《利用python进行数据分析》读书笔记--第七章 数据规整化：清理、转换、合并、重塑（三）
http://www.cnblogs.com/batteryhp/p/5046433.html
5、示例：usda食品数据库
下面是一个具体的例子，书中最重要的就是例子。
#-*- encoding: utf-8 -*-import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas import Series,DataFrame
import re
import json
#加载下面30M+的数据
db = json.load(open('E:\\foods-2011-10-03.json'))
#print len(db)
#print type(db) #得到的db是个list,每个条目都是含有某种食物全部数据的字典
#print db[0] #这一条非常长
#print db[0].keys()
#nutrients 是keys中的一个key，它对应的值是有关食物营养成分的一个字典列表，很长……
#print db[0]['nutrients'][0]
#下面将营养成分做成DataFrame
nutrients = DataFrame(db[0]['nutrients']) #将字典列表直接做成DataFrame
#print nutrients.head()
#print type(db[0]['nutrients'])
info_keys = ['description','group','id','manufacturer']
info = DataFrame(db,columns = info_keys)
#print info
#查看分类分布情况
#print pd.value_counts(info.group)
#现在，为了将所有的营养数据进行分析，需要将所有营养成分整合到一个大表中，下面分几个步骤来完成
nutrients = []
for rec in db:
fnuts = DataFrame(rec['nutrients'])
fnuts['id'] = rec['id'] #广播
nutrients.append(fnuts)
nutrients = pd.concat(nutrients,ignore_index = True) #将列表连接起来，相当于rbind，把行对其连接在一起
#去重,这是数据处理的重要步骤
print nutrients.duplicated().sum()
nutrients = nutrients.drop_duplicates()
#由于nutrients与info有重复的名字，所以需要重命名一下info
#注意下面这样的命名方式
col_mapping = {'description':'food',
'group':'fgroup'}
#rename函数返回的是副本，需要copy = False
info = info.rename(columns = col_mapping,copy = False)
#print info.columns #查看一下列名
col_mapping = {'description':'nutrient','group':'nutgroup'}
nutrients = nutrients.rename(columns = col_mapping,copy = False)
#print nutrients.columns
#做完上面这些，显然我们需要将两个DataFrame合并起来
print nutrients.ix[:10,:]
#print info.id
ndata = pd.merge(nutrients,info,on = 'id',how = 'outer')
print ndata
print ndata.ix[3000]
#注意下面的处理方式很nice
result = ndata.groupby(['nutrient','fgroup'])['value'].quantile(0.5)
print result
result['Zinc, Zn'].order().plot(kind = 'barh')
plt.show()
#只要稍微动动脑子（作者不止一次说过了……额），就可以发现各营养成分最为丰富的食物是什么了
by_nuttriend = ndata.groupby(['nutgroup','nutrient'])
print by_nuttriend.head()
#注意下面取出最大值的方式
get_maximum = lambda x:x.xs(x.value.idxmax())
get_minimum = lambda x:x.xs(x.value.idxmin())
max_foods = by_nuttriend.apply(get_maximum)[['value','food']]
#让food小一点
max_foods.food = max_foods.food.str[:50]
print max_foods.head()
print max_foods.ix['Amino Acids']['food']
>>>
14179
nutrient nutgroup units value id
0 Protein Composition g 25.18 1008
1 Total lipid (fat) Composition g 29.20 1008
2 Carbohydrate, by difference Composition g 3.06 1008
3 Ash Other g 3.28 1008
4 Energy Energy kcal 376.00 1008
5 Water Composition g 39.28 1008
6 Energy Energy kJ 1573.00 1008
7 Fiber, total dietary Composition g 0.00 1008
8 Calcium, Ca Elements mg 673.00 1008
9 Iron, Fe Elements mg 0.64 1008
10 Magnesium, Mg Elements mg 22.00 1008
<class 'pandas.core.frame.DataFrame'>
Int64Index: 375176 entries, 0 to 375175
Data columns:
nutrient 375176 non-null values
nutgroup 375176 non-null values
units 375176 non-null values
value 375176 non-null values
id 375176 non-null values
food 375176 non-null values
fgroup 375176 non-null values
manufacturer 293054 non-null values
dtypes: float64(1), int64(1), object(6)
nutrient Glycine
nutgroup Amino Acids
units g
value 0.073
id 1077
food Spearmint, fresh
fgroup Spices and Herbs
manufacturer
Name: 3000
nutrient fgroup
Adjusted Protein Sweets 12.900
Vegetables and Vegetable Products 2.180
Alanine Baby Foods 0.085
Baked Products 0.248
Beef Products 1.550
Beverages 0.003
Breakfast Cereals 0.311
Cereal Grains and Pasta 0.373
Dairy and Egg Products 0.271
Ethnic Foods 1.290
Fast Foods 0.514
Fats and Oils 0.000
Finfish and Shellfish Products 1.218
Fruits and Fruit Juices 0.027
Lamb, Veal, and Game Products 1.408
...
