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feat(editor): ChatExcel
🔥ChatExcel Mode Operation Manual
This commit is contained in:
yhjun1026
parent
91225e8b25
commit
c03825b2c6
@ -4,7 +4,6 @@ from pilot.commands.command_mange import command
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from pilot.configs.config import Config
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import pandas as pd
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import uuid
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import io
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import os
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import matplotlib
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import seaborn as sns
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@ -20,6 +19,53 @@ CFG = Config()
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logger = build_logger("show_chart_gen", LOGDIR + "show_chart_gen.log")
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static_message_img_path = os.path.join(os.getcwd(), "message/img")
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def data_pre_classification(df: DataFrame):
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## Data pre-classification
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columns = df.columns.tolist()
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number_columns = []
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non_numeric_colums = []
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# 收集数据分类小于10个的列
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non_numeric_colums_value_map = {}
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numeric_colums_value_map = {}
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for column_name in columns:
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if pd.api.types.is_numeric_dtype(df[column_name].dtypes):
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number_columns.append(column_name)
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unique_values = df[column_name].unique()
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numeric_colums_value_map.update({column_name: len(unique_values)})
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else:
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non_numeric_colums.append(column_name)
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unique_values = df[column_name].unique()
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non_numeric_colums_value_map.update({column_name: len(unique_values)})
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sorted_numeric_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
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numeric_colums_sort_list = list(sorted_numeric_colums_value_map.keys())
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sorted_colums_value_map = dict(sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1]))
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non_numeric_colums_sort_list = list(sorted_colums_value_map.keys())
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# Analyze x-coordinate
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if len(non_numeric_colums_sort_list) > 0:
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x_cloumn = non_numeric_colums_sort_list[-1]
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non_numeric_colums_sort_list.remove(x_cloumn)
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else:
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x_cloumn = number_columns[0]
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numeric_colums_sort_list.remove(x_cloumn)
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# Analyze y-coordinate
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if len(numeric_colums_sort_list) > 0:
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y_column = numeric_colums_sort_list[0]
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numeric_colums_sort_list.remove(y_column)
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else:
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raise ValueError("Not enough numeric columns for chart！")
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return x_cloumn, y_column, non_numeric_colums_sort_list, numeric_colums_sort_list
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def zh_font_set():
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font_names = ['Heiti TC', 'Songti SC', 'STHeiti Light', 'Microsoft YaHei', 'SimSun', 'SimHei', 'KaiTi']
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fm = FontManager()
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@ -36,9 +82,6 @@ def zh_font_set():
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'"speak": "<speak>", "df":"<data frame>"')
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def response_line_chart(speak: str, df: DataFrame) -> str:
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logger.info(f"response_line_chart:{speak},")
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columns = df.columns.tolist()
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if df.size <= 0:
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raise ValueError("No Data！")
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@ -65,7 +108,14 @@ def response_line_chart(speak: str, df: DataFrame) -> str:
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sns.set(context='notebook', style='ticks', rc=rc)
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fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
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sns.lineplot(df, x=columns[0], y=columns[1], ax=ax)
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x,y, non_num_columns, num_colmns =data_pre_classification(df)
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# ## 复杂折线图实现
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if len(num_colmns)>0:
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num_colmns.append(y)
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df_melted = pd.melt(df, id_vars=x, value_vars=num_colmns, var_name='line', value_name='Value')
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sns.lineplot(data=df_melted, x=x, y="Value", hue="line", ax=ax, palette="Set2")
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else:
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sns.lineplot(data=df, x=x, y=y, ax=ax, palette="Set2")
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chart_name = "line_" + str(uuid.uuid1()) + ".png"
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chart_path = static_message_img_path + "/" + chart_name
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@ -79,7 +129,6 @@ def response_line_chart(speak: str, df: DataFrame) -> str:
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'"speak": "<speak>", "df":"<data frame>"')
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def response_bar_chart(speak: str, df: DataFrame) -> str:
