字符串操作
字符串对象方法
val = 'a,b, guido'
val.split(',')
['a', 'b', ' guido']
pieces = [x.strip() for x in val.split(',')]
pieces
['a', 'b', 'guido']
first,second,third = pieces
'::'.join(pieces)
'a::b::guido'
'guido' in val
True
注意find和index的区别：如果找不到字符串，index将会引发一个异常（而不是返回-1）
# find不会报错
val.find(':')
-1
# 返回指定字串的出现次数
val.count(',')
2
正则表达式
如果想避免正则表达式中不需要的转义('')，则可以使用原始字符串字面量如r'C:\x'
import re
text = 'foo bar\t baz \tqux'
re.split('\s+',text)
['foo', 'bar', 'baz', 'qux']
regex = re.compile('\s+')
regex.split(text)
['foo', 'bar', 'baz', 'qux']
regex.findall(text)
[' ', '\t ', ' \t']
pandas中矢量化的字符串函数
import numpy as np
from pandas import Series
data = {'Davae':'dave@google.com','Steve':'steve@gmail.com','Rob':'rob@gmail.com','Wes':np.nan}
data
{'Davae': 'dave@google.com',
'Steve': 'steve@gmail.com',
'Rob': 'rob@gmail.com',
'Wes': nan}
data2 = Series(data)
data2
Davae dave@google.com
Steve steve@gmail.com
Rob rob@gmail.com
Wes NaN
dtype: object
data2.isnull()
Davae False
Steve False
Rob False
Wes True
dtype: bool
通过data.map，所有字符串和正则表达式方法都能被应用于各个值
对象下面的属性，可以取得所有的字符串
data2.str.contains('gmail')
Davae False
Steve True
Rob True
Wes NaN
dtype: object
# 匹配规则
regex = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})'
# 映射匹配
res = data2.str.findall(regex,flags = re.IGNORECASE)
res
Davae [(dave, google, com)]
Steve [(steve, gmail, com)]
Rob [(rob, gmail, com)]
Wes NaN
dtype: object
# 直接使用get属性索引取值
res_first = res.str.get(0)
res_first
Davae (dave, google, com)
Steve (steve, gmail, com)
Rob (rob, gmail, com)
Wes NaN
dtype: object
res_second = res_first.str.get(1)
res_second
Davae google
Steve gmail
Rob gmail
Wes NaN
dtype: object
res_second.str[:2]
Davae go
Steve gm
Rob gm
Wes NaN
dtype: object
示例： USDA食品数据库
import json
import pandas as pd
db = json.load(open(r'C:\Users\1\Desktop\Python\练习代码\基础模块面向对象网络编程\day2\food.json'))
len(db)
db[0].keys()
dict_keys(['id', 'description', 'tags', 'manufacturer', 'group', 'portions', 'nutrients'])
db[0]['nutrients'][0]
{'value': 25.18,
'units': 'g',
'description': 'Protein',
'group': 'Composition'}
nutrients = pd.DataFrame(db[0]['nutrients'])
nutrients[:7]
description group units value
0 Protein Composition g 25.18
1 Total lipid (fat) Composition g 29.20
2 Carbohydrate, by difference Composition g 3.06
3 Ash Other g 3.28
4 Energy Energy kcal 376.00
5 Water Composition g 39.28
6 Energy Energy kJ 1573.00
info_keys = ['description','group','id','manufacturer']
info = pd.DataFrame(db, columns=info_keys)
info.head()
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
# 查看食品的分类情况
pd.value_counts(info['group'])
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
Beverages 278
Soups, Sauces, and Gravies 275
Finfish and Shellfish Products 255
Baby Foods 209
Cereal Grains and Pasta 183
Ethnic Foods 165
Snacks 162
Nut and Seed Products 128
Poultry Products 116
Sausages and Luncheon Meats 111
Dairy and Egg Products 107
Fats and Oils 97
Meals, Entrees, and Sidedishes 57
Restaurant Foods 51
Spices and Herbs 41
Name: group, dtype: int64
nutrients = []
for rec in db:
fnuts = pd.DataFrame(rec['nutrients'])
fnuts['id'] = rec['id']
nutrients.append(fnuts)
# 拼接所有的营养成分
nutrients = pd.concat(nutrients, ignore_index=True)
nutrients.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 389355 entries, 0 to 389354
Data columns (total 5 columns):
description 389355 non-null object
group 389355 non-null object
units 389355 non-null object
value 389355 non-null float64
id 389355 non-null int64
dtypes: float64(1), int64(1), object(3)
memory usage: 14.9+ MB
# 去重,统计重复的行
nutrients.duplicated().sum()
14179
# 直接得到去重的结果
nutrients = nutrients.drop_duplicates()
# 这是营养成分的描述和分组，上面还有食物的描述和分组
nutrients.head()
description group 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
# 为了便于区别，需要重新命名
col_mapping = {'description':'food',
'group':'fgroup'
}
# 食物的重命名
info = info.rename(columns=col_mapping, copy=False)
info.head()
food fgroup 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
# 营养成分的重命名
col_mapping = {'description':'nutrient',
'group':'nutgroup'
}
nutrients = nutrients.rename(columns=col_mapping, copy=False)
nutrients.head()
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
# 两表合一,on指定两表都有列名，用外连
ndata = pd.merge(nutrients, info, on='id', how='outer')
ndata.head()
nutrient nutgroup units value id food fgroup manufacturer
0 Protein Composition g 25.18 1008 Cheese, caraway Dairy and Egg Products
1 Total lipid (fat) Composition g 29.20 1008 Cheese, caraway Dairy and Egg Products
2 Carbohydrate, by difference Composition g 3.06 1008 Cheese, caraway Dairy and Egg Products
3 Ash Other g 3.28 1008 Cheese, caraway Dairy and Egg Products
4 Energy Energy kcal 376.00 1008 Cheese, caraway Dairy and Egg Products
# 按食物和营养成分分组，得到各食物营养成分最多的食物
by_nutrient = ndata.groupby(['nutrient','fgroup'])
get_maximum = lambda x:x.xs(x.value.idxmax())
max_foods = by_nutrient.apply(get_maximum)
max_foods.head()
# 只看其中的value和food
max_foods[['value','food']].head()
value food
nutrient fgroup
Adjusted Protein Sweets 12.900 Baking chocolate, unsweetened, squares
Vegetables and Vegetable Products 2.180 Mushrooms, white, raw
Alanine Baby Foods 0.911 Babyfood, meat, ham, junior
Baked Products 2.320 Leavening agents, yeast, baker's, active dry
Beef Products 2.254 Beef, cured, breakfast strips, cooked
