feat(editor): ChatExcel

🔥ChatExcel Mode Operation Manual

docs/getting_started/application/chatexcel/chatexcel.md

@@ -0,0 +1,26 @@
+ChatExcel
+==================================
+ChatExcel uses natural language to analyze and query Excel data.![db plugins demonstration](../../../../assets/chat_excel/chat_excel_1.png)
+
+### 1.Select And Upload Excel or CSV File
+Select your excel or csv file to upload and start the conversation.
+```{tip}
+ChatExcel 
+
+The ChatExcel function supports Excel and CSV format files, select the corresponding file to use.
+```
+![db plugins demonstration](../../../../assets/chat_excel/chat_excel_2.png)
+![db plugins demonstration](../../../../assets/chat_excel/chat_excel_3.png)
+
+### 2.Wait for Data Processing
+After the data is uploaded, it will first learn and process the data structure and field meaning.
+![db plugins demonstration](../../../../assets/chat_excel/chat_excel_4.png)
+
+### 3.Use Data Analysis Calculation
+Now you can use natural language to analyze and query data in the dialog box.
+![db plugins demonstration](../../../../assets/chat_excel/chat_excel_5.png)
+![db plugins demonstration](../../../../assets/chat_excel/chat_excel_6.png)
+![db plugins demonstration](../../../../assets/chat_excel/chat_excel_7.png)
+
+
+

pilot/scene/chat_data/chat_excel/excel_learning/test.py

@@ -3,38 +3,155 @@
 import pandas as pd
 import matplotlib
 import seaborn as sns
+import uuid
+from pandas import DataFrame
 
 import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
+from matplotlib import font_manager
+from matplotlib.font_manager import FontManager
+matplotlib.use("Agg")
 import time
 from fsspec import filesystem
-import  spatial
+import spatial
 
 from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader
 
 
+def data_pre_classification(df: DataFrame):
+    ## Data pre-classification
+    columns = df.columns.tolist()
+
+
+    number_columns = []
+    non_numeric_colums = []
+
+    # 收集数据分类小于10个的列
+    non_numeric_colums_value_map = {}
+    numeric_colums_value_map = {}
+    for column_name in columns:
+
+        if pd.to_numeric(df[column_name], errors='coerce').notna().all():
+            number_columns.append(column_name)
+            unique_values = df[column_name].unique()
+            numeric_colums_value_map.update({column_name: len(unique_values)})
+        else:
+            non_numeric_colums.append(column_name)
+            unique_values = df[column_name].unique()
+            non_numeric_colums_value_map.update({column_name: len(unique_values)})
+
+
+    if len(non_numeric_colums) <=0:
+        sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
+        numeric_colums_sort_list = list(sorted_colums_value_map.keys())
+        x_column = number_columns[0]
+        hue_column = numeric_colums_sort_list[0]
+        y_column = numeric_colums_sort_list[1]
+    elif len(number_columns) <=0:
+        raise ValueError("Have No numeric Column！")
+    else:
+        # 数字和非数字都存在多列，放弃部分数字列
+        y_column = number_columns[0]
+        x_column = non_numeric_colums[0]
+        # if len(non_numeric_colums) > 1:
+        #
+        # else:
+
+        # non_numeric_colums_sort_list.remove(non_numeric_colums[0])
+        # hue_column = non_numeric_colums_sort_list
+    return x_column, y_column, hue_column
+
 if __name__ == "__main__":
     # connect = duckdb.connect("/Users/tuyang.yhj/Downloads/example.xlsx")
+    #
+
+    # fonts = fm.findSystemFonts()
+    # for font in fonts:
+    #     if 'Hei' in font:
+    #         print(font)
+
+    # fm = FontManager()
+    # mat_fonts = set(f.name for f in fm.ttflist)
+    # for i in mat_fonts:
+    #     print(i)
+    # print(len(mat_fonts))
+    # 获取系统中的默认中文字体名称
+    # default_font = fm.fontManager.defaultFontProperties.get_family()
+
+
     #
     excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/example.xlsx")
+    #
+    # # colunms, datas = excel_reader.run( "SELECT CONCAT(Year, '-', Quarter) AS QuarterYear, SUM(Sales) AS TotalSales FROM example GROUP BY QuarterYear ORDER BY QuarterYear")
+    # # colunms, datas = excel_reader.run( """ SELECT Year, SUM(Sales) AS Total_Sales FROM example GROUP BY Year ORDER BY Year; """)
+    df = excel_reader.get_df_by_sql_ex(""" SELECT Segment, Country, SUM(Sales) AS Total_Sales, SUM(Profit) AS Total_Profit FROM example GROUP BY Segment, Country """)
+
+    x,y,hue =data_pre_classification(df)
+    print(x, y, hue)
 
