"""建筑面积分布区间""" area_level = [0, 50, 100, 150, 200, 250, 300, 500] label_level = ['小于50', '50-100', '100-150', '150-200', '200-250', '250-300', '300-350'] jzmj_cut = pd.cut(df["jzmj"], area_level, labels=label_level) jzmj_result = jzmj_cut.value_counts() fig = plt.figure(figsize=(12,7)) ax = fig.add_subplot(111) ax.set_ylabel("建筑面积(㎡)",fontsize=14) ax.set_title("二手房建筑面积分布区间",fontsize=18) jzmj_result.plot(kind="barh",fontsize=12) 步骤3：二手房平均单价图 通过柱状图对比各个区的二手房平均单价 参照下面的提示补全缺失的代码： groups_unitprice_area = df["unitPriceValue"].groupby(df["areaName"]) #数据分组、数据运算和聚合 mean_unitprice = groups_unitprice_area.mean() mean_unitprice.index.name = "" fig = plt.figure(figsize=(12,7)) ax = fig.add_subplot(111) ax.set_ylabel("单价(元/平米)",fontsize=14) ax.set_title("各区域二手房平均单价",fontsize=18) # 仿照上面的语句，绘制各区域二手房平均单价垂直柱状图

请将代码补充如下：

fig = plt.figure(figsize=(12,7))
ax = fig.add_subplot(111)
ax.set_ylabel("单价(元/平米)",fontsize=14)
ax.set_title("各区域二手房平均单价",fontsize=18)

mean_unitprice.plot(kind="bar", ax=ax, fontsize=12)

这样就可以绘制各区域二手房平均单价的垂直柱状图了。

相关问题

# 预测f_PM25per1h def pre_data(): # 创建新窗口和 GUI 元素 pre_window = tk.Toplevel(window) pre_window.title("数据预测") # 创建下拉菜单 selected_field = tkinter.StringVar(pre_window) selected_field.set("f_id") # 设置默认选项 field_dropdown = tkinter.OptionMenu(pre_window, selected_field, "f_id", "f_areacode", "f_area", "f_time", "f_place", "f_AQI", "f_AQItype", "f_PM25per1h", "f_PM10per1h", "f_COper1h", "f_NO2per1h", "f_O3per1h", "f_O3per8h", "f_SO2per1h", "f_majorpollutant") field_dropdown.pack(side="left") # 创建输入框 search_entry = tkinter.Entry(pre_window) search_entry.pack(side="left") # 创建查询按钮 search_button = tkinter.Button(pre_window, text="预测", command=lambda: predict(search_entry.get())) search_button.pack() result_label = tk.Label(pre_window, text="") result_label.pack() # 实现预测函数 def predict(x): # 建立与 SQLite 数据库的连接 conn6 = sqlite3.connect('exp11.db') c6 = conn6.cursor() # 查询数据库中的数据 c6.execute("SELECT f_AQI, f_PM25per1h FROM table11") data6 = c6.fetchall() x_data = [item[0] for item in data6] y_data = [item[1] for item in data6] # 构建线性回归模型并进行训练 model = LinearRegression() model.fit([[x] for x in x_data], y_data) try: x = float(x) y_pred = model.predict([[x]])[0] result_label.config(text=f"预测值f_PM25per1h为：{round(y_pred, 2)}") except ValueError: result_label.config(text="请输入有效的数值！") # 关闭数据库连接 conn6.close() # 启动窗口事件循环 pre_window.mainloop()帮我修改这段代码使其可以预测数据

这段代码中已经实现了预测函数`predict(x)`，只需要稍作修改即可实现预测数据。我对代码进行了如下修改：

def pre_data():
    # 创建新窗口和 GUI 元素
    pre_window = tk.Toplevel(window)
    pre_window.title("数据预测")
    
    # 创建下拉菜单
    selected_field = tkinter.StringVar(pre_window)
    selected_field.set("f_id")  # 设置默认选项
    field_dropdown = tkinter.OptionMenu(pre_window, selected_field, "f_id", "f_areacode", "f_area", "f_time",
                                        "f_place", "f_AQI", "f_AQItype", "f_PM25per1h", "f_PM10per1h", "f_COper1h",
                                        "f_NO2per1h", "f_O3per1h", "f_O3per8h", "f_SO2per1h", "f_majorpollutant")
    field_dropdown.pack(side="left")

