前言
个人感觉网上对pandas的总结感觉不够详尽细致，在这里我对pandas做个相对细致的小结吧，在数据分析与人工智能方面会有所涉及到的东西在这里都说说吧，也是对自己学习的一种小结！
pandas用法的介绍
安装部分我就不说了，装个pip，使用命令pip install pandas就可以安装了，在Ubuntu中可能会出现没有权限的提示，直接加上sudo即可，以下讲解都是建立在python3平台的讲解，python2类似，python3中安装的时候使用sudo pip3 install pandas即可。
pandas是Python的一个数据分析模块，是为了解决数据分析任务而创建的，纳入了大量的库和标准数据模型，提供了高效地操作大型数据集所需的工具。
pandas中的数据结构 :
- Series: 一维数组，类似于python中的基本数据结构list，区别是series只允许存储相同的数据类型，这样可以更有效的使用内存，提高运算效率。就像数据库中的列数据。
- DataFrame: 二维的表格型数据结构。很多功能与R中的data.frame类似。可以将DataFrame理解为Series的容器。
- Panel：三维的数组，可以理解为DataFrame的容器。
关于pandas的更多详细的介绍请参看：http://pandas.pydata.org/pandas-docs/stable/10min.html
感兴趣的同学还可以看看我之前写过的numpy用法小结，库中大部分用法和numpy类似，可以对比着看，方便理解
下面我们以一个food_info.csv数据集来为大家讲解pandas的基本用法，该数据文件有需要的同学可以加我好友私聊我，或者把你的请求发邮箱至i_love_sjtu@qq.com，感谢看此文的您的支持和理解~~~
1.read_csv
pandas.read_csv("")，这里我们讲解下，read_csv函数的意思是读取文件信息，用来处理数据信息，可以处理数据文件。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(type(food_info))
print(food_info.dtypes)
打印结果如下：
<class 'pandas.core.frame.DataFrame'>
NDB_No int64
Shrt_Desc object
Water_(g) float64
Energ_Kcal int64
Protein_(g) float64
Lipid_Tot_(g) float64
Ash_(g) float64
Carbohydrt_(g) float64
Fiber_TD_(g) float64
Sugar_Tot_(g) float64
Calcium_(mg) float64
Iron_(mg) float64
Magnesium_(mg) float64
Phosphorus_(mg) float64
Potassium_(mg) float64
Sodium_(mg) float64
Zinc_(mg) float64
Copper_(mg) float64
Manganese_(mg) float64
Selenium_(mcg) float64
Vit_C_(mg) float64
Thiamin_(mg) float64
Riboflavin_(mg) float64
Niacin_(mg) float64
Vit_B6_(mg) float64
Vit_B12_(mcg) float64
Vit_A_IU float64
Vit_A_RAE float64
Vit_E_(mg) float64
Vit_D_mcg float64
Vit_D_IU float64
Vit_K_(mcg) float64
FA_Sat_(g) float64
FA_Mono_(g) float64
FA_Poly_(g) float64
Cholestrl_(mg) float64
dtype: object
我解释一下上面的用法，genfromtxt传入了三个参数，第一个参数是数据文件，名为world_alcohol.txt，该数据文件有需要的同学可以加我好友私聊我，或者把你的请求发邮箱至i_love_sjtu@qq.com
然后delimiter是分隔符，由于数据集中的数据是用逗号分隔的，所以设定参数delimiter=','，dtype是获取数据类型，数据集中的类型为str
print(type(food_info))打印数据文件的数据类型
print(food_info.dtypes)打印每一列数据的格式
2.shape
xxx.shape 显示的功能是查看数据表的维度数
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.shape)
打印结果：
(8618, 36)
显示出当前表的维度是8618行36列。
3.info()
xxx.info()获取数据表基本信息（维度、列名称、数据格式、所占空间等）
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.info())
打印结果：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8618 entries, 0 to 8617
Data columns (total 36 columns):
NDB_No 8618 non-null int64
Shrt_Desc 8618 non-null object
Water_(g) 8612 non-null float64
Energ_Kcal 8618 non-null int64
Protein_(g) 8618 non-null float64
Lipid_Tot_(g) 8618 non-null float64
Ash_(g) 8286 non-null float64
Carbohydrt_(g) 8618 non-null float64
Fiber_TD_(g) 7962 non-null float64
Sugar_Tot_(g) 6679 non-null float64
Calcium_(mg) 8264 non-null float64
Iron_(mg) 8471 non-null float64
Magnesium_(mg) 7936 non-null float64
Phosphorus_(mg) 8046 non-null float64
Potassium_(mg) 8208 non-null float64
Sodium_(mg) 8535 non-null float64
Zinc_(mg) 7917 non-null float64
Copper_(mg) 7363 non-null float64
Manganese_(mg) 6478 non-null float64
Selenium_(mcg) 6868 non-null float64
Vit_C_(mg) 7826 non-null float64
Thiamin_(mg) 7939 non-null float64
Riboflavin_(mg) 7961 non-null float64
Niacin_(mg) 7937 non-null float64
Vit_B6_(mg) 7677 non-null float64
Vit_B12_(mcg) 7427 non-null float64
Vit_A_IU 7932 non-null float64
Vit_A_RAE 7089 non-null float64
Vit_E_(mg) 5613 non-null float64
Vit_D_mcg 5319 non-null float64
Vit_D_IU 5320 non-null float64
Vit_K_(mcg) 4969 non-null float64
FA_Sat_(g) 8274 non-null float64
FA_Mono_(g) 7947 non-null float64
FA_Poly_(g) 7954 non-null float64
Cholestrl_(mg) 8250 non-null float64
dtypes: float64(33), int64(2), object(1)
memory usage: 2.4+ MB
4.dtypes和astypes
xxx.dtypes是显示每一列数据的格式，可以指定某一列。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.dtypes)
打印结果：
NDB_No int64
Shrt_Desc object
Water_(g) float64
Energ_Kcal int64
Protein_(g) float64
Lipid_Tot_(g) float64
Ash_(g) float64
Carbohydrt_(g) float64
Fiber_TD_(g) float64
Sugar_Tot_(g) float64
Calcium_(mg) float64
Iron_(mg) float64
Magnesium_(mg) float64
Phosphorus_(mg) float64
Potassium_(mg) float64
Sodium_(mg) float64
Zinc_(mg) float64
Copper_(mg) float64
Manganese_(mg) float64
Selenium_(mcg) float64
Vit_C_(mg) float64
Thiamin_(mg) float64
Riboflavin_(mg) float64
Niacin_(mg) float64
Vit_B6_(mg) float64
Vit_B12_(mcg) float64
Vit_A_IU float64
Vit_A_RAE float64
Vit_E_(mg) float64
Vit_D_mcg float64
Vit_D_IU float64
Vit_K_(mcg) float64
FA_Sat_(g) float64
FA_Mono_(g) float64
FA_Poly_(g) float64
Cholestrl_(mg) float64
dtype: object
而如果我们想转换表中指定列的数据类型 我们应该使用astype进行转换
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info['NDB_No'].astype('float64'))
打印结果：
0 1001.0
1 1002.0
2 1003.0
3 1004.0
4 1005.0
5 1006.0
6 1007.0
7 1008.0
8 1009.0
9 1010.0
10 1011.0
11 1012.0
12 1013.0
13 1014.0
14 1015.0
15 1016.0
16 1017.0
17 1018.0
18 1019.0
19 1020.0
20 1021.0
21 1022.0
22 1023.0
23 1024.0
24 1025.0
25 1026.0
26 1027.0
27 1028.0
28 1029.0
29 1030.0
...
8588 43544.0
8589 43546.0
8590 43550.0
8591 43566.0
8592 43570.0
8593 43572.0
8594 43585.0
8595 43589.0
8596 43595.0
8597 43597.0
8598 43598.0
8599 44005.0
8600 44018.0
8601 44048.0
8602 44055.0
8603 44061.0
8604 44074.0
8605 44110.0
8606 44158.0
8607 44203.0
8608 44258.0
8609 44259.0
8610 44260.0
8611 48052.0
8612 80200.0
8613 83110.0
8614 90240.0
8615 90480.0
8616 90560.0
8617 93600.0
Name: NDB_No, Length: 8618, dtype: float64
原来NDB_No是int64类型，现在转换为float64类型了
5.isnull()
xxx.isnull() 用来查看数据表或者某一列数据的值是否为空值。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.isnull())
打印结果：
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) Lipid_Tot_(g) \
0 False False False False False False
1 False False False False False False
2 False False False False False False
3 False False False False False False
4 False False False False False False
5 False False False False False False
6 False False False False False False
7 False False False False False False
8 False False False False False False
9 False False False False False False
10 False False False False False False
11 False False False False False False
12 False False False False False False
13 False False False False False False
14 False False False False False False
15 False False False False False False
16 False False False False False False
17 False False False False False False
18 False False False False False False
19 False False False False False False
20 False False False False False False
21 False False False False False False
22 False False False False False False
23 False False False False False False
24 False False False False False False
25 False False False False False False
26 False False False False False False
27 False False False False False False
28 False False False False False False
29 False False False False False False
... ... ... ... ... ... ...
8588 False False False False False False
8589 False False False False False False
8590 False False False False False False
8591 False False False False False False
8592 False False False False False False
8593 False False False False False False
8594 False False False False False False
8595 False False False False False False
8596 False False False False False False
8597 False False False False False False
8598 False False False False False False
8599 False False False False False False
8600 False False False False False False
8601 False False False False False False
8602 False False False False False False
8603 False False False False False False
8604 False False False False False False
8605 False False False False False False
8606 False False False False False False
8607 False False False False False False
8608 False False False False False False
8609 False False False False False False
8610 False False False False False False
8611 False False False False False False
8612 False False False False False False
8613 False False False False False False
8614 False False False False False False
8615 False False False False False False
8616 False False False False False False
8617 False False False False False False Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) ... \
0 False False False False ...
1 False False False False ...
2 False False False False ...
3 False False False False ...
4 False False False False ...
5 False False False False ...
6 False False False False ...
7 False False False True ...
8 False False False False ...
9 False False False True ...
10 False False False False ...
11 False False False False ...
12 False False False False ...
13 False False False False ...
14 False False False False ...
15 False False False False ...
16 False False False False ...
17 False False False False ...
18 False False False False ...
19 False False False False ...
20 False False False True ...
21 False False False False ...
22 False False False False ...
23 False False False False ...
24 False False False False ...
25 False False False False ...
26 False False False False ...
27 False False False False ...
28 False False False False ...
29 False False False False ...
... ... ... ... ... ...