-    # colunms, datas = excel_reader.run( "SELECT CONCAT(Year, '-', Quarter) AS QuarterYear, SUM(Sales) AS TotalSales FROM example GROUP BY QuarterYear ORDER BY QuarterYear")
-    colunms, datas = excel_reader.run( """ SELECT Year, SUM(Sales) AS Total_Sales FROM example GROUP BY Year ORDER BY Year; """)
-    df = excel_reader.get_df_by_sql_ex("SELECT Country, SUM(Profit) AS Total_Profit FROM example GROUP BY Country;")
     columns = df.columns.tolist()
-    plt.rcParams["font.family"] = ["sans-serif"]
-    rc = {"font.sans-serif": "SimHei", "axes.unicode_minus": False}
-    sns.set_style(rc={'font.sans-serif': "Microsoft Yahei"})
-    sns.set(context="notebook", style="ticks", color_codes=True, rc=rc)
+    font_names = ['Heiti TC', 'Songti SC', 'STHeiti Light', 'Microsoft YaHei', 'SimSun', 'SimHei', 'KaiTi']
+    fm = FontManager()
+    mat_fonts = set(f.name for f in fm.ttflist)
+    can_use_fonts = []
+    for font_name in font_names:
+        if font_name in mat_fonts:
+            can_use_fonts.append(font_name)
+    if len(can_use_fonts) > 0:
+        plt.rcParams['font.sans-serif'] = can_use_fonts
+
+    rc = {'font.sans-serif': can_use_fonts}
+    plt.rcParams['axes.unicode_minus'] = False  # 解决无法显示符号的问题
+    sns.set(font='Heiti TC', font_scale=0.8)  # 解决Seaborn中文显示问题
     sns.set_palette("Set3")  # 设置颜色主题
+    sns.set_style("dark")
+    sns.color_palette("hls", 10)
+    sns.hls_palette(8, l=.5, s=.7)
+    sns.set(context='notebook', style='ticks', rc=rc)
+    # sns.set_palette("Set3")  # 设置颜色主题
+    # sns.set_style("dark")
+    # sns.color_palette("hls", 10)
+    # sns.hls_palette(8, l=.5, s=.7)
+    # sns.set(context='notebook', style='ticks', rc=rc)
 
-    # fig, ax = plt.pie(df[columns[1]], labels=df[columns[0]], autopct='%1.1f%%', startangle=90)
     fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
-    plt.subplots_adjust(top=0.9)
-    ax = df.plot(kind='pie', y=columns[1], ax=ax, labels=df[columns[0]].values, startangle=90, autopct='%1.1f%%')
-    # 手动设置 labels 的位置和大小
-    ax.legend(loc='center left', bbox_to_anchor=(-1, 0.5, 0,0), labels=None, fontsize=10)
-    plt.axis('equal')  # 使饼图为正圆形
-    plt.show()
+    # plt.ticklabel_format(style='plain')
+    # ax = df.plot(kind='bar', ax=ax)
+    # sns.barplot(df, x=x, y=y, hue= "Country", ax=ax)
+    sns.catplot(data=df, x=x, y=y, hue='Country',  kind='bar')
+    # 设置 y 轴刻度格式为普通数字格式
+    ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, _: '{:,.0f}'.format(x)))
+
+    # fonts = font_manager.findSystemFonts()
+    # font_path = ""
+    # for font in fonts:
+    #     if "Heiti" in font:
+    #         font_path = font
+    # my_font = font_manager.FontProperties(fname=font_path)
+    # plt.title("测试", fontproperties=my_font)
+    # plt.ylabel(columns[1], fontproperties=my_font)
+    # plt.xlabel(columns[0], fontproperties=my_font)
+
+
+
+    chart_name = "bar_" + str(uuid.uuid1()) + ".png"
+    chart_path = chart_name
+    plt.savefig(chart_path, bbox_inches='tight', dpi=100)
+
+
+
+    # sns.set(context="notebook", style="ticks", color_codes=True)
+    # sns.set_palette("Set3")  # 设置颜色主题
+    #
+    # # fig, ax = plt.pie(df[columns[1]], labels=df[columns[0]], autopct='%1.1f%%', startangle=90)
+    # fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
+    # plt.subplots_adjust(top=0.9)
+    # ax = df.plot(kind='pie', y=columns[1], ax=ax, labels=df[columns[0]].values, startangle=90, autopct='%1.1f%%')
+    # # 手动设置 labels 的位置和大小
+    # ax.legend(loc='center left', bbox_to_anchor=(-1, 0.5, 0,0), labels=None, fontsize=10)
+    # plt.axis('equal')  # 使饼图为正圆形
+    # plt.show()
+
     #
     #
     # def csv_colunm_foramt(val):