更新subway_analysis.py, subway_get.py, rocket.jpg

subway_analysis.py

+from wordcloud import WordCloud, ImageColorGenerator
+from pyecharts import Line, Bar, Geo
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import jieba
+##使用版本：pyecharts 0.3版本
+plt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签
+plt.rcParams['axes.unicode_minus']=False # 设置正常显示符号
+
+# 设置列名与数据对齐
+pd.set_option('display.unicode.ambiguous_as_wide', True)
+pd.set_option('display.unicode.east_asian_width', True)
+# 显示10行
+pd.set_option('display.max_rows', 10)
+# 读取数据
+df = pd.read_csv('../data/data-analyselesson/subway.csv', header=None, names=['city', 'line', 'station'], encoding='gbk')
+print(type(df))
+# 统计各个城市地铁线路情况
+df_line = df.groupby(['city', 'line']).count().reset_index()
+print(df_line)
+
+def create_map(df):
+    # 绘制地图
+    value = [i for i in df['line']]
+    attr = [i for i in df['city']]
+    geo = Geo("已开通地铁城市分布情况", title_pos='center', title_top='0', width=800, height=400, title_color="#fff", background_color="#404a59", )
+    geo.add("", attr, value, is_visualmap=True, visual_range=[0, 25], visual_text_color="#fff", symbol_size=15)
+    geo.show_config()
+    geo.render("已开通地铁城市分布情况.html")
+def create_line(df):
+    """
+    生成城市地铁线路数量分布情况
+    """
+    title_len = df['line']
+    bins = [0, 5, 10, 15, 20, 25]
+    level = ['线路数0-5', '线路数5-10', '线路数10-15', '线路数15-20', '线路数20以上']
+    len_stage = pd.cut(title_len, bins=bins, labels=level).value_counts().sort_index()
+    # 生成柱状图
+    attr = len_stage.index
+    v1 = len_stage.values
+    bar = Bar("各城市地铁线路数量分布", title_pos='center', title_top='18', width=800, height=400)
+    bar.add("城市个数", attr, v1, is_stack=True, is_label_show=True)
+    bar.render("各城市地铁线路数量分布.html")
+
+
+# 各个城市地铁线路数
+df_city = df_line.groupby(['city']).count().reset_index().sort_values(by='line', ascending=False)
+print(df_city)
+create_map(df_city)
+create_line(df_city)
+
+# 哪个城市哪条线路地铁站最多
+print(df_line.sort_values(by='station', ascending=False))#这里只是把各个城市的所有线路的站点数排列了一下
+
+# 去除重复换乘站的地铁数据
+df_station = df.groupby(['city', 'station']).count().reset_index()
+print(df_station)
+
+# 统计每个城市包含地铁站数(已去除重复换乘站)
+print(df_station.groupby(['city']).count().reset_index().sort_values(by='station', ascending=False))
+
+
+def create_wordcloud(df):
+    """
+    #生成地铁名词云
+    """
+    # 分词
+    text = ''
+    for line in df['station']:
+        text += ' '.join(jieba.cut(line, cut_all=False))
+        text += ' '
+    backgroud_Image = plt.imread('rocket.jpg')
+    wc = WordCloud(
+        background_color='white',
+        mask=backgroud_Image,
+        font_path='C:\Windows\Fonts\HGY3_CNKI.TTF',
+        max_words=1000,
+        max_font_size=150,
+        min_font_size=15,
+        prefer_horizontal=1,
+        random_state=50,
+    )
+    wc.generate_from_text(text)
+    img_colors = ImageColorGenerator(backgroud_Image)
+    wc.recolor(color_func=img_colors)
+    # 看看词频高的有哪些
+    process_word = WordCloud.process_text(wc, text)
+    sort = sorted(process_word.items(), key=lambda e: e[1], reverse=True)
+    print(sort[:50])
+    plt.imshow(wc)
+    plt.axis('off')
+    wc.to_file("地铁名词云.jpg")
+    print('生成词云成功!')