8588 False False False False ...
8589 False False False False ...
8590 False False False False ...
8591 False False False False ...
8592 False False False False ...
8593 False False False False ...
8594 False False False False ...
8595 False False False False ...
8596 False False False False ...
8597 False False False False ...
8598 False False False False ...
8599 False False False False ...
8600 False False False False ...
8601 False False False False ...
8602 False False False False ...
8603 False False False False ...
8604 False False False True ...
8605 False False False False ...
8606 False False False False ...
8607 False False False False ...
8608 False False False False ...
8609 False False False False ...
8610 False False False False ...
8611 False False False False ...
8612 False False False False ...
8613 False False False False ...
8614 False False False False ...
8615 False False False False ...
8616 False False False False ...
8617 False False False False ... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU Vit_K_(mcg) \
0 False False False False False False
1 False False False False False False
2 False False False False False False
3 False False False False False False
4 False False False False False False
5 False False False False False False
6 False False False False False False
7 False False True True True True
8 False False False False False False
9 False False True True True True
10 False False False False False False
11 False False False False False False
12 False False False False False False
13 False False False False False False
14 False False False False False False
15 False False False False False False
16 False False False False False False
17 False False False False False False
18 False False False False False False
19 False False False False False False
20 False False True True True True
21 False False False False False False
22 False False False False False False
23 False False False False False False
24 False False False False False False
25 False False False False False False
26 False False False False False False
27 False False False False False False
28 False False False False False False
29 False False False False False False
... ... ... ... ... ... ...
8588 False False False False False False
8589 False False False False False False
8590 False False False False False False
8591 False False False False False False
8592 False False False False False False
8593 False False False False False False
8594 False False False False False False
8595 False False False False False False
8596 False False False False False False
8597 False False False False False False
8598 False False False False False False
8599 False False False False False False
8600 False False False False False False
8601 False False False False False False
8602 False False False False False False
8603 False False False False False False
8604 False True True True True True
8605 False False False False False False
8606 False False False False False False
8607 False False False False False False
8608 False False False False False False
8609 False False False False False False
8610 False False False False False False
8611 False False False False False False
8612 False False False False False False
8613 False False False False False False
8614 False False False False False False
8615 False False False False False False
8616 False False False False False False
8617 False False False False False False FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
0 False False False False
1 False False False False
2 False False False False
3 False False False False
4 False False False False
5 False False False False
6 False False False False
7 False False False False
8 False False False False
9 False False False False
10 False False False False
11 False False False False
12 False False False False
13 False False False False
14 False False False False
15 False False False False
16 False False False False
17 False False False False
18 False False False False
19 False False False False
20 False False False False
21 False False False False
22 False False False False
23 False False False False
24 False False False False
25 False False False False
26 False False False False
27 False False False False
28 False False False False
29 False False False False
... ... ... ... ...
8588 False False False False
8589 False False False False
8590 False False False False
8591 False False False False
8592 False False False False
8593 False False False False
8594 False False False False
8595 False False False False
8596 False False False False
8597 False False False False
8598 False False False False
8599 False False False False
8600 False False False False
8601 False False False False
8602 False False False False
8603 False False False False
8604 False False False False
8605 False False False False
8606 False False False False
8607 False False False False
8608 False False False False
8609 False False False False
8610 False False False False
8611 False False False False
8612 False False False False
8613 False False False False
8614 False False False False
8615 False False False False
8616 False False False False
8617 False False False False [8618 rows x 36 columns]
6.columns
xxx.columns可以用来查看数据表中列的名称
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.columns)
打印结果：
Index(['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)',
'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)',
'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)',
'Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)',
'Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)',
'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)',
'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg',
'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_Poly_(g)',
'Cholestrl_(mg)'],
dtype='object')
7.head()和tail()
xxx.head()默认是用来查看前10行数据
而xxx.tail()默认用来查看后10行数据
可以传入参数x，指定查看前x行的数据
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.head())
print(food_info.tail())
打印结果：
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
0 1001 BUTTER WITH SALT 15.87 717 0.85
1 1002 BUTTER WHIPPED WITH SALT 15.87 717 0.85
2 1003 BUTTER OIL ANHYDROUS 0.24 876 0.28
3 1004 CHEESE BLUE 42.41 353 21.40
4 1005 CHEESE BRICK 41.11 371 23.24 Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) \
0 81.11 2.11 0.06 0.0 0.06
1 81.11 2.11 0.06 0.0 0.06
2 99.48 0.00 0.00 0.0 0.00
3 28.74 5.11 2.34 0.0 0.50
4 29.68 3.18 2.79 0.0 0.51 ... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU \
0 ... 2499.0 684.0 2.32 1.5 60.0
1 ... 2499.0 684.0 2.32 1.5 60.0
2 ... 3069.0 840.0 2.80 1.8 73.0
3 ... 721.0 198.0 0.25 0.5 21.0
4 ... 1080.0 292.0 0.26 0.5 22.0 Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
0 7.0 51.368 21.021 3.043 215.0
1 7.0 50.489 23.426 3.012 219.0
2 8.6 61.924 28.732 3.694 256.0
3 2.4 18.669 7.778 0.800 75.0
4 2.5 18.764 8.598 0.784 94.0 [5 rows x 36 columns]
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
8613 83110 MACKEREL SALTED 43.00 305 18.50
8614 90240 SCALLOP (BAY&SEA) CKD STMD 70.25 111 20.54
8615 90480 SYRUP CANE 26.00 269 0.00
8616 90560 SNAIL RAW 79.20 90 16.10
8617 93600 TURTLE GREEN RAW 78.50 89 19.80 Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) \
8613 25.10 13.40 0.00 0.0 0.0
8614 0.84 2.97 5.41 0.0 0.0
8615 0.00 0.86 73.14 0.0 73.2
8616 1.40 1.30 2.00 0.0 0.0
8617 0.50 1.20 0.00 0.0 0.0 ... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU \
8613 ... 157.0 47.0 2.38 25.2 1006.0
8614 ... 5.0 2.0 0.00 0.0 2.0
8615 ... 0.0 0.0 0.00 0.0 0.0
8616 ... 100.0 30.0 5.00 0.0 0.0
8617 ... 100.0 30.0 0.50 0.0 0.0 Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
8613 7.8 7.148 8.320 6.210 95.0
8614 0.0 0.218 0.082 0.222 41.0
8615 0.0 0.000 0.000 0.000 0.0
8616 0.1 0.361 0.259 0.252 50.0
8617 0.1 0.127 0.088 0.170 50.0 [5 rows x 36 columns]
8.index和values
xxx.index查看表的索引值
而xxx.values查看表的值
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.index)
print(food_info.values)
打印结果：
RangeIndex(start=0, stop=8618, step=1)
[[1001 'BUTTER WITH SALT' 15.87 ... 21.021 3.043 215.0]
[1002 'BUTTER WHIPPED WITH SALT' 15.87 ... 23.426 3.012 219.0]
[1003 'BUTTER OIL ANHYDROUS' 0.24 ... 28.732 3.694 256.0]
...
[90480 'SYRUP CANE' 26.0 ... 0.0 0.0 0.0]
[90560 'SNAIL RAW' 79.2 ... 0.259 0.252 50.0]
[93600 'TURTLE GREEN RAW' 78.5 ... 0.08800000000000001 0.17 50.0]]
9.describe()
xxx.describe()用来查看数据的快速统计结果。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.describe())
打印结果：
<bound method NDFrame.describe of NDB_No Shrt_Desc Water_(g) \
0 1001 BUTTER WITH SALT 15.87
1 1002 BUTTER WHIPPED WITH SALT 15.87
2 1003 BUTTER OIL ANHYDROUS 0.24
3 1004 CHEESE BLUE 42.41
4 1005 CHEESE BRICK 41.11
5 1006 CHEESE BRIE 48.42
6 1007 CHEESE CAMEMBERT 51.80
7 1008 CHEESE CARAWAY 39.28
8 1009 CHEESE CHEDDAR 37.10
9 1010 CHEESE CHESHIRE 37.65
10 1011 CHEESE COLBY 38.20
11 1012 CHEESE COTTAGE CRMD LRG OR SML CURD 79.79
12 1013 CHEESE COTTAGE CRMD W/FRUIT 79.64
13 1014 CHEESE COTTAGE NONFAT UNCRMD DRY LRG OR SML CURD 81.01
14 1015 CHEESE COTTAGE LOWFAT 2% MILKFAT 81.24
15 1016 CHEESE COTTAGE LOWFAT 1% MILKFAT 82.48
16 1017 CHEESE CREAM 54.44
17 1018 CHEESE EDAM 41.56
18 1019 CHEESE FETA 55.22
19 1020 CHEESE FONTINA 37.92
20 1021 CHEESE GJETOST 13.44
21 1022 CHEESE GOUDA 41.46
22 1023 CHEESE GRUYERE 33.19
23 1024 CHEESE LIMBURGER 48.42
24 1025 CHEESE MONTEREY 41.01
25 1026 CHEESE MOZZARELLA WHL MILK 50.01
26 1027 CHEESE MOZZARELLA WHL MILK LO MOIST 48.38
27 1028 CHEESE MOZZARELLA PART SKIM MILK 53.78
28 1029 CHEESE MOZZARELLA LO MOIST PART-SKIM 45.54
29 1030 CHEESE MUENSTER 41.77
... ... ... ...
