pandas教程：USDA Food Database USDA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