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logger.info(f"response_bar_chart:{speak},")
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columns = df.columns.tolist()
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if df.size <= 0:
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raise ValueError("No Data！")
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@ -105,9 +154,34 @@ def response_bar_chart(speak: str, df: DataFrame) -> str:
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sns.set(context='notebook', style='ticks', rc=rc)
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fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
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sns.barplot(df, x=df[columns[0]], y=df[columns[1]], ax=ax)
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chart_name = "pie_" + str(uuid.uuid1()) + ".png"
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x,y, non_num_columns, num_colmns =data_pre_classification(df)
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if len(non_num_columns) >= 1:
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hue = non_num_columns[0]
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if len(num_colmns)>=1:
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if hue:
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if len(num_colmns) >= 2:
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can_use_columns = num_colmns[:2]
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else:
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can_use_columns = num_colmns
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sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
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for sub_y_column in can_use_columns:
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sns.barplot(data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax)
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else:
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if len(num_colmns) >= 3:
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can_use_columns = num_colmns[:3]
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else:
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can_use_columns = num_colmns
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sns.barplot(data=df, x=x, y=y, hue=can_use_columns[0], palette="Set2", ax=ax)
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for sub_y_column in can_use_columns[1:]:
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sns.barplot(data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax)
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else:
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sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
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chart_name = "bar_" + str(uuid.uuid1()) + ".png"
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chart_path = static_message_img_path + "/" + chart_name
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plt.savefig(chart_path, bbox_inches='tight', dpi=100)
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html_img = f"""<h5>{speak}</h5><img style='max-width: 100%; max-height: 70%;' src="/images/{chart_name}" />"""
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Binary file not shown.
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Before Width: | Height: | Size: 19 KiB |
Binary file not shown.
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Before Width: | Height: | Size: 18 KiB |
@ -1,9 +1,11 @@
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import os
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import duckdb
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import pandas as pd
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import numpy as np
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import matplotlib
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import seaborn as sns
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import uuid
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from pandas import DataFrame
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import matplotlib.pyplot as plt
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@ -29,9 +31,12 @@ def data_pre_classification(df: DataFrame):
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# 收集数据分类小于10个的列
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non_numeric_colums_value_map = {}
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numeric_colums_value_map = {}
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df_filtered = df.dropna()
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for column_name in columns:
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if pd.to_numeric(df[column_name], errors='coerce').notna().all():
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print(np.issubdtype(df_filtered[column_name].dtype, np.number))
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# if pd.to_numeric(df[column_name], errors='coerce').notna().all():
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# if np.issubdtype(df_filtered[column_name].dtype, np.number):
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if pd.api.types.is_numeric_dtype(df[column_name].dtypes):
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number_columns.append(column_name)
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unique_values = df[column_name].unique()
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numeric_colums_value_map.update({column_name: len(unique_values)})
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@ -40,26 +45,65 @@ def data_pre_classification(df: DataFrame):
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unique_values = df[column_name].unique()
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non_numeric_colums_value_map.update({column_name: len(unique_values)})
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sorted_numeric_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
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numeric_colums_sort_list = list(sorted_numeric_colums_value_map.keys())
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if len(non_numeric_colums) <=0:
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sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
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numeric_colums_sort_list = list(sorted_colums_value_map.keys())
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x_column = number_columns[0]
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hue_column = numeric_colums_sort_list[0]
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y_column = numeric_colums_sort_list[1]
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elif len(number_columns) <=0:
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raise ValueError("Have No numeric Column！")