8588 43544 BABYFOOD CRL RICE W/ PEARS & APPL DRY INST 2.00
8589 43546 BABYFOOD BANANA NO TAPIOCA STR 76.70
8590 43550 BABYFOOD BANANA APPL DSSRT STR 83.10
8591 43566 SNACKS TORTILLA CHIPS LT (BAKED W/ LESS OIL) 1.30
8592 43570 CEREALS RTE POST HONEY BUNCHES OF OATS HONEY RSTD 5.00
8593 43572 POPCORN MICROWAVE LOFAT&NA 2.80
8594 43585 BABYFOOD FRUIT SUPREME DSSRT 81.60
8595 43589 CHEESE SWISS LOW FAT 59.60
8596 43595 BREAKFAST BAR CORN FLAKE CRUST W/FRUIT 14.50
8597 43597 CHEESE MOZZARELLA LO NA 49.90
8598 43598 MAYONNAISE DRSNG NO CHOL 21.70
8599 44005 OIL CORN PEANUT AND OLIVE 0.00
8600 44018 SWEETENERS TABLETOP FRUCTOSE LIQ 23.90
8601 44048 CHEESE FOOD IMITATION 55.50
8602 44055 CELERY FLAKES DRIED 9.00
8603 44061 PUDDINGS CHOC FLAVOR LO CAL INST DRY MIX 4.20
8604 44074 BABYFOOD GRAPE JUC NO SUGAR CND 84.40
8605 44110 JELLIES RED SUGAR HOME PRESERVED 53.00
8606 44158 PIE FILLINGS BLUEBERRY CND 54.66
8607 44203 COCKTAIL MIX NON-ALCOHOLIC CONCD FRZ 28.24
8608 44258 PUDDINGS CHOC FLAVOR LO CAL REG DRY MIX 6.80
8609 44259 PUDDINGS ALL FLAVORS XCPT CHOC LO CAL REG DRY MIX 10.40
8610 44260 PUDDINGS ALL FLAVORS XCPT CHOC LO CAL INST DRY... 6.84
8611 48052 VITAL WHEAT GLUTEN 8.20
8612 80200 FROG LEGS RAW 81.90
8613 83110 MACKEREL SALTED 43.00
8614 90240 SCALLOP (BAY&SEA) CKD STMD 70.25
8615 90480 SYRUP CANE 26.00
8616 90560 SNAIL RAW 79.20
8617 93600 TURTLE GREEN RAW 78.50 Energ_Kcal Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \
0 717 0.85 81.11 2.11 0.06
1 717 0.85 81.11 2.11 0.06
2 876 0.28 99.48 0.00 0.00
3 353 21.40 28.74 5.11 2.34
4 371 23.24 29.68 3.18 2.79
5 334 20.75 27.68 2.70 0.45
6 300 19.80 24.26 3.68 0.46
7 376 25.18 29.20 3.28 3.06
8 406 24.04 33.82 3.71 1.33
9 387 23.37 30.60 3.60 4.78
10 394 23.76 32.11 3.36 2.57
11 98 11.12 4.30 1.41 3.38
12 97 10.69 3.85 1.20 4.61
13 72 10.34 0.29 1.71 6.66
14 81 10.45 2.27 1.27 4.76
15 72 12.39 1.02 1.39 2.72
16 342 5.93 34.24 1.32 4.07
17 357 24.99 27.80 4.22 1.43
18 264 14.21 21.28 5.20 4.09
19 389 25.60 31.14 3.79 1.55
20 466 9.65 29.51 4.75 42.65
21 356 24.94 27.44 3.94 2.22
22 413 29.81 32.34 4.30 0.36
23 327 20.05 27.25 3.79 0.49
24 373 24.48 30.28 3.55 0.68
25 300 22.17 22.35 3.28 2.19
26 318 21.60 24.64 2.91 2.47
27 254 24.26 15.92 3.27 2.77
28 301 24.58 19.72 3.80 6.36
29 368 23.41 30.04 3.66 1.12
... ... ... ... ... ...
8588 389 6.60 0.90 2.00 88.60
8589 91 1.00 0.20 0.76 21.34
8590 68 0.30 0.20 0.29 16.30
8591 465 8.70 15.20 1.85 73.40
8592 401 7.12 5.46 1.22 81.19
8593 429 12.60 9.50 1.71 73.39
8594 73 0.50 0.20 0.52 17.18
8595 179 28.40 5.10 3.50 3.40
8596 377 4.40 7.50 0.80 72.90
8597 280 27.50 17.10 2.40 3.10
8598 688 0.00 77.80 0.40 0.30
8599 884 0.00 100.00 0.00 0.00
8600 279 0.00 0.00 0.00 76.10
8601 257 4.08 19.50 4.74 16.18
8602 319 11.30 2.10 13.90 63.70
8603 356 5.30 2.40 9.90 78.20
8604 62 0.00 0.00 0.22 15.38
8605 179 0.30 0.03 0.08 46.10
8606 181 0.41 0.20 0.35 44.38
8607 287 0.08 0.01 0.07 71.60
8608 365 10.08 3.00 5.70 74.42
8609 351 1.60 0.10 1.86 86.04
8610 350 0.81 0.90 6.80 84.66
8611 370 75.16 1.85 1.00 13.79
8612 73 16.40 0.30 1.40 0.00
8613 305 18.50 25.10 13.40 0.00
8614 111 20.54 0.84 2.97 5.41
8615 269 0.00 0.00 0.86 73.14
8616 90 16.10 1.40 1.30 2.00
8617 89 19.80 0.50 1.20 0.00 Fiber_TD_(g) Sugar_Tot_(g) ... Vit_A_IU Vit_A_RAE \
0 0.0 0.06 ... 2499.0 684.0
1 0.0 0.06 ... 2499.0 684.0
2 0.0 0.00 ... 3069.0 840.0
3 0.0 0.50 ... 721.0 198.0
4 0.0 0.51 ... 1080.0 292.0
5 0.0 0.45 ... 592.0 174.0
6 0.0 0.46 ... 820.0 241.0
7 0.0 NaN ... 1054.0 271.0
8 0.0 0.28 ... 994.0 263.0
9 0.0 NaN ... 985.0 233.0
10 0.0 0.52 ... 994.0 264.0
11 0.0 2.67 ... 140.0 37.0
12 0.2 2.38 ... 146.0 38.0
13 0.0 1.85 ... 8.0 2.0
14 0.0 4.00 ... 225.0 68.0
15 0.0 2.72 ... 41.0 11.0
16 0.0 3.21 ... 1343.0 366.0
17 0.0 1.43 ... 825.0 243.0
18 0.0 4.09 ... 422.0 125.0
19 0.0 1.55 ... 913.0 261.0
20 0.0 NaN ... 1113.0 334.0
21 0.0 2.22 ... 563.0 165.0
22 0.0 0.36 ... 948.0 271.0
23 0.0 0.49 ... 1155.0 340.0
24 0.0 0.50 ... 769.0 198.0
25 0.0 1.03 ... 676.0 179.0
26 0.0 1.01 ... 745.0 197.0
27 0.0 1.13 ... 481.0 127.0
28 0.0 2.24 ... 846.0 254.0
29 0.0 1.12 ... 1012.0 298.0
... ... ... ... ... ...
8588 2.6 1.35 ... 0.0 0.0
8589 1.6 11.36 ... 5.0 0.0
8590 1.0 14.66 ... 30.0 2.0
8591 5.7 0.53 ... 81.0 4.0
8592 4.2 19.79 ... 2731.0 806.0
8593 14.2 0.54 ... 147.0 7.0
8594 2.0 14.87 ... 50.0 3.0
8595 0.0 1.33 ... 152.0 40.0
8596 2.1 35.10 ... 2027.0 608.0
8597 0.0 1.23 ... 517.0 137.0
8598 0.0 0.30 ... 0.0 0.0
8599 0.0 0.00 ... 0.0 0.0
8600 0.1 76.00 ... 0.0 0.0
8601 0.0 8.21 ... 900.0 45.0
8602 27.8 35.90 ... 1962.0 98.0
8603 6.1 0.70 ... 0.0 0.0
8604 0.1 NaN ... 8.0 NaN
8605 0.8 45.30 ... 3.0 0.0
8606 2.6 37.75 ... 22.0 1.0
8607 0.0 24.53 ... 12.0 1.0
8608 10.1 0.70 ... 0.0 0.0
8609 0.9 2.90 ... 0.0 0.0
8610 0.8 0.90 ... 0.0 0.0
8611 0.6 0.00 ... 0.0 0.0
8612 0.0 0.00 ... 50.0 15.0
8613 0.0 0.00 ... 157.0 47.0
8614 0.0 0.00 ... 5.0 2.0
8615 0.0 73.20 ... 0.0 0.0
8616 0.0 0.00 ... 100.0 30.0
8617 0.0 0.00 ... 100.0 30.0 Vit_E_(mg) Vit_D_mcg Vit_D_IU Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) \
0 2.32 1.5 60.0 7.0 51.368 21.021
1 2.32 1.5 60.0 7.0 50.489 23.426
2 2.80 1.8 73.0 8.6 61.924 28.732
3 0.25 0.5 21.0 2.4 18.669 7.778
4 0.26 0.5 22.0 2.5 18.764 8.598
5 0.24 0.5 20.0 2.3 17.410 8.013
6 0.21 0.4 18.0 2.0 15.259 7.023
7 NaN NaN NaN NaN 18.584 8.275
8 0.78 0.6 24.0 2.9 19.368 8.428
9 NaN NaN NaN NaN 19.475 8.671
10 0.28 0.6 24.0 2.7 20.218 9.280
11 0.08 0.1 3.0 0.0 1.718 0.778
12 0.04 0.0 0.0 0.4 2.311 1.036
13 0.01 0.0 0.0 0.0 0.169 0.079
14 0.08 0.0 0.0 0.0 1.235 0.516
15 0.01 0.0 0.0 0.1 0.645 0.291
16 0.29 0.6 25.0 2.9 19.292 8.620
17 0.24 0.5 20.0 2.3 17.572 8.125
18 0.18 0.4 16.0 1.8 14.946 4.623
19 0.27 0.6 23.0 2.6 19.196 8.687
20 NaN NaN NaN NaN 19.160 7.879
21 0.24 0.5 20.0 2.3 17.614 7.747
22 0.28 0.6 24.0 2.7 18.913 10.043
23 0.23 0.5 20.0 2.3 16.746 8.606
24 0.26 0.6 22.0 2.5 19.066 8.751
25 0.19 0.4 16.0 2.3 13.152 6.573
26 0.21 0.5 18.0 2.5 15.561 7.027
27 0.14 0.3 12.0 1.6 10.114 4.510
28 0.43 0.4 15.0 1.3 11.473 5.104
29 0.26 0.6 22.0 2.5 19.113 8.711
... ... ... ... ... ... ...
8588 0.13 0.0 0.0 0.3 0.185 0.252
8589 0.25 0.0 0.0 0.5 0.072 0.028
8590 0.02 0.0 0.0 0.1 0.058 0.018
8591 3.53 0.0 0.0 0.7 2.837 6.341
8592 1.22 4.6 183.0 3.0 0.600 2.831
8593 5.01 0.0 0.0 15.7 1.415 4.085
8594 0.79 0.0 0.0 5.1 0.030 0.025
8595 0.07 0.1 4.0 0.5 3.304 1.351
8596 0.76 0.0 0.0 13.8 1.500 5.000
8597 0.15 0.3 13.0 1.8 10.867 4.844
8598 11.79 0.0 0.0 24.7 10.784 18.026
8599 14.78 0.0 0.0 21.0 14.367 48.033
8600 0.00 0.0 0.0 0.0 0.000 0.000
8601 2.15 0.0 0.0 36.7 7.996 3.108
8602 5.55 0.0 0.0 584.2 0.555 0.405
8603 0.02 0.0 0.0 0.4 0.984 1.154
8604 NaN NaN NaN NaN 0.000 0.000
8605 0.00 0.0 0.0 0.2 0.009 0.001
8606 0.23 0.0 0.0 3.9 0.000 0.000
8607 0.02 0.0 0.0 0.0 0.003 0.001
8608 0.02 0.0 0.0 0.5 1.578 1.150
8609 0.05 0.0 0.0 1.1 0.018 0.032
8610 0.08 0.0 0.0 1.7 0.099 0.116
8611 0.00 0.0 0.0 0.0 0.272 0.156
8612 1.00 0.2 8.0 0.1 0.076 0.053
8613 2.38 25.2 1006.0 7.8 7.148 8.320
8614 0.00 0.0 2.0 0.0 0.218 0.082
8615 0.00 0.0 0.0 0.0 0.000 0.000
8616 5.00 0.0 0.0 0.1 0.361 0.259
8617 0.50 0.0 0.0 0.1 0.127 0.088 FA_Poly_(g) Cholestrl_(mg)
0 3.043 215.0
1 3.012 219.0
2 3.694 256.0
3 0.800 75.0
4 0.784 94.0
5 0.826 100.0
6 0.724 72.0
7 0.830 93.0
8 1.433 102.0
9 0.870 103.0
10 0.953 95.0
11 0.123 17.0
12 0.124 13.0
13 0.003 7.0
14 0.083 12.0
15 0.031 4.0
16 1.437 110.0
17 0.665 89.0
18 0.591 89.0
19 1.654 116.0
20 0.938 94.0
21 0.657 114.0
22 1.733 110.0
23 0.495 90.0
24 0.899 89.0
25 0.765 79.0
26 0.778 89.0
27 0.472 64.0
28 0.861 65.0
29 0.661 96.0
... ... ...