Zinc, Zn Finfish and Shellfish Products 0.67
Fruits and Fruit Juices 0.10
Lamb, Veal, and Game Products 3.94
Legumes and Legume Products 1.14
Meals, Entrees, and Sidedishes 0.63
Nut and Seed Products 3.29
Pork Products 2.32
Poultry Products 2.50
Restaurant Foods 0.80
Sausages and Luncheon Meats 2.13
Snacks 1.47
Soups, Sauces, and Gravies 0.20
Spices and Herbs 2.75
Sweets 0.36
Vegetables and Vegetable Products 0.33
Length: 2246
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 467 entries, (u'Amino Acids', u'Alanine', 48) to (u'Vitamins', u'Vitamin K (phylloquinone)', 395)
Data columns:
nutrient 467 non-null values
nutgroup 467 non-null values
units 467 non-null values
value 467 non-null values
id 467 non-null values
food 467 non-null values
fgroup 467 non-null values
manufacturer 444 non-null values
dtypes: float64(1), int64(1), object(6)
value food
nutgroup nutrient
Amino Acids Alanine 8.009 Gelatins, dry powder, unsweetened
Arginine 7.436 Seeds, sesame flour, low-fat
Aspartic acid 10.203 Soy protein isolate
Cystine 1.307 Seeds, cottonseed flour, low fat (glandless)
Glutamic acid 17.452 Soy protein isolate
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
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...
Name: food
[Finished in 14.1s]
5、示例：usda食品数据库
下面是一个具体的例子，书中最重要的就是例子。
#-*- encoding: utf-8 -*-import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas import Series,DataFrame
import re
import json
#加载下面30M+的数据
db = json.load(open('E:\\foods-2011-10-03.json'))
#print len(db)
#print type(db) #得到的db是个list,每个条目都是含有某种食物全部数据的字典
#print db[0] #这一条非常长
#print db[0].keys()
#nutrients 是keys中的一个key，它对应的值是有关食物营养成分的一个字典列表，很长……
#print db[0]['nutrients'][0]
#下面将营养成分做成DataFrame
nutrients = DataFrame(db[0]['nutrients']) #将字典列表直接做成DataFrame
#print nutrients.head()
#print type(db[0]['nutrients'])
info_keys = ['description','group','id','manufacturer']
info = DataFrame(db,columns = info_keys)
#print info
#查看分类分布情况
#print pd.value_counts(info.group)
#现在，为了将所有的营养数据进行分析，需要将所有营养成分整合到一个大表中，下面分几个步骤来完成
nutrients = []
for rec in db:
fnuts = DataFrame(rec['nutrients'])
fnuts['id'] = rec['id'] #广播
nutrients.append(fnuts)
nutrients = pd.concat(nutrients,ignore_index = True) #将列表连接起来，相当于rbind，把行对其连接在一起
#去重,这是数据处理的重要步骤
print nutrients.duplicated().sum()
nutrients = nutrients.drop_duplicates()
#由于nutrients与info有重复的名字，所以需要重命名一下info
#注意下面这样的命名方式
col_mapping = {'description':'food',
'group':'fgroup'}
#rename函数返回的是副本，需要copy = False
info = info.rename(columns = col_mapping,copy = False)
#print info.columns #查看一下列名
col_mapping = {'description':'nutrient','group':'nutgroup'}
nutrients = nutrients.rename(columns = col_mapping,copy = False)
#print nutrients.columns
#做完上面这些，显然我们需要将两个DataFrame合并起来
print nutrients.ix[:10,:]
#print info.id
ndata = pd.merge(nutrients,info,on = 'id',how = 'outer')
print ndata
print ndata.ix[3000]
#注意下面的处理方式很nice
result = ndata.groupby(['nutrient','fgroup'])['value'].quantile(0.5)
print result
result['Zinc, Zn'].order().plot(kind = 'barh')
plt.show()
#只要稍微动动脑子（作者不止一次说过了……额），就可以发现各营养成分最为丰富的食物是什么了
