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  • ki浅薄姿势了解到的是脂肪不同的形态会经过一系列灰常灰常灰常复杂的反应,被身体利用,代谢产物比如二氧化碳会经肺部排除体外、水会经皮肤以汗液的形式或者经肾脏以嘘嘘的形式排出体外、再有一些会经肝脏代谢排出体外。

  • print(food_info.columns)# 输出:输出全部的列名,而不是用省略号代替Index(['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)', 'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)', 'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)', 'Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)', 'Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)', 'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)', 'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg', 'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_Poly_(g)', 'Cholestrl_(mg)'], dtype='object')123

  • df.iloc[1] 获取第二行

  • pid) "','" str(level) "','" url "')"

  • print df.drop(df.columns[[1, 2]], axis = 1).head()# OUTPUT Abra Ifugao Kalinga0 1243 3300 105531 4158 8063 352572 1787 1074 45443 17152 19607 316874 1266 3315 8520123456789

  • two-tailed p-value 双侧概率值

  • pandas的基本数据结构来自: 冬之晓

  • https://blog.csdn.net/Mrzhangjwei/article/details/53509040

  • virtualenvwrapper 提供环境名字的tab补全功能。当您有很多环境, 并且很难记住它们的名字时,这就显得很有用。

  • 甘油糖酵解或有氧氧化供能,也可转变成糖脂肪酸与清蛋白结合转运入各组织经β-氧化供能。

  • print df.describe()# OUTPUT Abra Apayao Benguet Ifugao Kalingacount 79.000000 79.000000 79.000000 79.000000 79.000000mean 12874.379747 16860.645570 3237.392405 12414.620253 30446.417722std 16746.466945 15448.153794 1588.536429 5034.282019 22245.707692min 927.000000 401.000000 148.000000 1074.000000 2346.00000025% 1524.000000 3435.500000 2328.000000 8205.000000 8601.50000050% 5790.000000 10588.000000 3202.000000 13044.000000 24494.00000075% 13330.500000 33289.000000 3918.500000 16099.500000 52510.500000max 60303.000000 54625.000000 8813.000000 21031.000000 68663.000000123456789101112

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