8588 0.231 0.0
8589 0.041 0.0
8590 0.047 0.0
8591 5.024 0.0
8592 1.307 0.0
8593 3.572 0.0
8594 0.068 0.0
8595 0.180 35.0
8596 0.900 0.0
8597 0.509 54.0
8598 45.539 0.0
8599 33.033 0.0
8600 0.000 0.0
8601 7.536 6.0
8602 1.035 0.0
8603 0.131 0.0
8604 0.000 0.0
8605 0.008 0.0
8606 0.000 0.0
8607 0.009 0.0
8608 0.130 0.0
8609 0.050 0.0
8610 0.433 0.0
8611 0.810 0.0
8612 0.102 50.0
8613 6.210 95.0
8614 0.222 41.0
8615 0.000 0.0
8616 0.252 50.0
8617 0.170 50.0 [8618 rows x 36 columns]>
10.T
xxx.T可以对数据进行行列转换。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.T)
打印结果：
0 1 \
NDB_No 1001 1002
Shrt_Desc BUTTER WITH SALT BUTTER WHIPPED WITH SALT
Water_(g) 15.87 15.87
Energ_Kcal 717 717
Protein_(g) 0.85 0.85
Lipid_Tot_(g) 81.11 81.11
Ash_(g) 2.11 2.11
Carbohydrt_(g) 0.06 0.06
Fiber_TD_(g) 0 0
Sugar_Tot_(g) 0.06 0.06
Calcium_(mg) 24 24
Iron_(mg) 0.02 0.16
Magnesium_(mg) 2 2
Phosphorus_(mg) 24 23
Potassium_(mg) 24 26
Sodium_(mg) 643 659
Zinc_(mg) 0.09 0.05
Copper_(mg) 0 0.016
Manganese_(mg) 0 0.004
Selenium_(mcg) 1 1
Vit_C_(mg) 0 0
Thiamin_(mg) 0.005 0.005
Riboflavin_(mg) 0.034 0.034
Niacin_(mg) 0.042 0.042
Vit_B6_(mg) 0.003 0.003
Vit_B12_(mcg) 0.17 0.13
Vit_A_IU 2499 2499
Vit_A_RAE 684 684
Vit_E_(mg) 2.32 2.32
Vit_D_mcg 1.5 1.5
Vit_D_IU 60 60
Vit_K_(mcg) 7 7
FA_Sat_(g) 51.368 50.489
FA_Mono_(g) 21.021 23.426
FA_Poly_(g) 3.043 3.012
Cholestrl_(mg) 215 219 2 3 4 5 \
NDB_No 1003 1004 1005 1006
Shrt_Desc BUTTER OIL ANHYDROUS CHEESE BLUE CHEESE BRICK CHEESE BRIE
Water_(g) 0.24 42.41 41.11 48.42
Energ_Kcal 876 353 371 334
Protein_(g) 0.28 21.4 23.24 20.75
Lipid_Tot_(g) 99.48 28.74 29.68 27.68
Ash_(g) 0 5.11 3.18 2.7
Carbohydrt_(g) 0 2.34 2.79 0.45
Fiber_TD_(g) 0 0 0 0
Sugar_Tot_(g) 0 0.5 0.51 0.45
Calcium_(mg) 4 528 674 184
Iron_(mg) 0 0.31 0.43 0.5
Magnesium_(mg) 0 23 24 20
Phosphorus_(mg) 3 387 451 188
Potassium_(mg) 5 256 136 152
Sodium_(mg) 2 1146 560 629
Zinc_(mg) 0.01 2.66 2.6 2.38
Copper_(mg) 0.001 0.04 0.024 0.019
Manganese_(mg) 0 0.009 0.012 0.034
Selenium_(mcg) 0 14.5 14.5 14.5
Vit_C_(mg) 0 0 0 0
Thiamin_(mg) 0.001 0.029 0.014 0.07
Riboflavin_(mg) 0.005 0.382 0.351 0.52
Niacin_(mg) 0.003 1.016 0.118 0.38
Vit_B6_(mg) 0.001 0.166 0.065 0.235
Vit_B12_(mcg) 0.01 1.22 1.26 1.65
Vit_A_IU 3069 721 1080 592
Vit_A_RAE 840 198 292 174
Vit_E_(mg) 2.8 0.25 0.26 0.24
Vit_D_mcg 1.8 0.5 0.5 0.5
Vit_D_IU 73 21 22 20
Vit_K_(mcg) 8.6 2.4 2.5 2.3
FA_Sat_(g) 61.924 18.669 18.764 17.41
FA_Mono_(g) 28.732 7.778 8.598 8.013
FA_Poly_(g) 3.694 0.8 0.784 0.826
Cholestrl_(mg) 256 75 94 100 6 7 8 \
NDB_No 1007 1008 1009
Shrt_Desc CHEESE CAMEMBERT CHEESE CARAWAY CHEESE CHEDDAR
Water_(g) 51.8 39.28 37.1
Energ_Kcal 300 376 406
Protein_(g) 19.8 25.18 24.04
Lipid_Tot_(g) 24.26 29.2 33.82
Ash_(g) 3.68 3.28 3.71
Carbohydrt_(g) 0.46 3.06 1.33
Fiber_TD_(g) 0 0 0
Sugar_Tot_(g) 0.46 NaN 0.28
Calcium_(mg) 388 673 675
Iron_(mg) 0.33 0.64 0.16
Magnesium_(mg) 20 22 27
Phosphorus_(mg) 347 490 473
Potassium_(mg) 187 93 76
Sodium_(mg) 842 690 644
Zinc_(mg) 2.38 2.94 3.43
Copper_(mg) 0.021 0.024 0.056
Manganese_(mg) 0.038 0.021 0.033
Selenium_(mcg) 14.5 14.5 28.3
Vit_C_(mg) 0 0 0
Thiamin_(mg) 0.028 0.031 0.027
Riboflavin_(mg) 0.488 0.45 0.434
Niacin_(mg) 0.63 0.18 0.039
Vit_B6_(mg) 0.227 0.074 0.049
Vit_B12_(mcg) 1.3 0.27 0.88
Vit_A_IU 820 1054 994
Vit_A_RAE 241 271 263
Vit_E_(mg) 0.21 NaN 0.78
Vit_D_mcg 0.4 NaN 0.6
Vit_D_IU 18 NaN 24
Vit_K_(mcg) 2 NaN 2.9
FA_Sat_(g) 15.259 18.584 19.368
FA_Mono_(g) 7.023 8.275 8.428
FA_Poly_(g) 0.724 0.83 1.433
Cholestrl_(mg) 72 93 102 9 ... \
NDB_No 1010 ...
Shrt_Desc CHEESE CHESHIRE ...
Water_(g) 37.65 ...
Energ_Kcal 387 ...
Protein_(g) 23.37 ...
Lipid_Tot_(g) 30.6 ...
Ash_(g) 3.6 ...
Carbohydrt_(g) 4.78 ...
Fiber_TD_(g) 0 ...
Sugar_Tot_(g) NaN ...
Calcium_(mg) 643 ...
Iron_(mg) 0.21 ...
Magnesium_(mg) 21 ...
Phosphorus_(mg) 464 ...
Potassium_(mg) 95 ...
Sodium_(mg) 700 ...
Zinc_(mg) 2.79 ...
Copper_(mg) 0.042 ...
Manganese_(mg) 0.012 ...
Selenium_(mcg) 14.5 ...
Vit_C_(mg) 0 ...
Thiamin_(mg) 0.046 ...
Riboflavin_(mg) 0.293 ...
Niacin_(mg) 0.08 ...
Vit_B6_(mg) 0.074 ...
Vit_B12_(mcg) 0.83 ...
Vit_A_IU 985 ...
Vit_A_RAE 233 ...
Vit_E_(mg) NaN ...
Vit_D_mcg NaN ...
Vit_D_IU NaN ...
Vit_K_(mcg) NaN ...
FA_Sat_(g) 19.475 ...
FA_Mono_(g) 8.671 ...
FA_Poly_(g) 0.87 ...
Cholestrl_(mg) 103 ... 8608 \
NDB_No 44258
Shrt_Desc PUDDINGS CHOC FLAVOR LO CAL REG DRY MIX
Water_(g) 6.8
Energ_Kcal 365
Protein_(g) 10.08
Lipid_Tot_(g) 3
Ash_(g) 5.7
Carbohydrt_(g) 74.42
Fiber_TD_(g) 10.1
Sugar_Tot_(g) 0.7
Calcium_(mg) 50
Iron_(mg) 3.87
Magnesium_(mg) 110
Phosphorus_(mg) 174
Potassium_(mg) 570
Sodium_(mg) 3326
Zinc_(mg) 1.49
Copper_(mg) 0.854
Manganese_(mg) 0.887
Selenium_(mcg) 5.1
Vit_C_(mg) 0
Thiamin_(mg) 0.025
Riboflavin_(mg) 0.105
Niacin_(mg) 0.545
Vit_B6_(mg) 0.027
Vit_B12_(mcg) 0
Vit_A_IU 0
Vit_A_RAE 0
Vit_E_(mg) 0.02
Vit_D_mcg 0
Vit_D_IU 0
Vit_K_(mcg) 0.5
FA_Sat_(g) 1.578
FA_Mono_(g) 1.15
FA_Poly_(g) 0.13
Cholestrl_(mg) 0 8609 \
NDB_No 44259
Shrt_Desc PUDDINGS ALL FLAVORS XCPT CHOC LO CAL REG DRY MIX
Water_(g) 10.4
Energ_Kcal 351
Protein_(g) 1.6
Lipid_Tot_(g) 0.1
Ash_(g) 1.86
Carbohydrt_(g) 86.04
Fiber_TD_(g) 0.9
Sugar_Tot_(g) 2.9
Calcium_(mg) 49
Iron_(mg) 0.05
Magnesium_(mg) 17
Phosphorus_(mg) 12
Potassium_(mg) 18
Sodium_(mg) 1765
Zinc_(mg) 0.19
Copper_(mg) 0.04
Manganese_(mg) NaN
Selenium_(mcg) 0.9
Vit_C_(mg) 0
Thiamin_(mg) 0
Riboflavin_(mg) 0
Niacin_(mg) 0
Vit_B6_(mg) 0
Vit_B12_(mcg) 0
Vit_A_IU 0
Vit_A_RAE 0
Vit_E_(mg) 0.05
Vit_D_mcg 0
Vit_D_IU 0
Vit_K_(mcg) 1.1
FA_Sat_(g) 0.018
FA_Mono_(g) 0.032
FA_Poly_(g) 0.05
Cholestrl_(mg) 0 8610 \
NDB_No 44260
Shrt_Desc PUDDINGS ALL FLAVORS XCPT CHOC LO CAL INST DRY...
