# สร้าง DataFrame สำหรับแต่ละตารางที่พบ
 
# Table 1: Yield and fruit characteristics
table_1 = pd.DataFrame({
    "พารามิเตอร์": ["จำนวนผล/เสา", "ผลผลิต (กก./เสา)", "ผลผลิต (ตัน/เฮกตาร์)", "น้ำหนักผล (กรัม)", "น้ำหนักเปลือก (กรัม)", "น้ำหนักเนื้อ (กรัม)", "อัตราส่วนเนื้อ:เปลือก", "เปอร์เซ็นต์เปลือก", "เปอร์เซ็นต์เนื้อ"],
    "สีขาว": [51, 28.3, 31.6, 554, 152, 402, 2.70, 27.5, 72.5],
    "สีแดง": [52, 22.0, 24.6, 421, 110, 311, 2.89, 26.1, 73.9]
})
 
# Table 2: Sugar, organic acids, and phenolic acids profile
table_2 = pd.DataFrame({
    "พารามิเตอร์": ["กลูโคส (g)", "ฟรุกโตส (g)", "ซูโครส (mg)", "ซอร์บิทอล (mg)", "ไรโบส (mg)", "ไซโลส (mg)", "อาราบิโนส (mg)", "อิโนซิทอล (mg)", "แมนโนส (mg)"],
    "สีขาว": [1.58, 2.15, 2.12, 2.61, 101, 79.2, 144, 209, 266],
    "สีแดง": [2.05, 2.13, 1.42, 1.49, 121, 84.3, 157, 245, 269]
})
 
# Table 3: Amino acid profile
table_3 = pd.DataFrame({
    "พารามิเตอร์": ["ฮิสติดีน (mg/g โปรตีน)", "ลูซีน", "ไลซีน", "เมไทโอนีน", "ฟีนิลอะลานีน", "ทริปโตเฟน", "วาลีน"],
    "สีขาว": [32.4, 60.4, 63.1, 47.6, 220, 0.142, 33.3],
    "สีแดง": [31.8, 57.3, 70.4, 59.1, 213, 0.145, 29.8]
})
 
# Table 4: Vitamins and minerals profile
table_4 = pd.DataFrame({
    "พารามิเตอร์": ["วิตามินซี (mg)", "ไนอาซิน (µg)", "ไพริดอกซิน (µg)", "โพแทสเซียม (mg)", "แมกนีเซียม (mg)", "แคลเซียม (mg)", "เหล็ก (mg)"],
    "สีขาว": [5.64, 9.25, 0.589, 193, 45.9, 45.7, 0.877],
    "สีแดง": [4.71, 10.57, 0.520, 180, 33.6, 34.5, 1.554]
})
 
# บันทึกเป็นไฟล์ Excel
file_path = "/mnt/data/ตาราง_แก้วมังกร.xlsx"
with pd.ExcelWriter(file_path) as writer:
    table_1.to_excel(writer, sheet_name="Table 1", index=False)
    table_2.to_excel(writer, sheet_name="Table 2", index=False)
    table_3.to_excel(writer, sheet_name="Table 3", index=False)
    table_4.to_excel(writer, sheet_name="Table 4", index=False)
 
file_path
 

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