Revisioni di 使用 Streamlit 建立 YAML 員工資料管理系統

timmy ha revisionato questo gist 10 months ago. Vai alla revisione

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timmy ha revisionato questo gist 2 years ago. Vai alla revisione

1 file changed, 21 insertions, 21 deletions

edit_employee_data.py

			@@ -3,53 +3,53 @@ import pandas as pd
3	3		import yaml
4	4		from yaml.loader import SafeLoader
5	5
6		-	# Read YAML file
	6	+	# 讀取 YAML 檔案
7	7		def read_yaml(file_path):
8	8		with open(file_path, 'r') as file:
9	9		data = yaml.load(file, Loader=SafeLoader)
10	10		return data
11	11
12		-	# Write to YAML file
	12	+	# 寫入 YAML 檔案
13	13		def write_yaml(file_path, data):
14	14		with open(file_path, 'w') as file:
15	15		yaml.dump(data, file)
16	16
17		-	# Read data
	17	+	# 讀取資料
18	18		yaml_file = 'data.yaml'
19	19		data = read_yaml(yaml_file)
20	20
21		-	# Convert data to DataFrame
	21	+	# 轉換資料為 DataFrame
22	22		df = pd.DataFrame(data['employees'])
23	23
24		-	# Display DataFrame
25		-	st.write('## Employee Data Table')
	24	+	# 顯示 DataFrame
	25	+	st.write('## 員工資料表')
26	26		st.write(df)
27	27
28		-	# Allow user to edit data
29		-	st.write('## Edit Employee Data')
30		-	edited_df = st.data_editor(df)
	28	+	# 允許用戶編輯資料，並隱藏 City 欄位
	29	+	st.write('## 編輯員工資料')
	30	+	edited_df = st.data_editor(df.drop(columns=['City']))
31	31
32		-	# When user submits edits, write data back to YAML file
33		-	if st.button('Save Changes'):
	32	+	# 當用戶提交修改後，將資料寫回 YAML 檔案，但要保留 City 欄位
	33	+	if st.button('儲存修改'):
	34	+	edited_df['City'] = df['City'] # 將原始資料中的 City 欄位加回來
34	35		data['employees'] = edited_df.to_dict(orient='records')
35	36		write_yaml(yaml_file, data)
36		-	st.success('Changes saved successfully!')
	37	+	st.success('修改成功儲存！')
37	38
38		-	# Hide certain columns
	39	+	# 隱藏某些欄位
39	40		columns_to_hide = ['Salary']
40	41		df_visible = df.drop(columns=columns_to_hide)
41	42
42		-	# Display DataFrame with hidden columns
43		-	st.write('## Employee Data Table (Filtered)')
	43	+	# 顯示隱藏某些欄位後的 DataFrame
	44	+	st.write('## 員工資料表 (過濾後)')
44	45		st.write(df_visible)
45	46
46		-	# Interaction: Slider
47		-	age_filter = st.slider('Select age range', 20, 30, (20, 30))
	47	+	# 互動元素：滑桿
	48	+	age_filter = st.slider('選擇年齡範圍', 20, 30, (20, 30))
48	49
49		-	# Filter DataFrame
	50	+	# 篩選 DataFrame
50	51		filtered_df = df_visible[(df_visible['Age'] >= age_filter[0]) & (df_visible['Age'] <= age_filter[1])]
51	52
52		-	# Display filtered DataFrame
53		-	st.write('## Filtered Employee Data Table')
	53	+	# 顯示篩選後的 DataFrame
	54	+	st.write('## 過濾後的員工資料表')
54	55		st.write(filtered_df)
55		-

timmy ha revisionato questo gist 2 years ago. Vai alla revisione

2 files changed, 71 insertions

data.yaml(file creato)

		@@ -0,0 +1,16 @@
1	+	employees:
2	+	- Age: 34
3	+	City: New York
4	+	Name: Alice
5	+	Occupation: Engineer
6	+	Salary: 70000
7	+	- Age: 27
8	+	City: Los Angeles
9	+	Name: Bob
10	+	Occupation: Doctor
11	+	Salary: 120000
12	+	- Age: 22
13	+	City: Chicago
14	+	Name: Charlie
15	+	Occupation: Artist
16	+	Salary: 50000

edit_employee_data.py(file creato)

		@@ -0,0 +1,55 @@
1	+	import streamlit as st
2	+	import pandas as pd
3	+	import yaml
4	+	from yaml.loader import SafeLoader
5	+
6	+	# Read YAML file
7	+	def read_yaml(file_path):
8	+	with open(file_path, 'r') as file:
9	+	data = yaml.load(file, Loader=SafeLoader)
10	+	return data
11	+
12	+	# Write to YAML file
13	+	def write_yaml(file_path, data):
14	+	with open(file_path, 'w') as file:
15	+	yaml.dump(data, file)
16	+
17	+	# Read data
18	+	yaml_file = 'data.yaml'
19	+	data = read_yaml(yaml_file)
20	+
21	+	# Convert data to DataFrame
22	+	df = pd.DataFrame(data['employees'])
23	+
24	+	# Display DataFrame
25	+	st.write('## Employee Data Table')
26	+	st.write(df)
27	+
28	+	# Allow user to edit data
29	+	st.write('## Edit Employee Data')
30	+	edited_df = st.data_editor(df)
31	+
32	+	# When user submits edits, write data back to YAML file
33	+	if st.button('Save Changes'):
34	+	data['employees'] = edited_df.to_dict(orient='records')
35	+	write_yaml(yaml_file, data)
36	+	st.success('Changes saved successfully!')
37	+
38	+	# Hide certain columns
39	+	columns_to_hide = ['Salary']
40	+	df_visible = df.drop(columns=columns_to_hide)
41	+
42	+	# Display DataFrame with hidden columns
43	+	st.write('## Employee Data Table (Filtered)')
44	+	st.write(df_visible)
45	+
46	+	# Interaction: Slider
47	+	age_filter = st.slider('Select age range', 20, 30, (20, 30))
48	+
49	+	# Filter DataFrame
50	+	filtered_df = df_visible[(df_visible['Age'] >= age_filter[0]) & (df_visible['Age'] <= age_filter[1])]
51	+
52	+	# Display filtered DataFrame
53	+	st.write('## Filtered Employee Data Table')
54	+	st.write(filtered_df)
55	+

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