Water_(g) 6.84
Energ_Kcal 350
Protein_(g) 0.81
Lipid_Tot_(g) 0.9
Ash_(g) 6.8
Carbohydrt_(g) 84.66
Fiber_TD_(g) 0.8
Sugar_Tot_(g) 0.9
Calcium_(mg) 143
Iron_(mg) 0.38
Magnesium_(mg) 5
Phosphorus_(mg) 2368
Potassium_(mg) 30
Sodium_(mg) 3750
Zinc_(mg) 0.1
Copper_(mg) 0.038
Manganese_(mg) 0.041
Selenium_(mcg) 0.8
Vit_C_(mg) 0
Thiamin_(mg) 0.005
Riboflavin_(mg) 0.021
Niacin_(mg) 0.014
Vit_B6_(mg) 0.005
Vit_B12_(mcg) 0.05
Vit_A_IU 0
Vit_A_RAE 0
Vit_E_(mg) 0.08
Vit_D_mcg 0
Vit_D_IU 0
Vit_K_(mcg) 1.7
FA_Sat_(g) 0.099
FA_Mono_(g) 0.116
FA_Poly_(g) 0.433
Cholestrl_(mg) 0 8611 8612 8613 \
NDB_No 48052 80200 83110
Shrt_Desc VITAL WHEAT GLUTEN FROG LEGS RAW MACKEREL SALTED
Water_(g) 8.2 81.9 43
Energ_Kcal 370 73 305
Protein_(g) 75.16 16.4 18.5
Lipid_Tot_(g) 1.85 0.3 25.1
Ash_(g) 1 1.4 13.4
Carbohydrt_(g) 13.79 0 0
Fiber_TD_(g) 0.6 0 0
Sugar_Tot_(g) 0 0 0
Calcium_(mg) 142 18 66
Iron_(mg) 5.2 1.5 1.4
Magnesium_(mg) 25 20 60
Phosphorus_(mg) 260 147 254
Potassium_(mg) 100 285 520
Sodium_(mg) 29 58 4450
Zinc_(mg) 0.85 1 1.1
Copper_(mg) 0.182 0.25 0.1
Manganese_(mg) NaN NaN NaN
Selenium_(mcg) 39.7 14.1 73.4
Vit_C_(mg) 0 0 0
Thiamin_(mg) 0 0.14 0.02
Riboflavin_(mg) 0 0.25 0.19
Niacin_(mg) 0 1.2 3.3
Vit_B6_(mg) 0 0.12 0.41
Vit_B12_(mcg) 0 0.4 12
Vit_A_IU 0 50 157
Vit_A_RAE 0 15 47
Vit_E_(mg) 0 1 2.38
Vit_D_mcg 0 0.2 25.2
Vit_D_IU 0 8 1006
Vit_K_(mcg) 0 0.1 7.8
FA_Sat_(g) 0.272 0.076 7.148
FA_Mono_(g) 0.156 0.053 8.32
FA_Poly_(g) 0.81 0.102 6.21
Cholestrl_(mg) 0 50 95 8614 8615 8616 \
NDB_No 90240 90480 90560
Shrt_Desc SCALLOP (BAY&SEA) CKD STMD SYRUP CANE SNAIL RAW
Water_(g) 70.25 26 79.2
Energ_Kcal 111 269 90
Protein_(g) 20.54 0 16.1
Lipid_Tot_(g) 0.84 0 1.4
Ash_(g) 2.97 0.86 1.3
Carbohydrt_(g) 5.41 73.14 2
Fiber_TD_(g) 0 0 0
Sugar_Tot_(g) 0 73.2 0
Calcium_(mg) 10 13 10
Iron_(mg) 0.58 3.6 3.5
Magnesium_(mg) 37 10 250
Phosphorus_(mg) 426 8 272
Potassium_(mg) 314 63 382
Sodium_(mg) 667 58 70
Zinc_(mg) 1.55 0.19 1
Copper_(mg) 0.033 0.02 0.4
Manganese_(mg) 0.029 NaN NaN
Selenium_(mcg) 21.7 0.7 27.4
Vit_C_(mg) 0 0 0
Thiamin_(mg) 0.012 0.13 0.01
Riboflavin_(mg) 0.024 0.06 0.12
Niacin_(mg) 1.076 0.1 1.4
Vit_B6_(mg) 0.112 0 0.13
Vit_B12_(mcg) 2.15 0 0.5
Vit_A_IU 5 0 100
Vit_A_RAE 2 0 30
Vit_E_(mg) 0 0 5
Vit_D_mcg 0 0 0
Vit_D_IU 2 0 0
Vit_K_(mcg) 0 0 0.1
FA_Sat_(g) 0.218 0 0.361
FA_Mono_(g) 0.082 0 0.259
FA_Poly_(g) 0.222 0 0.252
Cholestrl_(mg) 41 0 50 8617
NDB_No 93600
Shrt_Desc TURTLE GREEN RAW
Water_(g) 78.5
Energ_Kcal 89
Protein_(g) 19.8
Lipid_Tot_(g) 0.5
Ash_(g) 1.2
Carbohydrt_(g) 0
Fiber_TD_(g) 0
Sugar_Tot_(g) 0
Calcium_(mg) 118
Iron_(mg) 1.4
Magnesium_(mg) 20
Phosphorus_(mg) 180
Potassium_(mg) 230
Sodium_(mg) 68
Zinc_(mg) 1
Copper_(mg) 0.25
Manganese_(mg) NaN
Selenium_(mcg) 16.8
Vit_C_(mg) 0
Thiamin_(mg) 0.12
Riboflavin_(mg) 0.15
Niacin_(mg) 1.1
Vit_B6_(mg) 0.12
Vit_B12_(mcg) 1
Vit_A_IU 100
Vit_A_RAE 30
Vit_E_(mg) 0.5
Vit_D_mcg 0
Vit_D_IU 0
Vit_K_(mcg) 0.1
FA_Sat_(g) 0.127
FA_Mono_(g) 0.088
FA_Poly_(g) 0.17
Cholestrl_(mg) 50 [36 rows x 8618 columns]
11.set_index
xxx.set_index 设置索引列。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.set_index('Energ_Kcal'))
打印结果：
NDB_No Shrt_Desc \
Energ_Kcal
717 1001 BUTTER WITH SALT
717 1002 BUTTER WHIPPED WITH SALT
876 1003 BUTTER OIL ANHYDROUS
353 1004 CHEESE BLUE
371 1005 CHEESE BRICK
334 1006 CHEESE BRIE
300 1007 CHEESE CAMEMBERT
376 1008 CHEESE CARAWAY
406 1009 CHEESE CHEDDAR
387 1010 CHEESE CHESHIRE
394 1011 CHEESE COLBY
98 1012 CHEESE COTTAGE CRMD LRG OR SML CURD
97 1013 CHEESE COTTAGE CRMD W/FRUIT
72 1014 CHEESE COTTAGE NONFAT UNCRMD DRY LRG OR SML CURD
81 1015 CHEESE COTTAGE LOWFAT 2% MILKFAT
72 1016 CHEESE COTTAGE LOWFAT 1% MILKFAT
342 1017 CHEESE CREAM
357 1018 CHEESE EDAM
264 1019 CHEESE FETA
389 1020 CHEESE FONTINA
466 1021 CHEESE GJETOST
356 1022 CHEESE GOUDA
413 1023 CHEESE GRUYERE
327 1024 CHEESE LIMBURGER
373 1025 CHEESE MONTEREY
300 1026 CHEESE MOZZARELLA WHL MILK
318 1027 CHEESE MOZZARELLA WHL MILK LO MOIST
254 1028 CHEESE MOZZARELLA PART SKIM MILK
301 1029 CHEESE MOZZARELLA LO MOIST PART-SKIM
368 1030 CHEESE MUENSTER
... ... ...
389 43544 BABYFOOD CRL RICE W/ PEARS & APPL DRY INST
91 43546 BABYFOOD BANANA NO TAPIOCA STR
68 43550 BABYFOOD BANANA APPL DSSRT STR
465 43566 SNACKS TORTILLA CHIPS LT (BAKED W/ LESS OIL)
401 43570 CEREALS RTE POST HONEY BUNCHES OF OATS HONEY RSTD
429 43572 POPCORN MICROWAVE LOFAT&NA
73 43585 BABYFOOD FRUIT SUPREME DSSRT
179 43589 CHEESE SWISS LOW FAT
377 43595 BREAKFAST BAR CORN FLAKE CRUST W/FRUIT
280 43597 CHEESE MOZZARELLA LO NA
688 43598 MAYONNAISE DRSNG NO CHOL
884 44005 OIL CORN PEANUT AND OLIVE
279 44018 SWEETENERS TABLETOP FRUCTOSE LIQ
257 44048 CHEESE FOOD IMITATION
319 44055 CELERY FLAKES DRIED
356 44061 PUDDINGS CHOC FLAVOR LO CAL INST DRY MIX
62 44074 BABYFOOD GRAPE JUC NO SUGAR CND
179 44110 JELLIES RED SUGAR HOME PRESERVED
181 44158 PIE FILLINGS BLUEBERRY CND
287 44203 COCKTAIL MIX NON-ALCOHOLIC CONCD FRZ
365 44258 PUDDINGS CHOC FLAVOR LO CAL REG DRY MIX
351 44259 PUDDINGS ALL FLAVORS XCPT CHOC LO CAL REG DRY MIX
350 44260 PUDDINGS ALL FLAVORS XCPT CHOC LO CAL INST DRY...
