数
unit_width = plot_width / max(top10_china_sports['Medal'])   # 200 / 56 = 3.57
# 每个漏斗部分相对于绘图宽度的宽度
phase_w = [int(v * unit_width) for v in top10_china_sports['Medal']] 
# 绘制图的总高度
height = section_h * n_phase + section_d * (n_phase - 1)  

shapes = [] # 列表存储所有的绘制形状
label_y = [] # 列表存储每个section的name、value文本的Y轴地址

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 = Scatter(
    x=[-200]*n_phase,
    y=label_y,
    mode='text',
    text= top10_china_sports['Sport'],
    textfont=dict(
        color='rgb(200,200,200)',
        size=15
    )
)  

# For phase values
value_trace = Scatter(
    x=[-350]*n_phase,
    y=label_y,
    mode='text',
    text=top10_china_sports['Medal'],
    textfont=dict(
        color='rgb(200,200,200)',
        size=12
    )
)

data = [label_trace, value_trace]
 
layout = Layout(
    title="Top 10 中国的强项运动项目",
    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 = Figure(data=data, layout=layout)
iplot(fig)

8 最后