11.筛选出平均气温在18-25度的数据
# 筛选出平均气温在18-25度的数据 filtered_data = data[(data['平均温度'] >= 18) & (data['平均温度'] <= 25)] # 分别统计两个城市符合条件的天数 gz_num_days = len(filtered_data[filtered_data['城市'] == '广州']) zj_num_days = len(filtered_data[filtered_data['城市'] == '湛江']) # 输出结果 gz_num_days
12.解决乱码问题
plt.rcParams["font.sans-serif"] = ["SimHei"] # 设置字体 plt.rcParams["axes.unicode_minus"] = False # 该语句解决图像中的“-”负号的乱码问题
13.广州-湛江每日平均温度折线图
# 筛选出广州和湛江的数据 gz_data = data[data['城市'] == '广州'] zj_data = data[data['城市'] == '湛江'] # 提取日期和平均温度数据 x = gz_data['日期'] y1 = gz_data['平均温度'] y2 = zj_data['平均温度'] # 绘制折线图 plt.figure(dpi=500, figsize=(10, 5)) plt.title("广州-湛江每日平均温度折线图") plt.plot(x, y1, color='red', label='广州') plt.plot(x, y2, color='blue', label='湛江') # 获取图的坐标信息 coordinates = plt.gca() # 设置x轴每个刻度的间隔天数 xLocator = mpl.ticker.MultipleLocator(30) coordinates.xaxis.set_major_locator(xLocator) # 将日期旋转30° plt.xticks(rotation=30) plt.xticks(fontsize=8) plt.ylabel("温度(℃)") plt.xlabel("日期") plt.legend() plt.savefig("广州-湛江每日平均温度折线图.png") plt.show()
14.广州-湛江每月气温折线图
# 筛选出广州和湛江的数据 data_GZ_ZJ = grouped_AB[(grouped_AB['城市'] == '广州') | (grouped_AB['城市'] == '湛江')] # 绘制折线图 fig, ax = plt.subplots() for city in ['广州', '湛江']: ax.plot(data_GZ_ZJ[data_GZ_ZJ['城市'] == city]['月份'], data_GZ_ZJ[data_GZ_ZJ['城市'] == city]['平均温度'], label=city) # 设置图例和标题 ax.legend() ax.set_title('广州-湛江每月气温折线图') # 显示图形 plt.show()
15.广州-湛江各类天气饼图
# 创建一个 1 行 2 列的子图，figsize 参数用于设置图表大小 fig, ax = plt.subplots(1, 2, figsize=(10, 5)) # 绘制广州各类天气饼图 ax[0].pie(df3['天数'], labels=df3['新天气'], autopct='%1.1f%%', startangle=90) ax[0].set_title('广州各类天气天数占比') # 绘制湛江各类天气饼图 ax[1].pie(df4['天数'], labels=df4['新天气'], autopct='%1.1f%%', startangle=90) ax[1].set_title('湛江各类天气天数占比') # 调整子图之间的间距 plt.subplots_adjust(wspace=0.3) # 显示图表 plt.show()
16.广州和湛江各类天气天数对比
import matplotlib.pyplot as plt # 创建一个画布 fig, ax = plt.subplots(figsize=(10, 5)) # 绘制广州各类天气条形图 ax.bar(df3['新天气'], df3['天数'], width=0.4, label='广州') # 绘制湛江各类天气条形图 ax.bar(df4['新天气'], df4['天数'], width=0.4, label='湛江', alpha=0.7) # 设置图例 ax.legend() # 设置 x 轴标签和标题 ax.set_xlabel('天气类型') ax.set_ylabel('天数') ax.set_title('广州和湛江各类天气天数对比') # 显示图表 plt.show()
17.广州各类天气天数占比
import pandas as pd from pyecharts.charts import Pie from pyecharts import options as opts # 读取csv文件并转换为列表格式 df = pd.read_csv('df3.csv') data_list = df[['新天气', '天数']].values.tolist() # 生成饼图 pie = Pie() pie.add("", data_list) pie.set_global_opts(title_opts=opts.TitleOpts(title="广州各类天气天数占比")) pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}")) pie.render_notebook()
18.湛江各类天气天数占比
import pandas as pd from pyecharts.charts import Pie from pyecharts import options as opts # 读取csv文件并转换为列表格式 df = pd.read_csv('df4.csv') data_list = df[['新天气', '天数']].values.tolist() # 生成饼图 pie = Pie() pie.add("", data_list) pie.set_global_opts(title_opts=opts.TitleOpts(title="湛江各类天气天数占比")) pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}")) pie.render_notebook()
19.下面我们使用前端网页生成仪表图,这个仪表图的id是使用哈希加密来定义的,这些名称都是使用Unicode编码中的字符来写的,需要的可以研究一下
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最后前端网页实现的网页如下图所示:
博主写代码来之不易
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一键三连！！！
一键三连！！！
感谢一键三连！