370 48052 VITAL WHEAT GLUTEN
73 80200 FROG LEGS RAW
305 83110 MACKEREL SALTED
111 90240 SCALLOP (BAY&SEA) CKD STMD
269 90480 SYRUP CANE
90 90560 SNAIL RAW
89 93600 TURTLE GREEN RAW Water_(g) Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \
Energ_Kcal
717 15.87 0.85 81.11 2.11 0.06
717 15.87 0.85 81.11 2.11 0.06
876 0.24 0.28 99.48 0.00 0.00
353 42.41 21.40 28.74 5.11 2.34
371 41.11 23.24 29.68 3.18 2.79
334 48.42 20.75 27.68 2.70 0.45
300 51.80 19.80 24.26 3.68 0.46
376 39.28 25.18 29.20 3.28 3.06
406 37.10 24.04 33.82 3.71 1.33
387 37.65 23.37 30.60 3.60 4.78
394 38.20 23.76 32.11 3.36 2.57
98 79.79 11.12 4.30 1.41 3.38
97 79.64 10.69 3.85 1.20 4.61
72 81.01 10.34 0.29 1.71 6.66
81 81.24 10.45 2.27 1.27 4.76
72 82.48 12.39 1.02 1.39 2.72
342 54.44 5.93 34.24 1.32 4.07
357 41.56 24.99 27.80 4.22 1.43
264 55.22 14.21 21.28 5.20 4.09
389 37.92 25.60 31.14 3.79 1.55
466 13.44 9.65 29.51 4.75 42.65
356 41.46 24.94 27.44 3.94 2.22
413 33.19 29.81 32.34 4.30 0.36
327 48.42 20.05 27.25 3.79 0.49
373 41.01 24.48 30.28 3.55 0.68
300 50.01 22.17 22.35 3.28 2.19
318 48.38 21.60 24.64 2.91 2.47
254 53.78 24.26 15.92 3.27 2.77
301 45.54 24.58 19.72 3.80 6.36
368 41.77 23.41 30.04 3.66 1.12
... ... ... ... ... ...
389 2.00 6.60 0.90 2.00 88.60
91 76.70 1.00 0.20 0.76 21.34
68 83.10 0.30 0.20 0.29 16.30
465 1.30 8.70 15.20 1.85 73.40
401 5.00 7.12 5.46 1.22 81.19
429 2.80 12.60 9.50 1.71 73.39
73 81.60 0.50 0.20 0.52 17.18
179 59.60 28.40 5.10 3.50 3.40
377 14.50 4.40 7.50 0.80 72.90
280 49.90 27.50 17.10 2.40 3.10
688 21.70 0.00 77.80 0.40 0.30
884 0.00 0.00 100.00 0.00 0.00
279 23.90 0.00 0.00 0.00 76.10
257 55.50 4.08 19.50 4.74 16.18
319 9.00 11.30 2.10 13.90 63.70
356 4.20 5.30 2.40 9.90 78.20
62 84.40 0.00 0.00 0.22 15.38
179 53.00 0.30 0.03 0.08 46.10
181 54.66 0.41 0.20 0.35 44.38
287 28.24 0.08 0.01 0.07 71.60
365 6.80 10.08 3.00 5.70 74.42
351 10.40 1.60 0.10 1.86 86.04
350 6.84 0.81 0.90 6.80 84.66
370 8.20 75.16 1.85 1.00 13.79
73 81.90 16.40 0.30 1.40 0.00
305 43.00 18.50 25.10 13.40 0.00
111 70.25 20.54 0.84 2.97 5.41
269 26.00 0.00 0.00 0.86 73.14
90 79.20 16.10 1.40 1.30 2.00
89 78.50 19.80 0.50 1.20 0.00 Fiber_TD_(g) Sugar_Tot_(g) Calcium_(mg) ... \
Energ_Kcal ...
717 0.0 0.06 24.0 ...
717 0.0 0.06 24.0 ...
876 0.0 0.00 4.0 ...
353 0.0 0.50 528.0 ...
371 0.0 0.51 674.0 ...
334 0.0 0.45 184.0 ...
300 0.0 0.46 388.0 ...
376 0.0 NaN 673.0 ...
406 0.0 0.28 675.0 ...
387 0.0 NaN 643.0 ...
394 0.0 0.52 685.0 ...
98 0.0 2.67 83.0 ...
97 0.2 2.38 53.0 ...
72 0.0 1.85 86.0 ...
81 0.0 4.00 111.0 ...
72 0.0 2.72 61.0 ...
342 0.0 3.21 98.0 ...
357 0.0 1.43 731.0 ...
264 0.0 4.09 493.0 ...
389 0.0 1.55 550.0 ...
466 0.0 NaN 400.0 ...
356 0.0 2.22 700.0 ...
413 0.0 0.36 1011.0 ...
327 0.0 0.49 497.0 ...
373 0.0 0.50 746.0 ...
300 0.0 1.03 505.0 ...
318 0.0 1.01 575.0 ...
254 0.0 1.13 782.0 ...
301 0.0 2.24 716.0 ...
368 0.0 1.12 717.0 ...
... ... ... ... ...
389 2.6 1.35 38.0 ...
91 1.6 11.36 4.0 ...
68 1.0 14.66 3.0 ...
465 5.7 0.53 159.0 ...
401 4.2 19.79 23.0 ...
429 14.2 0.54 11.0 ...
73 2.0 14.87 6.0 ...
179 0.0 1.33 961.0 ...
377 2.1 35.10 41.0 ...
280 0.0 1.23 731.0 ...
688 0.0 0.30 7.0 ...
884 0.0 0.00 0.0 ...
279 0.1 76.00 1.0 ...
257 0.0 8.21 649.0 ...
319 27.8 35.90 587.0 ...
356 6.1 0.70 126.0 ...
62 0.1 NaN 12.0 ...
179 0.8 45.30 5.0 ...
181 2.6 37.75 27.0 ...
287 0.0 24.53 2.0 ...
365 10.1 0.70 50.0 ...
351 0.9 2.90 49.0 ...
350 0.8 0.90 143.0 ...
370 0.6 0.00 142.0 ...
73 0.0 0.00 18.0 ...
305 0.0 0.00 66.0 ...
111 0.0 0.00 10.0 ...
269 0.0 73.20 13.0 ...
90 0.0 0.00 10.0 ...
89 0.0 0.00 118.0 ... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU Vit_K_(mcg) \
Energ_Kcal
717 2499.0 684.0 2.32 1.5 60.0 7.0
717 2499.0 684.0 2.32 1.5 60.0 7.0
876 3069.0 840.0 2.80 1.8 73.0 8.6
353 721.0 198.0 0.25 0.5 21.0 2.4
371 1080.0 292.0 0.26 0.5 22.0 2.5
334 592.0 174.0 0.24 0.5 20.0 2.3
300 820.0 241.0 0.21 0.4 18.0 2.0
376 1054.0 271.0 NaN NaN NaN NaN
406 994.0 263.0 0.78 0.6 24.0 2.9
387 985.0 233.0 NaN NaN NaN NaN
394 994.0 264.0 0.28 0.6 24.0 2.7
98 140.0 37.0 0.08 0.1 3.0 0.0
97 146.0 38.0 0.04 0.0 0.0 0.4
72 8.0 2.0 0.01 0.0 0.0 0.0
81 225.0 68.0 0.08 0.0 0.0 0.0
72 41.0 11.0 0.01 0.0 0.0 0.1
342 1343.0 366.0 0.29 0.6 25.0 2.9
357 825.0 243.0 0.24 0.5 20.0 2.3
264 422.0 125.0 0.18 0.4 16.0 1.8
389 913.0 261.0 0.27 0.6 23.0 2.6
466 1113.0 334.0 NaN NaN NaN NaN
356 563.0 165.0 0.24 0.5 20.0 2.3
413 948.0 271.0 0.28 0.6 24.0 2.7
327 1155.0 340.0 0.23 0.5 20.0 2.3
373 769.0 198.0 0.26 0.6 22.0 2.5
300 676.0 179.0 0.19 0.4 16.0 2.3
318 745.0 197.0 0.21 0.5 18.0 2.5
254 481.0 127.0 0.14 0.3 12.0 1.6
301 846.0 254.0 0.43 0.4 15.0 1.3
368 1012.0 298.0 0.26 0.6 22.0 2.5
... ... ... ... ... ... ...
389 0.0 0.0 0.13 0.0 0.0 0.3
91 5.0 0.0 0.25 0.0 0.0 0.5
68 30.0 2.0 0.02 0.0 0.0 0.1
465 81.0 4.0 3.53 0.0 0.0 0.7
401 2731.0 806.0 1.22 4.6 183.0 3.0
429 147.0 7.0 5.01 0.0 0.0 15.7
73 50.0 3.0 0.79 0.0 0.0 5.1
179 152.0 40.0 0.07 0.1 4.0 0.5
377 2027.0 608.0 0.76 0.0 0.0 13.8
280 517.0 137.0 0.15 0.3 13.0 1.8
688 0.0 0.0 11.79 0.0 0.0 24.7
884 0.0 0.0 14.78 0.0 0.0 21.0
279 0.0 0.0 0.00 0.0 0.0 0.0
257 900.0 45.0 2.15 0.0 0.0 36.7
319 1962.0 98.0 5.55 0.0 0.0 584.2
356 0.0 0.0 0.02 0.0 0.0 0.4
62 8.0 NaN NaN NaN NaN NaN
179 3.0 0.0 0.00 0.0 0.0 0.2
181 22.0 1.0 0.23 0.0 0.0 3.9
287 12.0 1.0 0.02 0.0 0.0 0.0
365 0.0 0.0 0.02 0.0 0.0 0.5
351 0.0 0.0 0.05 0.0 0.0 1.1
350 0.0 0.0 0.08 0.0 0.0 1.7
370 0.0 0.0 0.00 0.0 0.0 0.0
73 50.0 15.0 1.00 0.2 8.0 0.1
305 157.0 47.0 2.38 25.2 1006.0 7.8
111 5.0 2.0 0.00 0.0 2.0 0.0
269 0.0 0.0 0.00 0.0 0.0 0.0
90 100.0 30.0 5.00 0.0 0.0 0.1
89 100.0 30.0 0.50 0.0 0.0 0.1 FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
Energ_Kcal
717 51.368 21.021 3.043 215.0
717 50.489 23.426 3.012 219.0
876 61.924 28.732 3.694 256.0
353 18.669 7.778 0.800 75.0
371 18.764 8.598 0.784 94.0
334 17.410 8.013 0.826 100.0
300 15.259 7.023 0.724 72.0
376 18.584 8.275 0.830 93.0
406 19.368 8.428 1.433 102.0
387 19.475 8.671 0.870 103.0
394 20.218 9.280 0.953 95.0
98 1.718 0.778 0.123 17.0
97 2.311 1.036 0.124 13.0
72 0.169 0.079 0.003 7.0
81 1.235 0.516 0.083 12.0
72 0.645 0.291 0.031 4.0
342 19.292 8.620 1.437 110.0
357 17.572 8.125 0.665 89.0
264 14.946 4.623 0.591 89.0
389 19.196 8.687 1.654 116.0
466 19.160 7.879 0.938 94.0
356 17.614 7.747 0.657 114.0
413 18.913 10.043 1.733 110.0
327 16.746 8.606 0.495 90.0
373 19.066 8.751 0.899 89.0
300 13.152 6.573 0.765 79.0
318 15.561 7.027 0.778 89.0
254 10.114 4.510 0.472 64.0
301 11.473 5.104 0.861 65.0
368 19.113 8.711 0.661 96.0
... ... ... ... ...
