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文章目录
- 14.4 USDA Food Database（美国农业部食品数据库）
14.4 USDA Food Database（美国农业部食品数据库）
这个数据是关于食物营养成分的。存储格式是JSON，看起来像这样：
{"id": 21441, "description": "KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY, Wing, meat and skin with breading", "tags": ["KFC"], "manufacturer": "Kentucky Fried Chicken", "group": "Fast Foods", "portions": [ { "amount": 1, "unit": "wing, with skin", "grams": 68.0}...],"nutrients": [ { "value": 20.8, "units": "g", "description": "Protein", "group": "Composition" },...]
}
每种食物都有一系列特征，其中有两个list，protions和nutrients。我们必须把这样的数据进行处理，方便之后的分析。
这里使用python内建的json模块：
import pandas as pd
import numpy as np
import json
pd.options.display.max_rows = 10
db = json.load(open('../datasets/usda_food/database.json'))
len(db)
6636
db[0].keys()
dict_keys(['manufacturer', 'description', 'group', 'id', 'tags', 'nutrients', 'portions'])
db[0]['nutrients'][0]
{'description': 'Protein','group': 'Composition','units': 'g','value': 25.18}
nutrients = pd.DataFrame(db[0]['nutrients'])
nutrients
| description | group | units | value | |
|---|---|---|---|---|
| 0 | Protein | Composition | g | 25.180 |
| 1 | Total lipid (fat) | Composition | g | 29.200 |
| 2 | Carbohydrate, by difference | Composition | g | 3.060 |
| 3 | Ash | Other | g | 3.280 |
| 4 | Energy | Energy | kcal | 376.000 |
| ... | ... | ... | ... | ... |
| 157 | Serine | Amino Acids | g | 1.472 |
| 158 | Cholesterol | Other | mg | 93.000 |
| 159 | Fatty acids, total saturated | Other | g | 18.584 |
| 160 | Fatty acids, total monounsaturated | Other | g | 8.275 |
| 161 | Fatty acids, total polyunsaturated | Other | g | 0.830 |
162 rows × 4 columns
当把由字典组成的list转换为DataFrame的时候，我们可以吹创业提取的list部分。这里我们提取食品名，群（group），ID，制造商：
info_keys = ['description', 'group', 'id', 'manufacturer']
info = pd.DataFrame(db, columns=info_keys)
info[:5]
| description | group | id | manufacturer | |
|---|---|---|---|---|
| 0 | Cheese, caraway | Dairy and Egg Products | 1008 | |
| 1 | Cheese, cheddar | Dairy and Egg Products | 1009 | |
| 2 | Cheese, edam | Dairy and Egg Products | 1018 | |
| 3 | Cheese, feta | Dairy and Egg Products | 1019 | |
| 4 | Cheese, mozzarella, part skim milk | Dairy and Egg Products | 1028 |
info.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6636 entries, 0 to 6635
Data columns (total 4 columns):
description 6636 non-null object
group 6636 non-null object
id 6636 non-null int64
manufacturer 5195 non-null object
dtypes: int64(1), object(3)
memory usage: 207.5+ KB
我们可以看到食物群的分布，使用value_counts:
pd.value_counts(info.group)[:10]
Vegetables and Vegetable Products 812
Beef Products 618
Baked Products 496
Breakfast Cereals 403
Legumes and Legume Products 365
Fast Foods 365
Lamb, Veal, and Game Products 345
Sweets 341
Pork Products 328
Fruits and Fruit Juices 328
Name: group, dtype: int64
这里我们对所有的nutrient数据做一些分析，把每种食物的nutrient部分组合成一个大表格。首先，把每个食物的nutrient列表变为DataFrame，添加一列为id，然后把id添加到DataFrame中，接着使用concat联结到一起：
# 先创建一个空DataFrame用来保存最后的结果
# 这部分代码运行时间较长，请耐心等待
nutrients_all = pd.DataFrame()for food in db:nutrients = pd.DataFrame(food['nutrients'])nutrients['id'] = food['id']nutrients_all = nutrients_all.append(nutrients, ignore_index=True)
译者：虽然作者在书中说了用concat联结在一起，但我实际测试后，这个concat的方法非常耗时，用时几乎是append方法的两倍，所以上面的代码中使用了append方法。
一切正常的话出来的效果是这样的：
nutrients_all
| description | group | units | value | id | |
|---|---|---|---|---|---|
| 0 | Protein | Composition | g | 25.180 | 1008 |
| 1 | Total lipid (fat) | Composition | g | 29.200 | 1008 |
| 2 | Carbohydrate, by difference | Composition | g | 3.060 | 1008 |
| 3 | Ash | Other | g | 3.280 | 1008 |
| 4 | Energy | Energy | kcal | 376.000 | 1008 |
| ... | ... | ... | ... | ... | ... |
| 389350 | Vitamin B-12, added | Vitamins | mcg | 0.000 | 43546 |
| 389351 | Cholesterol | Other | mg | 0.000 | 43546 |
| 389352 | Fatty acids, total saturated | Other | g | 0.072 | 43546 |
| 389353 | Fatty acids, total monounsaturated | Other | g | 0.028 | 43546 |
| 389354 | Fatty acids, total polyunsaturated | Other | g | 0.041 | 43546 |
389355 rows × 5 columns
这个DataFrame中有一些重复的部分，看一下有多少重复的行：
nutrients_all.duplicated().sum() # number of duplicates