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sorted_colums_value_map = dict(sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1]))
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non_numeric_colums_sort_list = list(sorted_colums_value_map.keys())
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# Analyze x-coordinate
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if len(non_numeric_colums_sort_list) > 0:
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x_cloumn = non_numeric_colums_sort_list[-1]
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non_numeric_colums_sort_list.remove(x_cloumn)
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else:
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# 数字和非数字都存在多列，放弃部分数字列
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y_column = number_columns[0]
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x_column = non_numeric_colums[0]
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x_cloumn = number_columns[0]
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numeric_colums_sort_list.remove(x_cloumn)
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# Analyze y-coordinate
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if len(numeric_colums_sort_list) > 0:
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y_column = numeric_colums_sort_list[0]
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numeric_colums_sort_list.remove(y_column)
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else:
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raise ValueError("Not enough numeric columns for chart！")
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return x_cloumn, y_column, non_numeric_colums_sort_list, numeric_colums_sort_list
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#
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# if len(non_numeric_colums) <=0:
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# sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
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# numeric_colums_sort_list = list(sorted_colums_value_map.keys())
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# x_column = number_columns[0]
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# hue_column = numeric_colums_sort_list[0]
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# y_column = numeric_colums_sort_list[1]
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# cols = numeric_colums_sort_list[2:]
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# elif len(number_columns) <=0:
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# raise ValueError("Have No numeric Column！")
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# else:
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# # 数字和非数字都存在多列，放弃部分数字列
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# x_column = non_numeric_colums[0]
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# y_column = number_columns[0]
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# if len(non_numeric_colums) > 1:
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# sorted_colums_value_map = dict(sorted(non_numeric_colums_value_map.items(), key=lambda x: x[1]))
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# non_numeric_colums_sort_list = list(sorted_colums_value_map.keys())
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# non_numeric_colums_sort_list.remove(non_numeric_colums[0])
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# hue_column = non_numeric_colums_sort_list[0]
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# if len(number_columns) > 1:
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# # try multiple charts
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# cols = number_columns.remove( number_columns[0])
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#
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# else:
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# non_numeric_colums_sort_list.remove(non_numeric_colums[0])
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# hue_column = non_numeric_colums_sort_list
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return x_column, y_column, hue_column
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# sorted_colums_value_map = dict(sorted(numeric_colums_value_map.items(), key=lambda x: x[1]))
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# numeric_colums_sort_list = list(sorted_colums_value_map.keys())
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# numeric_colums_sort_list.remove(number_columns[0])
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# if sorted_colums_value_map[numeric_colums_sort_list[0]].value < 5:
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# hue_column = numeric_colums_sort_list[0]
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# if len(number_columns) > 2:
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# # try multiple charts
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# cols = numeric_colums_sort_list.remove(numeric_colums_sort_list[0])
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#
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# print(x_column, y_column, hue_column, cols)
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# return x_column, y_column, hue_column
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if __name__ == "__main__":
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# connect = duckdb.connect("/Users/tuyang.yhj/Downloads/example.xlsx")
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@ -78,16 +122,35 @@ if __name__ == "__main__":
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# 获取系统中的默认中文字体名称
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# default_font = fm.fontManager.defaultFontProperties.get_family()
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# 创建一个示例 DataFrame
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df = pd.DataFrame({
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'A': [1, 2, 3, None, 5],
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'B': [10, 20, 30, 40, 50],
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'C': [1.1, 2.2, None, 4.4, 5.5],
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'D': ['a', 'b', 'c', 'd', 'e']
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})
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# 判断列是否为数字列
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column_name = 'A' # 要判断的列名
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is_numeric = pd.to_numeric(df[column_name], errors='coerce').notna().all()
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if is_numeric:
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print(f"Column '{column_name}' is a numeric column (ignoring null and NaN values in some elements).")
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else:
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print(f"Column '{column_name}' is not a numeric column (ignoring null and NaN values in some elements).")