+
+
+# create_wordcloud(df_station)
+
+words = []
+for line in df['station']:
+    for i in line:
+        # 将字符串输出一个个中文
+        words.append(i)
+
+
+def all_np(arr):
+    """
+    #统计单字频率
+    """
+    arr = np.array(arr)
+    key = np.unique(arr)
+    result = {}
+    for k in key:
+        mask = (arr == k)
+        arr_new = arr[mask]
+        v = arr_new.size
+        result[k] = v
+    return result
+
+
+def create_word(word_message):
+    """
+    #生成柱状图
+    """
+    attr = [j[0] for j in word_message]
+    v1 = [j[1] for j in word_message]
+    bar = Bar("中国地铁站最爱用的字", title_pos='center', title_top='18', width=800, height=400)
+    bar.add("", attr, v1, is_stack=True, is_label_show=True)
+    bar.render("中国地铁站最爱用的字.html")
+
+
+word = all_np(words)
+word_message = sorted(word.items(), key=lambda x: x[1], reverse=True)[:10]
+create_word(word_message)
+
+# 选取上海的地铁站
+df1 = df_station[df_station['city'] == '上海']
+print(df1)
+# 选取上海地铁站名字包含路的数据
+df2 = df1[df1['station'].str.contains('路')]
+print(df2)
+
+# 选取武汉的地铁站
+df1 = df_station[df_station['city'] == '武汉']
+print(df1)
+# 选取武汉地铁站名字包含家的数据
+df2 = df1[df1['station'].str.contains('家')]
+print(df2)
+
+# 选取重庆的地铁站
+df1 = df_station[df_station['city'] == '重庆']
+print(df1)
+# 选取重庆地铁站名字包含家的数据
+df2 = df1[df1['station'].str.contains('家')]
+print(df2)
+
+
+def create_door(door):
+    """
+    生成柱状图
+    """
+    attr = [j for j in door['city'][:8]]
+    v1 = [j for j in door['line'][:8]]
+    bar = Bar("地铁站最爱用“门”命名的城市", title_pos='center', title_top='18', width=800, height=400,)
+    bar.add("", attr, v1, is_stack=True, is_label_show=True, yaxis_max=40)
+    bar.render("地铁站最爱用门命名的城市.html")
+
+
+# 选取地铁站名字包含门的数据
+df1 = df_station[df_station['station'].str.contains('门')]
+# 对数据进行分组计数
+df2 = df1.groupby(['city']).count().reset_index().sort_values(by='line', ascending=False)
+print(df2)
+create_door(df2)
+
+
+# 选取北京的地铁站
+df1 = df_station[df_station['city'] == '北京']
+print(df1)
+# 选取北京地铁站名字包含门的数据
+df2 = df1[df1['station'].str.contains('门')]
+print(df2)
+
+# 选取南京的地铁站
+df1 = df_station[df_station['city'] == '南京']
+# 选取南京地铁站名字包含门的数据
+df2 = df1[df1['station'].str.contains('门')]
+print(df2)
+
+# 选取西安的地铁站
+df1 = df_station[df_station['city'] == '西安']
+# 选取西安地铁站名字包含门的数据
+df2 = df1[df1['station'].str.contains('门')]
+print(df2)
+
+
+
+#问题：选取数量前5个名字中带有大学的地铁站的城市，并绘制柱状图
+df1=df[df['station'].str.contains('大学')]
+city_counts=df1['city'].value_counts()
+plt.figure(figsize=(10,5))
+labelline=list(city_counts[:5].index)#
+print(labelline)#['上海', '沈阳', '北京', '天津', '重庆']
+plt.xlabel('城市')
+plt.ylabel('站点数量')
+plt.title('名字中带有大学的地铁站的城市数量分布')
+plt.bar([i for i in labelline],city_counts[:5])
+
+#问题：绘制北京、武汉、天津、上海等各线路站点数量的折线图趋势分布
+#北京：
+df1=df[df['city']=='北京']
+Bei_station=df1['line'].value_counts()
+print(Bei_station)
+plt.figure(figsize=(12,6))
+labelline=list(Bei_station[:8].index)
+plt.xlabel=('线路')
+plt.ylabel=('各站点数量')
+plt.title("北京各线路站点数量的分布趋势")
+plt.plot([i for i in labelline],Bei_station[:8])
+# plt.show()
+
+#武汉
+df1=df[df['city']=='武汉']
+Wu_station=df1['line'].value_counts()
+print(Wu_station)
+plt.figure(figsize=(12,6))
+labelline=list(Wu_station[:8].index)
+plt.xlabel=('线路')
+plt.ylabel=('各站点数量')
+plt.title("武汉各线路站点数量的分布趋势")
+plt.plot([i for i in labelline],Wu_station[:8])
+# plt.show()
+
+#天津
+df1=df[df['city']=='天津']
+Tian_station=df1['line'].value_counts()
+print(Tian_station)
+plt.figure(figsize=(12,6))
+labelline=list(Tian_station[:8].index)
+plt.xlabel=('线路')