by_nuttriend = ndata.groupby(['nutgroup','nutrient'])
print by_nuttriend.head()
#注意下面取出最大值的方式
get_maximum = lambda x:x.xs(x.value.idxmax())
get_minimum = lambda x:x.xs(x.value.idxmin())
max_foods = by_nuttriend.apply(get_maximum)[['value','food']]
#让food小一点
max_foods.food = max_foods.food.str[:50]
print max_foods.head()
print max_foods.ix['Amino Acids']['food']
>>>
14179
nutrient nutgroup units value id
0 Protein Composition g 25.18 1008
1 Total lipid (fat) Composition g 29.20 1008
2 Carbohydrate, by difference Composition g 3.06 1008
3 Ash Other g 3.28 1008
4 Energy Energy kcal 376.00 1008
5 Water Composition g 39.28 1008
6 Energy Energy kJ 1573.00 1008
7 Fiber, total dietary Composition g 0.00 1008
8 Calcium, Ca Elements mg 673.00 1008
9 Iron, Fe Elements mg 0.64 1008
10 Magnesium, Mg Elements mg 22.00 1008
<class 'pandas.core.frame.DataFrame'>
Int64Index: 375176 entries, 0 to 375175
Data columns:
nutrient 375176 non-null values
nutgroup 375176 non-null values
units 375176 non-null values
value 375176 non-null values
id 375176 non-null values
food 375176 non-null values
fgroup 375176 non-null values
manufacturer 293054 non-null values
dtypes: float64(1), int64(1), object(6)
nutrient Glycine
nutgroup Amino Acids
units g
value 0.073
id 1077
food Spearmint, fresh
fgroup Spices and Herbs
manufacturer
Name: 3000
nutrient fgroup
Adjusted Protein Sweets 12.900
Vegetables and Vegetable Products 2.180
Alanine Baby Foods 0.085
Baked Products 0.248
Beef Products 1.550
Beverages 0.003
Breakfast Cereals 0.311
Cereal Grains and Pasta 0.373
Dairy and Egg Products 0.271
Ethnic Foods 1.290
Fast Foods 0.514
Fats and Oils 0.000
Finfish and Shellfish Products 1.218
Fruits and Fruit Juices 0.027
Lamb, Veal, and Game Products 1.408
...
Zinc, Zn Finfish and Shellfish Products 0.67
Fruits and Fruit Juices 0.10
Lamb, Veal, and Game Products 3.94
Legumes and Legume Products 1.14
Meals, Entrees, and Sidedishes 0.63
Nut and Seed Products 3.29
Pork Products 2.32
Poultry Products 2.50
Restaurant Foods 0.80
Sausages and Luncheon Meats 2.13
Snacks 1.47
Soups, Sauces, and Gravies 0.20
Spices and Herbs 2.75
Sweets 0.36
Vegetables and Vegetable Products 0.33
Length: 2246
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 467 entries, (u'Amino Acids', u'Alanine', 48) to (u'Vitamins', u'Vitamin K (phylloquinone)', 395)
Data columns:
nutrient 467 non-null values
nutgroup 467 non-null values
units 467 non-null values
value 467 non-null values
id 467 non-null values
food 467 non-null values
fgroup 467 non-null values
manufacturer 444 non-null values
dtypes: float64(1), int64(1), object(6)
value food
nutgroup nutrient
Amino Acids Alanine 8.009 Gelatins, dry powder, unsweetened
Arginine 7.436 Seeds, sesame flour, low-fat
Aspartic acid 10.203 Soy protein isolate
Cystine 1.307 Seeds, cottonseed flour, low fat (glandless)
Glutamic acid 17.452 Soy protein isolate
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
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...
Name: food
[Finished in 14.1s]
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