389 0.185 0.252 0.231 0.0
91 0.072 0.028 0.041 0.0
68 0.058 0.018 0.047 0.0
465 2.837 6.341 5.024 0.0
401 0.600 2.831 1.307 0.0
429 1.415 4.085 3.572 0.0
73 0.030 0.025 0.068 0.0
179 3.304 1.351 0.180 35.0
377 1.500 5.000 0.900 0.0
280 10.867 4.844 0.509 54.0
688 10.784 18.026 45.539 0.0
884 14.367 48.033 33.033 0.0
279 0.000 0.000 0.000 0.0
257 7.996 3.108 7.536 6.0
319 0.555 0.405 1.035 0.0
356 0.984 1.154 0.131 0.0
62 0.000 0.000 0.000 0.0
179 0.009 0.001 0.008 0.0
181 0.000 0.000 0.000 0.0
287 0.003 0.001 0.009 0.0
365 1.578 1.150 0.130 0.0
351 0.018 0.032 0.050 0.0
350 0.099 0.116 0.433 0.0
370 0.272 0.156 0.810 0.0
73 0.076 0.053 0.102 50.0
305 7.148 8.320 6.210 95.0
111 0.218 0.082 0.222 41.0
269 0.000 0.000 0.000 0.0
90 0.361 0.259 0.252 50.0
89 0.127 0.088 0.170 50.0 [8618 rows x 35 columns]
12.sort_values
xxx.sort_values 按照特定列的值排序。
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.sort_values(by='Vit_D_IU'))
打印结果：
NDB_No Shrt_Desc Water_(g) \
3689 12109 COCONUT MEAT DRIED (DESICCATED) SWTND FLAKED P... 15.46
4167 14026 BEVERAGES ENERGY DRK SUGAR-FREE W/ GUARANA 98.35
4169 14028 WHISKEY SOUR MIX BTLD 78.20
4171 14030 BEVERAGES ENERGY DRK ORIGINAL GRAPE LOADED CHE... 88.45
4172 14031 BEVERAGES H2O BTLD YUMBERRY POMEGRANATE W/ ANT... 98.75
4173 14033 BEVERAGES ABBOTT EAS WHEY PROT PDR 6.61
4174 14034 ALCOHOLIC BEV CREME DE MENTHE 72 PROOF 28.30
4175 14035 BEVERAGES ABBOTT EAS SOY PROT PDR 2.83
4177 14037 ALCOHOLIC BEV DISTILLED ALL (GIN RUM VODKA WHI... 66.60
4178 14038 BEVERAGES OCEAN SPRAY CRAN-ENERGY CRANBERRY EN... 96.18
4180 14042 BEVERAGES FORT LO CAL FRUIT JUC BEV 97.21
4185 14049 ALCOHOLIC BEV DISTILLED GIN 90 PROOF 62.10
4186 14050 ALCOHOLIC BEV DISTILLED RUM 80 PROOF 66.60
4191 14057 ALCOHOLIC BEV WINE DSSRT SWT 70.51
4164 14022 BEVERAGES MONSTER ENERGY DRK LO CARB 98.35
4192 14058 BEVERAGES WHEY PROT PDR ISOLATE 0.86
4195 14061 BEVERAGES ENERGY DRK SUGAR FREE 99.11
4197 14063 BEVERAGES CHOC PDR NO SUGAR ADDED 7.40
4198 14064 BEVERAGES ORANGE JUC LT NO PULP 94.19
4199 14065 BEVERAGES HI-C FLASHIN' FRUIT PUNCH 87.49
4200 14066 BEVERAGES PROT PDR WHEY BSD 3.44
4201 14067 BEVERAGES PROT PDR SOY BSD 4.13
4202 14068 BEVERAGES KELLOGG'S SPL K20 PROT H2O MIX 3.95
4206 14073 BEVERAGES ZEVIA COLA 98.36
4207 14074 BEVERAGES ZEVIA COLA CAFFEINE FREE 98.87
4208 14075 BEVERAGES H2O BTLD NATURALLY SPARKLING (CARBON... 99.95
4209 14076 BEVERAGES ICELANDIC GLACIAL NAT SPRING H2O 100.00
4213 14082 BEVERAGES GEROLSTEINER BRUNNEN GMBH H2O BTLD N... 99.95
4215 14084 ALCOHOLIC BEV WINE TABLE ALL 86.58
4194 14060 BEVERAGES ENERGY DRK W/ CARB H2O & HI FRUCTOSE... 84.52
... ... ... ...
8345 36408 RESTAURANT LATINO PUPUSAS CON FRIJOLES (PUPUSA... 52.16
8350 36413 RESTAURANT LATINO BLACK BEAN SOUP 75.91
8351 36414 RESTAURANT LATINO TRIPE SOUP 83.41
8352 36415 RESTAURANT LATINO AREPA (UNLEAVENED CORNMEAL B... 50.80
8353 36416 RESTAURANT LATINO BUNUELOS (FRIED YEAST BREAD) 15.30
8354 36417 RESTAURANT MEXICAN SPANISH RICE 58.54
8355 36418 RESTAURANT MEXICAN REFRIED BNS 67.57
8356 36601 RESTAURANT CHINESE EGG ROLLS ASSORTED 50.60
8359 36604 CRACKER BARREL CHICK TENDERLOIN PLATTER FRIED ... 42.61
8360 36605 CRACKER BARREL COUNTRY FRIED SHRIMP PLATTER 46.08
8361 36606 CRACKER BARREL FARM RAISED CATFISH PLATTER 52.32
8362 36607 CRACKER BARREL STEAK FRIES 51.32
8363 36608 CRACKER BARREL GRILLED SIRLOIN STEAK 59.40
8364 36609 CRACKER BARREL MACARONI N' CHS PLATE FROM KID'... 64.80
8365 36610 DENNY'S FRENCH FR 46.05
8366 36611 DENNY'S MOZZARELLA CHS STKS 37.63
8367 36612 DENNY'S GOLDEN FRIED SHRIMP 41.05
8368 36613 DENNY'S MACARONI & CHS FROM KID'S MENU 67.38
8369 36614 DENNY'S CHICK NUGGETS STAR SHAPED FROM KID'S MENU 39.25
8370 36615 DENNY'S TOP SIRLOIN STEAK 61.82
8374 36620 RESTAURANT CHINESE SHRIMP & VEG 84.06
8376 36622 RESTAURANT CHINESE SWT & SOUR PORK 50.84
8377 36623 RESTAURANT CHINESE CHICK CHOW MEIN 81.01
8382 36630 RESTAURANT ITALIAN SPAGHETTI W/ MEAT SAU 73.16
8383 36631 OLIVE GARDEN SPAGHETTI W/ MEAT SAU 72.75
8384 36632 CARRABBA'S ITALIAN GRILL SPAGHETTI W/ MEAT SAU 73.32
8445 42270 ORANGE JUICE DRINK 86.20
8454 42286 BABYFOOD GRN BNS&TURKEY STR 87.50
8527 43297 PORK ORIENTAL STYLE DEHYD 21.80
8604 44074 BABYFOOD GRAPE JUC NO SUGAR CND 84.40 Energ_Kcal Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \
3689 456 3.13 27.99 1.57 51.85
4167 4 0.00 0.00 0.62 1.03
4169 87 0.10 0.10 0.20 21.40
4171 44 0.00 0.00 0.28 11.25
4172 5 0.00 0.00 0.00 1.25
4173 385 66.67 5.13 3.65 17.95
4174 371 0.00 0.30 0.01 41.60
4175 405 47.62 3.57 2.03 43.94
4177 231 0.00 0.00 0.01 0.00
4178 15 0.00 0.00 0.11 3.75
4180 4 0.00 0.00 2.09 0.70
4185 263 0.00 0.00 0.00 0.00
4186 231 0.00 0.00 0.00 0.00
4191 160 0.20 0.00 0.30 13.69
4164 5 0.00 0.00 0.40 1.38
4192 359 58.14 1.16 10.77 29.07
4195 4 0.42 0.00 0.06 0.42
4197 373 9.09 9.09 10.70 63.64
4198 21 0.21 0.00 0.19 5.42
4199 45 0.00 0.00 0.01 12.50
4200 352 78.13 1.56 10.55 6.25
4201 388 55.56 5.56 5.87 28.89
4202 380 35.20 0.60 1.85 58.40
4206 0 0.00 0.00 0.00 1.13
4207 0 0.00 0.00 0.01 1.13
4208 0 0.00 0.00 0.05 0.00
4209 0 0.00 0.00 0.00 0.00
4213 0 0.00 0.00 0.05 0.00
4215 83 0.07 0.00 0.24 2.72
4194 62 0.42 0.00 0.06 15.00
... ... ... ... ... ...
8345 229 5.59 9.01 1.74 31.49
8350 103 5.10 2.57 1.63 14.79
8351 74 8.61 2.58 1.33 4.07
8352 219 5.48 5.38 1.19 37.14
8353 462 8.02 26.24 1.87 48.57
8354 185 3.28 5.29 1.72 31.16
8355 156 6.91 6.77 1.96 16.79
8356 250 8.28 11.94 1.89 27.29
8359 294 18.67 15.41 3.08 20.24
8360 287 12.62 16.77 3.13 21.40
8361 266 22.94 17.05 2.38 5.31
8362 255 3.26 13.18 1.37 30.87
8363 203 31.52 8.52 1.64 0.00
8364 192 6.46 11.51 1.64 15.58
8365 282 3.41 14.13 1.20 35.20
8366 324 13.56 17.87 3.72 27.22
8367 319 13.88 20.01 4.14 20.93
8368 150 5.19 4.92 1.35 21.16
8369 377 16.27 28.57 2.33 13.59
8370 182 28.90 7.34 1.80 0.14
8374 78 5.90 4.05 1.47 4.52
8376 270 8.91 15.66 1.25 23.34
8377 85 6.76 2.80 1.13 8.29
8382 121 5.79 3.59 1.06 16.40
8383 121 5.80 3.28 0.98 17.19
8384 122 5.87 3.92 1.18 15.71
8445 54 0.20 0.00 0.19 13.41
8454 51 4.10 1.50 1.55 5.35
8527 615 11.80 62.40 2.60 1.40
8604 62 0.00 0.00 0.22 15.38 Fiber_TD_(g) Sugar_Tot_(g) ... Vit_A_IU Vit_A_RAE \
3689 9.9 36.75 ... 0.0 0.0
4167 0.0 0.00 ... 0.0 0.0
4169 0.0 21.40 ... 0.0 0.0
4171 0.0 11.25 ... 0.0 0.0
4172 0.0 0.00 ... 0.0 0.0
4173 0.0 5.13 ... 54.0 15.0
4174 0.0 41.60 ... 0.0 0.0
4175 0.0 40.48 ... 0.0 0.0
4177 0.0 0.00 ... 0.0 0.0
4178 0.0 3.75 ... 0.0 0.0
4180 0.0 0.63 ... 400.0 20.0
4185 0.0 0.00 ... 0.0 0.0
4186 0.0 0.00 ... 0.0 0.0
4191 0.0 7.78 ... 0.0 0.0
4164 0.0 1.38 ... 0.0 0.0
4192 0.0 1.16 ... 2909.0 872.0
4195 0.0 0.00 ... 0.0 0.0
4197 9.1 27.27 ... 0.0 0.0
4198 0.0 4.17 ... 208.0 10.0
4199 0.0 12.50 ... 0.0 0.0
4200 3.1 0.00 ... 0.0 0.0
4201 6.7 22.22 ... 0.0 0.0
4202 37.5 2.00 ... 8.0 2.0
4206 0.0 0.00 ... 0.0 0.0
4207 0.0 0.00 ... 0.0 0.0
4208 0.0 0.00 ... 0.0 0.0
4209 0.0 0.00 ... 0.0 0.0
4213 0.0 0.00 ... 0.0 0.0
4215 0.0 0.79 ... 0.0 0.0
4194 0.0 13.75 ... 0.0 0.0
... ... ... ... ... ...