+plt.ylabel=('各站点数量')
+plt.title("天津各线路站点数量的分布趋势")
+plt.plot([i for i in labelline],Tian_station[:8])
+# plt.show()
+
+#上海
+df1=df[df['city']=='上海']
+Shang_station=df1['line'].value_counts()
+print(Shang_station)
+plt.figure(figsize=(12,6))
+labelline=list(Shang_station[:8].index)
+plt.xlabel=('线路')
+plt.ylabel=('各站点数量')
+plt.title("上海各线路站点数量的分布趋势")
+plt.plot([i for i in labelline],Shang_station[:8])
+# plt.show()
+
+#问题：各个城市的线路数量的饼状图分布
+line_count=df['city'].value_counts()
+plt.figure(figsize=(10,7))
+plt.pie(line_count,labels=line_count.index,autopct='%1.1f%%')
+plt.title('各个城市的线路数量的饼状图分布')
+# plt.show()
+
+#问题：各个城市的站点数量的饼状图分布
+#饼状图展示
+df_station = df.groupby(['city', 'station']).count().reset_index()  #此处去除每个城市的重复换乘站点数，得到实际数量的站点数量
+df1=df_station.groupby(['city']).count().reset_index().sort_values(by='station', ascending=False)
+df1['city']=df1['city']+'(站点数'+df1['station'].map(str)+')'
+line_count=df1['station']
+plt.figure(figsize=(10,7))
+plt.pie(line_count,labels=df1['city'],autopct='%1.1f%%')
+plt.title('各个城市的站点数量的饼状图分布')
+plt.show()
+
+# #散点图展示
+# df_station = df.groupby(['city', 'station']).count().reset_index()  #此处去除每个城市的重复换乘站点数，得到实际数量的站点数量
+# df1=df_station.groupby(['city']).count().reset_index().sort_values(by='station', ascending=False)
+# line_count=df1['station']
+# plt.figure(figsize=(10,7))
+# plt.xlabel=('城市')
+# plt.ylabel=('站点数量')
+# plt.scatter(x=df1['city'],y=line_count,marker='*')
+# plt.title('各个城市的站点数量的散点图分布')
+# plt.show()
+
+#各城市的每条线路的站点数量的变化 折线图
+df1=df_line.sort_values(by='station', ascending=False)#by中指定按照什么列排序，ascending中默认升序排列，值为True
+station_count=df1['line']+df1['city']
+plt.figure(figsize=(15,8))
+labelline=list(station_count[:12])
+plt.xlabel=('线路')
+plt.ylabel=('各站点数量')
+plt.title("各城市各线路的站点数量前10的变化")
+plt.plot([i for i in labelline],df1['station'][:12])
+plt.show()
+
+#每个城市的哪条线路的地铁站点数量最多  柱形图
+df_1=df_line.sort_values(by='station', ascending=False)
+df_2=df_1.groupby('city')['station'].max().reset_index(drop=False)#保留索引
+line_station_c=df_2.sort_values(by='station',ascending=False)
+# line_station_c.to_csv("../1.csv",header=False,index=False)
+plt.figure(figsize=(15,5))
+labelline=list(line_station_c['city'])
+# line_text=pd.merge(left=line_station_c,right=df_1,on=['city','station'],how='inner')
+# line_text.to_csv("../2.csv",header=False,index=False)
+labelline=labelline#+line_text['line'].map(str)
+plt.xlabel=('城市')
+plt.ylabel=('站点数量')
+plt.bar([i for i in labelline],line_station_c['station'])
+plt.title('每个城市哪条线路的站点数最多')
+plt.show()
+
+#问题：统计各个城市的大学数量，然后利用回归图进行拟合（分析各个城市的大学数量与站点数量的关系
+df_uni= pd.read_csv('../university.csv', header=None, names=['city', 'uni_count'], encoding='gbk')
+df_uni=pd.merge(left=line_station_c,right=df_uni,on='city',how='inner') #将两个表格中的数据基于city列进行内连接。
+x=df_uni['uni_count']
+y=df_uni['station']
+sns.regplot(x=x,y=y,color='b')
+plt.title('分析各个城市的大学数量与站点数量的关系')
+plt.show()
+
+#散点图
+fig=plt.figure(figsize=(10,7))
+plt.xlabel=('站点数量')
+plt.ylabel=('大学数量')
+plt.title('各个城市的大学数量与站点数量的关系')
+plt.scatter(x=x,y=y,cmap='b',marker='*',alpha=0.8)
+plt.grid()
+plt.show()
+
+#seaborn的双变量图：可以查看多变量之间的分布关系，也可以显示它本身的单变量情况
+df_s=df_uni
+sns.jointplot(x='uni_count',y='station',data=df_s)
+plt.show()
+plt.close()
+
+#问题：选取郑州、武汉、广州、长沙同名的线路1-线路6，绘制折线图分析这些城市的目标线路的站点数量分布
+df_1=df_line.sort_values(by='station', ascending=False)
+zz_=df_1[df_1['city']=='郑州'].sort_values(by='line',ascending=False).reset_index()#ascending参数值为False时，则数据按指定列降序排序。
