近段时间天气暴热，所以采集北上广深去年天气数据，制作可视化图看下
作者：互联网
前言
最近天气异常暴热，看到某些地方地表温度居然达到70°，这就离谱
所以就想采集一下天气的数据，做个可视化图，回忆一下去年的天气情况
开发环境
- python 3.8 运行代码
- pycharm 2021.2 辅助敲代码
- requests 第三方模块
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天气数据采集
1. 发送请求
url = 'https://tianqi.2345.com/Pc/GetHistory?areaInfo%5BareaId%5D=54511&areaInfo%5BareaType%5D=2&date%5Byear%5D=2022&date%5Bmonth%5D=5' response = requests.get(url) print(response)
返回<Response [200]>: 请求成功
2. 获取数据
print(response.json())
3. 解析数据 天气信息提取出来
结构化数据解析：Python字典取值
非结构化数据解析：网页结构
json_data = response.json()
html_data = json_data['data']
select = parsel.Selector(html_data)
trs = select.css('table tr')
for tr in trs[1:]:
# 网页结构
# html网页 <td>asdfwaefaewfweafwaef</td> <a></a> <div></div>
# ::text: 我需要这个 标签里面的文本内容
td = tr.css('td::text').getall()
print(td)
4. 保存数据
with open('天气数据.csv', encoding='utf-8', mode='a', newline='') as f:
csv_writer = csv.writer(f)
csv_writer.writerow(td)
数据可视化效果
读取数据
data = pd.read_csv('天气数据.csv')
data
分割日期/星期
data[['日期','星期']] = data['日期'].str.split(' ',expand=True,n=1)
data
去除多余字符
data[['最高温度','最低温度']] = data[['最高温度','最低温度']].apply(lambda x: x.str.replace('°',''))
data.head()
北上广深2021年10月份天气热力图分布
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import seaborn as sns
#设置全局默认字体 为 雅黑
plt.rcParams['font.family'] = ['Microsoft YaHei']
# 设置全局轴标签字典大小
plt.rcParams["axes.labelsize"] = 14
# 设置背景
sns.set_style("darkgrid",{"font.family":['Microsoft YaHei', 'SimHei']})
# 设置画布长宽 和 dpi
plt.figure(figsize=(18,8),dpi=100)
# 自定义色卡
cmap = mcolors.LinearSegmentedColormap.from_list("n",['#95B359','#D3CF63','#E0991D','#D96161','#A257D0','#7B1216'])
# 绘制热力图
ax = sns.heatmap(data_pivot, cmap=cmap, vmax=30,
annot=True, # 热力图上显示数值
linewidths=0.5,
)
# 将x轴刻度放在最上面
ax.xaxis.set_ticks_position('top')
plt.title('北京最近10个月天气分布',fontsize=16) #图片标题文本和字体大小
plt.show()
北京2021年每日最高最低温度变化
color0 = ['#FF76A2','#24ACE6']
color_js0 = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
[{offset: 0, color: '#FFC0CB'}, {offset: 1, color: '#ed1941'}], false)"""
color_js1 = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
[{offset: 0, color: '#FFFFFF'}, {offset: 1, color: '#009ad6'}], false)"""
tl = Timeline()
for i in range(0,len(data_bj)):
coordy_high = list(data_bj['最高温度'])[i]
coordx = list(data_bj['日期'])[i]
coordy_low = list(data_bj['最低温度'])[i]
x_max = list(data_bj['日期'])[i]+datetime.timedelta(days=10)
y_max = int(max(list(data_bj['最高温度'])[0:i+1]))+3
y_min = int(min(list(data_bj['最低温度'])[0:i+1]))-3
title_date = list(data_bj['日期'])[i].strftime('%Y-%m-%d')
c = (
Line(
init_opts=opts.InitOpts(
theme='dark',
#设置动画
animation_opts=opts.AnimationOpts(animation_delay_update=800),#(animation_delay=1000, animation_easing="elasticOut"),
#设置宽度、高度
width='1500px',
height='900px', )
)
.add_xaxis(list(data_bj['日期'])[0:i])
.add_yaxis(
series_name="",
y_axis=list(data_bj['最高温度'])[0:i], is_smooth=True,is_symbol_show=False,
linestyle_opts={
'normal': {
'width': 3,
'shadowColor': 'rgba(0, 0, 0, 0.5)',
'shadowBlur': 5,
'shadowOffsetY': 10,
'shadowOffsetX': 10,
'curve': 0.5,
'color': JsCode(color_js0)
}
},
itemstyle_opts={
"normal": {
"color": JsCode(
"""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
offset: 0,
color: '#ed1941'
}, {
offset: 1,
color: '#009ad6'
}], false)"""
),
"barBorderRadius": [45, 45, 45, 45],
"shadowColor": "rgb(0, 160, 221)",
}
},
)
.add_yaxis(
series_name="",
y_axis=list(data_bj['最低温度'])[0:i], is_smooth=True,is_symbol_show=False,
# linestyle_opts=opts.LineStyleOpts(color=color0[1],width=3),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_js1)),
linestyle_opts={
'normal': {
'width': 3,
'shadowColor': 'rgba(0, 0, 0, 0.5)',
'shadowBlur': 5,
'shadowOffsetY': 10,
'shadowOffsetX': 10,