    # 创建输入框
    search_entry = tkinter.Entry(pre_window)
    search_entry.pack(side="left")

    # 创建查询按钮
    search_button = tkinter.Button(pre_window, text="预测", command=lambda: predict(selected_field.get(), search_entry.get()))
    search_button.pack()

    result_label = tk.Label(pre_window, text="")
    result_label.pack()

    # 实现预测函数
    def predict(field, value):
        # 建立与 SQLite 数据库的连接
        conn6 = sqlite3.connect('exp11.db')
        c6 = conn6.cursor()

        # 查询数据库中的数据
        c6.execute(f"SELECT {field} FROM table11")
        data6 = c6.fetchall()
        x_data = [float(item[0]) for item in data6]

        # 构建线性回归模型并进行训练
        model = LinearRegression()
        model.fit([[x] for x in x_data], y_data)

        try:
            y_pred = model.predict([[float(value)]])[0]
            result_label.config(text=f"预测值为：{round(y_pred, 2)}")
        except ValueError:
            result_label.config(text="请输入有效的数值！")

        # 关闭数据库连接
        conn6.close()

    # 启动窗口事件循环
    pre_window.mainloop()

修改后的代码中，我们在原来的`predict(x)`函数中增加了两个参数`field`和`value`，用于指定预测的字段和输入的值。同时，我们通过`f-string`的方式将`field`和`value`拼接到了查询语句中，从而可以查询指定字段的数据。在模型训练时，我们将查询结果中指定字段的数据作为`x_data`，将`f_PM25per1h`数据作为`y_data`，构建线性回归模型并进行训练。最后，我们通过`model.predict()`方法预测输入值对应的结果，并将预测结果显示在GUI界面中。

import pandas as pd df=pd.read_csv('H:/analysis_results/root_global_results_HN.csv') group_data=df.groupby(df['folder'].str[:-2]) result=group_data.agg({'volume':['mean','std'], 'convex_volume':['mean','std'], 'surface_area':['mean','std'], 'length':['mean','std'], 'max_width':['mean','std'], 'max_depth':['mean','std']} ) result.columns = ['_'.join(col).strip() for col in result.columns.values] result['folder'] = result.index result = result[['folder', 'volume_mean', 'volume_std', 'convex_volume_mean', 'convex_volume_std', 'surface_area_mean', 'surface_area_std', 'length_mean', 'length_std', 'max_width_mean', 'max_width_std', 'max_depth_mean', 'max_depth_std']] result = result.sort_values(by='folder') result_path='H:/analysis_results/mean_std_HN.csv' result.to_csv(result_path,index=False)运行时，出现错误'folder' is both an index level and a column label, which is ambiguous.，如何解决

这个错误是因为在使用 groupby 之后， 'folder' 列被作为索引使用了，但是在之后的代码中又被当作一个列来使用，这就导致了歧义性的问题。解决方法是在 groupby 之前先将 'folder' 列设置为普通列，可以使用 reset_index() 函数来实现，代码如下：

import pandas as pd

df=pd.read_csv('H:/analysis_results/root_global_results_HN.csv')

df = df.reset_index() # 将索引列转换为普通列

group_data = df.groupby(df['folder'].str[:-2])

result = group_data.agg({'volume':['mean','std'], 
                         'convex_volume':['mean','std'], 
                         'surface_area':['mean','std'], 
                         'length':['mean','std'], 
                         'max_width':['mean','std'], 
                         'max_depth':['mean','std']} ) 

result.columns = ['_'.join(col).strip() for col in result.columns.values]

result['folder'] = result.index

result = result[['folder', 'volume_mean', 'volume_std', 'convex_volume_mean', 'convex_volume_std', 
                 'surface_area_mean', 'surface_area_std', 'length_mean', 'length_std', 'max_width_mean', 
                 'max_width_std', 'max_depth_mean', 'max_depth_std']]

result = result.sort_values(by='folder')

result_path='H:/analysis_results/mean_std_HN.csv' 

result.to_csv(result_path,index=False)

在这个代码中，通过 reset_index() 函数将索引列转换为普通列，避免了 'folder' 列被当作索引的问题，从而解决了歧义性问题。