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#
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excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/example.xlsx")
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# excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/example.xlsx")
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excel_reader = ExcelReader("/Users/tuyang.yhj/Downloads/yhj-zx.csv")
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#
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# # colunms, datas = excel_reader.run( "SELECT CONCAT(Year, '-', Quarter) AS QuarterYear, SUM(Sales) AS TotalSales FROM example GROUP BY QuarterYear ORDER BY QuarterYear")
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# # colunms, datas = excel_reader.run( """ SELECT Year, SUM(Sales) AS Total_Sales FROM example GROUP BY Year ORDER BY Year; """)
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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 """)
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# 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 """)
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df = excel_reader.get_df_by_sql_ex(""" SELECT `明细`, `费用小计`, `支出小计` FROM yhj-zx limit 10""")
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x,y,hue =data_pre_classification(df)
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print(x, y, hue)
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for column_name in df.columns.tolist():
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print(column_name + ":"+ str(df[column_name].dtypes))
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print(column_name + ":"+ str(pd.api.types.is_numeric_dtype(df[column_name].dtypes)))
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columns = df.columns.tolist()
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font_names = ['Heiti TC', 'Songti SC', 'STHeiti Light', 'Microsoft YaHei', 'SimSun', 'SimHei', 'KaiTi']
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@ -108,30 +171,63 @@ if __name__ == "__main__":
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sns.color_palette("hls", 10)
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sns.hls_palette(8, l=.5, s=.7)
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sns.set(context='notebook', style='ticks', rc=rc)
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# sns.set_palette("Set3") # 设置颜色主题
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# sns.set_style("dark")
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# sns.color_palette("hls", 10)
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# sns.hls_palette(8, l=.5, s=.7)
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# sns.set(context='notebook', style='ticks', rc=rc)
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fig, ax = plt.subplots(figsize=(8, 5), dpi=100)
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# plt.ticklabel_format(style='plain')
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# ax = df.plot(kind='bar', ax=ax)
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# sns.barplot(df, x=x, y=y, hue= "Country", ax=ax)
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sns.catplot(data=df, x=x, y=y, hue='Country', kind='bar')
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# sns.barplot(df, x=x, y="Total_Sales", hue='Country', ax=ax)
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# sns.barplot(df, x=x, y="Total_Profit", hue='Country', ax=ax)
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# sns.catplot(data=df, x=x, y=y, hue='Country', kind='bar')
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x,y, non_num_columns, num_colmns =data_pre_classification(df)
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print(x, y, str(non_num_columns), str(num_colmns))
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## 复杂折线图实现
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if len(num_colmns)>0:
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num_colmns.append(y)
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df_melted = pd.melt(df, id_vars=x, value_vars=num_colmns, var_name='line', value_name='Value')
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sns.lineplot(data=df_melted, x=x, y="Value", hue="line", ax=ax, palette="Set2")
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else:
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sns.lineplot(data=df, x=x, y=y, ax=ax, palette="Set2")
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# hue = None
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# ## 复杂柱状图实现
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# x,y, non_num_columns, num_colmns =data_pre_classification(df)
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# if len(non_num_columns) >= 1:
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# hue = non_num_columns[0]
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# if len(num_colmns)>=1:
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# if hue:
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# if len(num_colmns) >= 2:
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# can_use_columns = num_colmns[:2]
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# else:
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# can_use_columns = num_colmns
