%pwd
u'/Users/zhongyaode'
import numpy as np
import pandas as pd
path='/Users/zhongyaode/'
import json
from pandas import Series,DataFrame
#加載數(shù)據(jù)
db = json.load(open('/Users/zhongyaode/pythonbook/ch07/foods-2011-10-03.json'))
len(db)
6636
#db每個條目中都是一個含有某種食物全部數(shù)據(jù)的字典， nutrients字段是一個字典列表，
#其中的每個字典對應(yīng)一種營養(yǎng)成分
db[0].keys()
[u'portions',
u'description',
u'tags',
u'nutrients',
u'group',
u'id',
u'manufacturer']
db[0]['nutrients'][0]
{u'description': u'Protein',
u'group': u'Composition',
u'units': u'g',
u'value': 25.18}
nutrients=DataFrame(db[0]['nutrients'])
nutrients[0:7]
#在將字典列表轉(zhuǎn)換為DataFrame時，可以只抽取其中的一部分字段，這里取出
#食物的名稱、分類、編號、以及制造商等信息
info_keys=['description','group','id','manufacturer']
info=DataFrame(db,columns=info_keys)
info[:5]
查看info的統(tǒng)計信息
info.describe()
#查看info字典的基本信息
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: 207.4+ KB
#通過value_counts查看食物類別的分布情況
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
Fruits and Fruit Juices 328
Pork Products 328
Name: group, dtype: int64
pd.value_counts(info.description)[:3]
Bread, pound cake type, pan de torta salvadoran 1
MISSION FOODS, MISSION Flour Tortillas, Soft Taco, 8 inch 1
Lamb, domestic, shoulder, arm, separable lean and fat, trimmed to 1/8 fat, cooked, broiled 1
Name: description, dtype: int64
#為了對全部營養(yǎng)數(shù)據(jù)做一些分析，最簡單的辦法是將所有食物的營養(yǎng)成分整合到一個大表中
#分幾步完成，首先，將各食物的營養(yǎng)成分列表轉(zhuǎn)換成為一個DateFrame,并添加一個個表示
#編號的列，然后將該DataFrame添加到一個列表中，最后通過concat將這些東西鏈接起來
nutrients=[]
for rec in db:
fnuts=DataFrame(rec['nutrients'])
fnuts['id']=rec['id']
nutrients.append(fnuts)
nutrients=pd.concat(nutrients,ignore_index=True)
nutrients.duplicated().sum()
14179
nutrients=nutrients.drop_duplicates()
nutrients.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 375176 entries, 0 to 389354
Data columns (total 5 columns):
description 375176 non-null object
group 375176 non-null object
units 375176 non-null object
value 375176 non-null float64
id 375176 non-null int64
dtypes: float64(1), int64(1), object(3)
memory usage: 17.2+ MB
#兩個DataFrame對象中都有'group'和'description',為了明確到底誰是誰
#對他們進行重命名
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: 207.4+ KB
col_mapping={'description':'nutrient',\
'group':'nutgroup'}
nutrients[:1]
nutrients=nutrients.rename(columns=col_mapping,copy=False)
#nutrients.info()
#nutrients=nutrients.rename(columns=col_maping,copy=False)
nutrients.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 375176 entries, 0 to 389354
Data columns (total 5 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
dtypes: float64(1), int64(1), object(3)
memory usage: 17.2+ MB
nutrients[:4]
info[0:2]
#將info 和nutrients合并
ndata=pd.merge(nutrients,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.ix[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
#根據(jù)營養(yǎng)分類得出的鋅中位值
result=ndata.groupby(['nutrient','fgroup'])['value'].quantile(0.5)
%pylab inline
b=result['Zinc, Zn'].order().plot(kind='barh')
Populating the interactive namespace from numpy and matplotlib
/Users/zhongyaode/anaconda/envs/py/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['info', 'rec']
`%matplotlib` prevents importing * from pylab and numpy
"\n`%matplotlib` prevents importing * from pylab and numpy"
/Users/zhongyaode/anaconda/envs/py/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: order is deprecated, use sort_values(...)
from ipykernel import kernelapp as app
output_40_2.png
#現(xiàn)在可知道，各營養(yǎng)成分最為豐富的食物是什么
by_nutrient=ndata.groupby(['nutgroup','nutrient'])
get_maximum=lambda x:x.xs(x.value.idxmax())
get_minimun=lambda x:x.xs(x.value.idxmin())
max_foods=by_nutrient.apply(get_maximum)[['value','food']]
#讓food小點
max_foods=max_foods.food.str[:50]
max_foods[:20]
nutgroup nutrient
Amino Acids 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
Glycine Gelatins, dry powder, unsweetened
Histidine Whale, beluga, meat, dried (Alaska Native)
Hydroxyproline KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...
Isoleucine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Leucine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Lysine Seal, bearded (Oogruk), meat, dried (Alaska Na...
Methionine Fish, cod, Atlantic, dried and salted
Phenylalanine Soy protein isolate, PROTEIN TECHNOLOGIES INTE...
Proline Gelatins, dry powder, unsweetened
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...
Composition Adjusted Protein Baking chocolate, unsweetened, squares
Name: food, dtype: object