14179
把重复的部分去掉：
nutrients_all = nutrients_all.drop_duplicates()
nutrients_all
| description | group | units | value | id | |
|---|---|---|---|---|---|
| 0 | Protein | Composition | g | 25.180 | 1008 |
| 1 | Total lipid (fat) | Composition | g | 29.200 | 1008 |
| 2 | Carbohydrate, by difference | Composition | g | 3.060 | 1008 |
| 3 | Ash | Other | g | 3.280 | 1008 |
| 4 | Energy | Energy | kcal | 376.000 | 1008 |
| ... | ... | ... | ... | ... | ... |
| 389350 | Vitamin B-12, added | Vitamins | mcg | 0.000 | 43546 |
| 389351 | Cholesterol | Other | mg | 0.000 | 43546 |
| 389352 | Fatty acids, total saturated | Other | g | 0.072 | 43546 |
| 389353 | Fatty acids, total monounsaturated | Other | g | 0.028 | 43546 |
| 389354 | Fatty acids, total polyunsaturated | Other | g | 0.041 | 43546 |
375176 rows × 5 columns
为了与info_keys中的group和descripton区别开，我们把列名更改一下：
col_mapping = {'description': 'food','group': 'fgroup'}
info = info.rename(columns=col_mapping, copy=False)
info.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6636 entries, 0 to 6635
Data columns (total 4 columns):
food 6636 non-null object
fgroup 6636 non-null object
id 6636 non-null int64
manufacturer 5195 non-null object
dtypes: int64(1), object(3)
memory usage: 207.5+ KB
col_mapping = {'description' : 'nutrient','group': 'nutgroup'}
nutrients_all = nutrients_all.rename(columns=col_mapping, copy=False)
nutrients_all
| nutrient | nutgroup | units | value | id | |
|---|---|---|---|---|---|
| 0 | Protein | Composition | g | 25.180 | 1008 |
| 1 | Total lipid (fat) | Composition | g | 29.200 | 1008 |
| 2 | Carbohydrate, by difference | Composition | g | 3.060 | 1008 |
| 3 | Ash | Other | g | 3.280 | 1008 |
| 4 | Energy | Energy | kcal | 376.000 | 1008 |
| ... | ... | ... | ... | ... | ... |
| 389350 | Vitamin B-12, added | Vitamins | mcg | 0.000 | 43546 |
| 389351 | Cholesterol | Other | mg | 0.000 | 43546 |
| 389352 | Fatty acids, total saturated | Other | g | 0.072 | 43546 |
| 389353 | Fatty acids, total monounsaturated | Other | g | 0.028 | 43546 |
| 389354 | Fatty acids, total polyunsaturated | Other | g | 0.041 | 43546 |
375176 rows × 5 columns
上面所有步骤结束后，我们可以把info和nutrients_all合并（merge）：
ndata = pd.merge(nutrients_all, info, on='id', how='outer')
ndata.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 375176 entries, 0 to 375175
Data columns (total 8 columns):
nutrient 375176 non-null object
nutgroup 375176 non-null object
units 375176 non-null object
value 375176 non-null float64
id 375176 non-null int64
food 375176 non-null object
fgroup 375176 non-null object
manufacturer 293054 non-null object
dtypes: float64(1), int64(1), object(6)
memory usage: 25.8+ MB
ndata.iloc[30000]
nutrient Glycine
nutgroup Amino Acids
units g
value 0.04
id 6158
food Soup, tomato bisque, canned, condensed
fgroup Soups, Sauces, and Gravies
manufacturer
Name: 30000, dtype: object
我们可以对食物群（food group）和营养类型（nutrient type）分组后，对中位数进行绘图：
result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)
%matplotlib inline
result['Zinc, Zn'].sort_values().plot(kind='barh', figsize=(10, 8))
我们还可以找到每一种营养成分含量最多的食物是什么：
by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])get_maximum = lambda x: x.loc[x.value.idxmax()]
get_minimum = lambda x: x.loc[x.value.idxmin()]max_foods = by_nutrient.apply(get_maximum)[['value', 'food']]# make the food a little smaller
max_foods.food = max_foods.food.str[:50]
因为得到的DataFrame太大，这里只输出'Amino Acids'(氨基酸)的营养群（nutrient group）:
max_foods.loc['Amino Acids']['food']
nutrient
Alanine Gelatins, dry powder, unsweetened
Arginine Seeds, sesame flour, low-fat
Aspartic acid Soy protein isolate
Cystine Seeds, cottonseed flour, low fat (glandless)
Glutamic acid Soy protein isolate...
Serine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Threonine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Tryptophan Sea lion, Steller, meat with fat (Alaska Native)
Tyrosine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Valine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Name: food, Length: 19, dtype: object
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