'curve': 0.5,
'color': JsCode(color_js1)
}
},
)
.set_global_opts(
title_opts=opts.TitleOpts("北京2021年每日最高最低温度变化\n\n{}".format(title_date),pos_left=330,padding=[30,20]),
xaxis_opts=opts.AxisOpts(type_="time",max_=x_max),#, interval=10,min_=i-5,split_number=20,axistick_opts=opts.AxisTickOpts(length=2500),axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color="grey"))
yaxis_opts=opts.AxisOpts(min_=y_min,max_=y_max),#坐标轴颜色,axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color="grey"))
)
)
tl.add(c, "{}".format(list(data_bj['日期'])[i]))
tl.add_schema(
axis_type='time',
play_interval=100, # 表示播放的速度
pos_bottom="-29px",
is_loop_play=False, # 是否循环播放
width="780px",
pos_left='30px',
is_auto_play=True, # 是否自动播放。
is_timeline_show=False)
tl.render_notebook()
北上广深10月份每日最高气温变化
# 背景色
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
# 线条样式
linestyle_dic = { 'normal': {
'width': 4,
'shadowColor': '#696969',
'shadowBlur': 10,
'shadowOffsetY': 10,
'shadowOffsetX': 10,
}
}
timeline = Timeline(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
width='980px',height='600px'))
bj, gz, sh, sz= [], [], [], []
all_max = []
x_data = data_10[data_10['城市'] == '北京']['日'].tolist()
for d_time in range(len(x_data)):
bj.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='北京')]["最高温度"].values.tolist()[0])
gz.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='广州')]["最高温度"].values.tolist()[0])
sh.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='上海')]["最高温度"].values.tolist()[0])
sz.append(data_10[(data_10['日'] == x_data[d_time]) & (data_10['城市']=='深圳')]["最高温度"].values.tolist()[0])
line = (
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js),
width='980px',height='600px'))
.add_xaxis(
x_data,
)
.add_yaxis(
'北京',
bj,
symbol_size=5,
is_smooth=True,
is_hover_animation=True,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
'广州',
gz,
symbol_size=5,
is_smooth=True,
is_hover_animation=True,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
'上海',
sh,
symbol_size=5,
is_smooth=True,
is_hover_animation=True,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
'深圳',
sz,
symbol_size=5,
is_smooth=True,
is_hover_animation=True,
label_opts=opts.LabelOpts(is_show=False),
)
.set_series_opts(linestyle_opts=linestyle_dic)
.set_global_opts(
title_opts=opts.TitleOpts(
title='北上广深10月份最高气温变化趋势',
pos_left='center',
pos_top='2%',
title_textstyle_opts=opts.TextStyleOpts(color='#DC143C', font_size=20)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
xaxis_opts=opts.AxisOpts(
# axislabel_opts=opts.LabelOpts(font_size=14, color='red'),
# axisline_opts=opts.AxisLineOpts(is_show=True,
# linestyle_opts=opts.LineStyleOpts(width=2, color='#DB7093'))
is_show = False
),
yaxis_opts=opts.AxisOpts(
name='最高气温',
is_scale=True,
# min_= int(min([gz[d_time],sh[d_time],sz[d_time],bj[d_time]])) - 10,
max_= int(max([gz[d_time],sh[d_time],sz[d_time],bj[d_time]])) + 10,
name_textstyle_opts=opts.TextStyleOpts(font_size=16,font_weight='bold',color='#5470c6'),
axislabel_opts=opts.LabelOpts(font_size=13,color='#5470c6'),
splitline_opts=opts.SplitLineOpts(is_show=True,
linestyle_opts=opts.LineStyleOpts(type_='dashed')),
axisline_opts=opts.AxisLineOpts(is_show=True,
linestyle_opts=opts.LineStyleOpts(width=2, color='#5470c6'))
),
legend_opts=opts.LegendOpts(is_show=True, pos_right='1%', pos_top='2%',
legend_icon='roundRect',orient = 'vertical'),
))
timeline.add(line, '{}'.format(x_data[d_time]))
timeline.add_schema(
play_interval=1000, # 轮播速度
is_timeline_show=True, # 是否显示 timeline 组件
is_auto_play=True, # 是否自动播放
pos_left="0",
pos_right="0"
)
timeline.render_notebook()
标签：10,color,True,天气,bj,图看,data,opts,暴热 来源： https://www.cnblogs.com/qshhl/p/16426623.html