+zz_=zz_.loc[zz_['line'].isin(['1号线','2号线','3号线','4号线','5号线','6号线'])]
+
+wh_=df_1[df_1['city']=='武汉'].sort_values(by='line',ascending=False).reset_index()
+wh_=wh_.loc[wh_['line'].isin(['1号线','2号线','3号线','4号线','5号线','6号线'])]
+
+gz_=df_1[df_1['city']=='广州'].sort_values(by='line',ascending=False).reset_index()
+gz_=gz_.loc[gz_['line'].isin(['1号线','2号线','3号线','4号线','5号线','6号线'])]
+
+cs_=df_1[df_1['city']=='长沙'].sort_values(by='line',ascending=False).reset_index()
+cs_=cs_.loc[cs_['line'].isin(['1号线','2号线','3号线','4号线','5号线','6号线'])]
+
+print(zz_)
+print(wh_)
+print(gz_)
+print(cs_)
+plt.figure(figsize=(10,7))
+L1=plt.plot(zz_['line'],zz_['station'],color='b',label='郑州线路1-6的站点数量变化')
+L2=plt.plot(wh_['line'],wh_['station'],color='g',label='武汉线路1-6的站点数量变化')
+L3=plt.plot(gz_['line'],gz_['station'],color='r',label='广州线路1-6的站点数量变化')
+L4=plt.plot(cs_['line'],cs_['station'],color='k',label='长沙线路1-6的站点数量变化')
+plt.legend()
+plt.title('郑州、武汉、广州、长沙同名的线路1-线路6的站点数量分布')
+plt.xlabel=('线路1-线路6')
+plt.ylabel=('站点数量')
+plt.show()
+
+#全国各城市的总的换乘站点数量（2换乘、3换乘、4换乘等）分布统计
+df_1=df.groupby(['city','station']).count().reset_index()
+print(df_1)
+df_1=df_1[df_1['line']>1]#筛选出来全国的换乘站点数
+tran_sit=df_1.groupby('line').count().reset_index()  #保留原索引，但是值是count()函数计数之后的值
+plt.figure(figsize=(10,5))
+plt.xlabel=('站点可换乘等级')
+plt.ylabel=('站点数量')
+plt.bar(tran_sit['line'],tran_sit['station'],color='g')
+plt.title('全国各城市总的换乘站点数量（2换乘、3换乘、4换乘等）分布统计')
+plt.show()
+print(tran_sit[tran_sit['line']==5]['station'])
\ No newline at end of file

subway_get.py

+import json
+import requests
+from bs4 import BeautifulSoup
+
+headers = {'user-agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'}
+
+
+def get_message(ID, cityname, name):
+    """
+    地铁线路信息获取
+    """
+    url = 'http://map.amap.com/service/subway?_1555502190153&srhdata=' + ID + '_drw_' + cityname + '.json'
+    response = requests.get(url=url, headers=headers)
+    html = response.text
+    result = json.loads(html)
+    for i in result['l']:
+        for j in i['st']:
+            # 判断是否含有地铁分线
+            if len(i['la']) > 0:
+                print(name, i['ln'] + '(' + i['la'] + ')', j['n'])
+                with open('../data/data-analyselesson/subway.csv', 'a+', encoding='gbk') as f:
+                    f.write(name + ',' + i['ln'] + '(' + i['la'] + ')' + ',' + j['n'] + '\n')
+            else:
+                print(name, i['ln'], j['n'])
+                with open('../data/data-analyselesson/subway.csv', 'a+', encoding='gbk') as f:
+                    f.write(name + ',' + i['ln'] + ',' + j['n'] + '\n')
+
+
+def get_city():
+    """
+    城市信息获取
+    """
+    url = 'http://map.amap.com/subway/index.html?&1100'
+    response = requests.get(url=url, headers=headers)
+    html = response.text
+    # 编码
+    html = html.encode('ISO-8859-1')
+    html = html.decode('utf-8')
+    soup = BeautifulSoup(html, 'lxml')
+    # 城市列表
+    res1 = soup.find_all(class_="city-list fl")[0]
+    res2 = soup.find_all(class_="more-city-list")[0]
+    for i in res1.find_all('a'):
+        # 城市ID值
+        ID = i['id']
+        # 城市拼音名
+        cityname = i['cityname']
+        # 城市名
+        name = i.get_text()
+        get_message(ID, cityname, name)
+    for i in res2.find_all('a'):
+        # 城市ID值
+        ID = i['id']
+        # 城市拼音名
+        cityname = i['cityname']
+        # 城市名
+        name = i.get_text()
+        get_message(ID, cityname, name)
+
+
+if __name__ == '__main__':
+    get_city()