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# sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
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# for sub_y_column in can_use_columns:
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# sns.barplot(data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax)
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# else:
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# if len(num_colmns) >= 3:
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# can_use_columns = num_colmns[:3]
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# else:
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# can_use_columns = num_colmns
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# sns.barplot(data=df, x=x, y=y, hue=can_use_columns[0], palette="Set2", ax=ax)
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#
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# for sub_y_column in can_use_columns[1:]:
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# sns.barplot(data=df, x=x, y=sub_y_column, hue=hue, palette="Set2", ax=ax)
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# else:
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# sns.barplot(data=df, x=x, y=y, hue=hue, palette="Set2", ax=ax)
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# # 转换 DataFrame 格式
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# df_melted = pd.melt(df, id_vars=x, value_vars=['Total_Sales', 'Total_Profit'], var_name='line', value_name='y')
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#
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# # 绘制多列柱状图
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#
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# sns.barplot(data=df, x=x, y="Total_Sales", hue = "Country", palette="Set2", ax=ax)
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# sns.barplot(data=df, x=x, y="Total_Profit", hue = "Country", palette="Set1", ax=ax)
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# 设置 y 轴刻度格式为普通数字格式
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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"
|
||||
@ -139,89 +235,4 @@ if __name__ == "__main__":
|
||||
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):
|
||||
# if str(val).find("$") >= 0:
|
||||
# return float(val.replace('$', '').replace(',', ''))
|
||||
# if str(val).find("¥") >= 0:
|
||||
# return float(val.replace('¥', '').replace(',', ''))
|
||||
# return val
|
||||
#
|
||||
# # 获取当前时间戳，作为代码开始的时间
|
||||
# start_time = int(time.time() * 1000)
|
||||
#
|
||||
# df = pd.read_excel('/Users/tuyang.yhj/Downloads/example.xlsx')
|
||||
# # 读取 Excel 文件为 Pandas DataFrame
|
||||
# df = pd.read_excel('/Users/tuyang.yhj/Downloads/example.xlsx', converters={i: csv_colunm_foramt for i in range(df.shape[1])})
|
||||
#
|
||||
# # d = df.values
|
||||
# # print(d.shape[0])
|
||||
# # for row in d:
|
||||
# # print(row[0])
|
||||
# # print(len(row))
|
||||
# # r = df.iterrows()
|
||||
#
|
||||
# # 获取当前时间戳，作为代码结束的时间
|
||||
# end_time = int(time.time() * 1000)
|
||||
#
|
||||
# print(f"耗时:{(end_time-start_time)/1000}秒")
|
||||
#
|
||||
# # 连接 DuckDB 数据库
|
||||
# con = duckdb.connect(database=':memory:', read_only=False)
|
||||
#
|
||||
# # 将 DataFrame 写入 DuckDB 数据库中的一个表
|
||||
# con.register('example', df)
|
||||
#
|
||||
# # 查询 DuckDB 数据库中的表
|
||||
# conn = con.cursor()
|
||||
# results = con.execute('SELECT Country, SUM(Profit) AS Total_Profit FROM example GROUP BY Country ORDER BY Total_Profit DESC LIMIT 1;')
|
||||
# colunms = []
|
||||
# for descrip in results.description:
|
||||
# colunms.append(descrip[0])
|
||||
# print(colunms)
|
||||
# for row in results.fetchall():
|
||||
# print(row)
|
||||
#
|
||||
#
|
||||
# # 连接 DuckDB 数据库
|
||||
# # con = duckdb.connect(':memory:')
|
||||
#
|
||||
# # # 加载 spatial 扩展
|
||||
# # con.execute('install spatial;')
|
||||
# # con.execute('load spatial;')
|
||||
# #
|
||||
# # # 查询 duckdb_internal 系统表，获取扩展列表
|
||||
# # result = con.execute("SELECT * FROM duckdb_internal.functions WHERE schema='list_extensions';")
|
||||
# #
|
||||
# # # 遍历查询结果，输出扩展名称和版本号
|
||||
# # for row in result:
|
||||
# # print(row['name'], row['return_type'])
|
||||
# # duckdb.read_csv('/Users/tuyang.yhj/Downloads/example_csc.csv')
|
||||
# # result = duckdb.sql('SELECT * FROM "/Users/tuyang.yhj/Downloads/yhj-zx.csv" ')
|
||||
# # result = duckdb.sql('SELECT * FROM "/Users/tuyang.yhj/Downloads/example_csc.csv" limit 20')
|
||||
# # for row in result.fetchall():
|
||||
# # print(row)
|
||||
#
|
||||
#
|
||||
# # result = con.execute("SELECT * FROM st_read('/Users/tuyang.yhj/Downloads/example.xlsx', layer='Sheet1')")
|
||||
# # # 遍历查询结果
|
||||
# # for row in result.fetchall():
|
||||
# # print(row)
|
||||
# print("xx")
|
||||
#
|
||||
#
|
||||
#
|
||||
|
||||
@ -62,7 +62,8 @@ class ExcelReader:
|
||||
for column_name in df_tmp.columns:
|
||||
self.columns_map.update({column_name: excel_colunm_format(column_name)})
|
||||
try:
|
||||
self.df[column_name] = self.df[column_name].astype(float)
|
||||
self.df[column_name] = pd.to_numeric(self.df[column_name])
|
||||
self.df[column_name] = self.df[column_name].fillna(0)
|
||||
except Exception as e:
|
||||
print("transfor column error！" + column_name)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user