8345 5.8 1.30 ... NaN NaN
8350 4.9 0.89 ... 2.0 1.0
8351 NaN NaN ... 0.0 0.0
8352 2.6 0.87 ... 213.0 61.0
8353 1.5 12.24 ... NaN NaN
8354 1.2 1.30 ... 100.0 6.0
8355 8.0 0.78 ... 37.0 11.0
8356 2.6 NaN ... NaN NaN
8359 1.0 0.19 ... 10.0 2.0
8360 0.3 NaN ... 5.0 1.0
8361 1.6 NaN ... 0.0 0.0
8362 3.5 0.86 ... NaN NaN
8363 NaN NaN ... 23.0 7.0
8364 0.7 2.83 ... 254.0 67.0
8365 3.5 0.85 ... NaN NaN
8366 1.6 2.83 ... 343.0 96.0
8367 1.5 0.75 ... 6.0 2.0
8368 1.2 4.20 ... 58.0 15.0
8369 0.8 0.00 ... 58.0 17.0
8370 NaN NaN ... NaN NaN
8374 1.4 2.16 ... 1320.0 66.0
8376 1.0 10.34 ... 553.0 29.0
8377 1.0 1.74 ... 362.0 19.0
8382 1.6 1.82 ... 232.0 12.0
8383 1.7 1.67 ... 191.0 10.0
8384 1.5 1.96 ... 272.0 14.0
8445 0.2 9.36 ... 44.0 2.0
8454 1.4 1.39 ... 629.0 31.0
8527 0.0 0.00 ... 0.0 0.0
8604 0.1 NaN ... 8.0 NaN Vit_E_(mg) Vit_D_mcg Vit_D_IU Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) \
3689 0.00 0.0 0.0 0.0 26.396 1.377
4167 0.00 0.0 0.0 0.0 0.000 0.000
4169 0.00 0.0 0.0 0.0 0.008 0.002
4171 0.00 0.0 0.0 0.0 0.000 0.000
4172 0.56 0.0 0.0 0.0 0.000 0.000
4173 0.38 0.0 0.0 0.5 2.564 0.281
4174 0.00 0.0 0.0 0.0 0.014 0.015
4175 0.12 0.0 0.0 2.7 0.000 0.644
4177 0.00 0.0 0.0 0.0 0.000 0.000
4178 0.00 0.0 0.0 0.1 0.000 0.000
4180 1.14 0.0 0.0 0.1 0.000 0.000
4185 0.00 0.0 0.0 0.0 0.000 0.000
4186 0.00 0.0 0.0 0.0 0.000 0.000
4191 0.00 0.0 0.0 0.0 0.000 0.000
4164 0.00 0.0 0.0 0.0 0.000 0.000
4192 7.85 0.0 0.0 46.5 0.581 0.149
4195 0.00 0.0 0.0 0.0 0.000 0.000
4197 0.00 0.0 0.0 0.0 4.545 2.119
4198 1.25 0.0 0.0 0.0 0.000 0.000
4199 0.00 0.0 0.0 0.0 0.000 0.000
4200 0.00 0.0 0.0 0.0 0.781 0.158
4201 0.00 0.0 0.0 0.0 1.111 1.057
4202 0.00 0.0 0.0 0.0 0.236 0.146
4206 0.00 0.0 0.0 0.0 0.000 0.000
4207 0.00 0.0 0.0 0.0 0.000 0.000
4208 0.00 0.0 0.0 0.0 0.000 0.000
4209 0.00 0.0 0.0 0.0 0.000 0.000
4213 0.00 0.0 0.0 0.0 0.000 0.000
4215 0.00 0.0 0.0 0.0 0.000 0.000
4194 0.00 0.0 0.0 0.0 0.000 0.000
... ... ... ... ... ... ...
8345 0.36 NaN NaN 7.4 2.188 2.986
8350 0.07 NaN NaN 6.0 0.535 1.035
8351 0.38 NaN NaN 2.3 1.045 1.124
8352 0.29 NaN NaN 3.5 2.902 1.514
8353 0.98 NaN NaN 25.8 6.834 9.415
8354 0.60 NaN NaN 13.0 1.005 1.511
8355 0.45 NaN NaN 13.3 1.840 1.834
8356 NaN NaN NaN 58.9 2.116 3.036
8359 1.27 NaN NaN 33.3 2.830 3.426
8360 1.88 NaN NaN NaN 3.064 3.787
8361 NaN NaN NaN 24.7 3.249 4.577
8362 1.34 NaN NaN 32.1 2.369 3.156
8363 0.46 NaN NaN 1.0 3.045 3.405
8364 0.75 NaN NaN 9.9 4.197 2.824
8365 0.98 NaN NaN 28.8 2.534 3.408
8366 0.74 NaN NaN 25.4 6.643 4.287
8367 2.61 NaN NaN 35.3 3.515 4.772
8368 0.53 NaN NaN 3.0 1.384 2.046
8369 1.91 NaN NaN 36.6 5.606 9.817
8370 NaN NaN NaN 1.0 2.595 2.840
8374 0.99 NaN NaN 52.0 0.633 0.817
8376 0.89 NaN NaN 27.9 2.680 3.527
8377 0.43 NaN NaN 22.0 0.490 0.613
8382 0.63 NaN NaN 4.2 1.062 1.486
8383 0.58 NaN NaN 4.3 1.024 1.242
8384 0.68 NaN NaN 3.3 1.100 1.731
8445 0.02 NaN NaN 0.0 0.000 0.010
8454 0.45 NaN NaN 14.8 0.500 0.270
8527 0.36 NaN NaN 0.0 23.056 28.555
8604 NaN NaN NaN NaN 0.000 0.000 FA_Poly_(g) Cholestrl_(mg)
3689 0.222 0.0
4167 0.000 0.0
4169 0.020 0.0
4171 0.000 0.0
4172 0.000 0.0
4173 0.926 205.0
4174 0.167 0.0
4175 1.956 0.0
4177 0.000 0.0
4178 0.000 0.0
4180 0.000 0.0
4185 0.000 0.0
4186 0.000 0.0
4191 0.000 0.0
4164 0.000 0.0
4192 0.021 12.0
4195 0.000 0.0
4197 1.831 0.0
4198 0.000 0.0
4199 0.000 0.0
4200 0.299 16.0
4201 2.701 0.0
4202 0.400 4.0
4206 0.000 0.0
4207 0.000 0.0
4208 0.000 0.0
4209 0.000 0.0
4213 0.000 0.0
4215 0.000 0.0
4194 0.000 0.0
... ... ...
8345 2.907 NaN
8350 0.787 1.0
8351 0.310 59.0
8352 0.989 5.0
8353 7.090 NaN
8354 2.317 0.0
8355 2.344 5.0
8356 5.601 16.0
8359 8.142 42.0
8360 8.519 89.0
8361 7.612 67.0
8362 6.833 0.0
8363 0.742 87.0
8364 3.925 16.0
8365 6.548 0.0
8366 5.104 32.0
8367 9.516 83.0
8368 1.010 7.0
8369 10.701 57.0
8370 0.672 82.0
8374 1.984 36.0
8376 7.116 24.0
8377 1.226 16.0
8382 0.512 9.0
8383 0.530 8.0
8384 0.494 9.0
8445 0.010 0.0
8454 0.420 11.0
8527 7.320 67.0
8604 0.000 0.0 [8618 rows x 36 columns]
13.loc
xxx.loc按索引提取单行的数值
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.loc[3])
打印结果：
NDB_No 1004
Shrt_Desc CHEESE BLUE
Water_(g) 42.41
Energ_Kcal 353
Protein_(g) 21.4
Lipid_Tot_(g) 28.74
Ash_(g) 5.11
Carbohydrt_(g) 2.34
Fiber_TD_(g) 0
Sugar_Tot_(g) 0.5
Calcium_(mg) 528
Iron_(mg) 0.31
Magnesium_(mg) 23
Phosphorus_(mg) 387
Potassium_(mg) 256
Sodium_(mg) 1146
Zinc_(mg) 2.66
Copper_(mg) 0.04
Manganese_(mg) 0.009
Selenium_(mcg) 14.5
Vit_C_(mg) 0
Thiamin_(mg) 0.029
Riboflavin_(mg) 0.382
Niacin_(mg) 1.016
Vit_B6_(mg) 0.166
Vit_B12_(mcg) 1.22
Vit_A_IU 721
Vit_A_RAE 198
Vit_E_(mg) 0.25
Vit_D_mcg 0.5
Vit_D_IU 21
Vit_K_(mcg) 2.4
FA_Sat_(g) 18.669
FA_Mono_(g) 7.778
FA_Poly_(g) 0.8
Cholestrl_(mg) 75
Name: 3, dtype: object
14.iloc
xxx.iloc 按索引提取区域行数值
举个例子：
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info.iloc[0:5])
打印结果：
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
0 1001 BUTTER WITH SALT 15.87 717 0.85
1 1002 BUTTER WHIPPED WITH SALT 15.87 717 0.85
2 1003 BUTTER OIL ANHYDROUS 0.24 876 0.28
3 1004 CHEESE BLUE 42.41 353 21.40
4 1005 CHEESE BRICK 41.11 371 23.24 Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) \
0 81.11 2.11 0.06 0.0 0.06
1 81.11 2.11 0.06 0.0 0.06
2 99.48 0.00 0.00 0.0 0.00
3 28.74 5.11 2.34 0.0 0.50
4 29.68 3.18 2.79 0.0 0.51 ... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU \
0 ... 2499.0 684.0 2.32 1.5 60.0
1 ... 2499.0 684.0 2.32 1.5 60.0
2 ... 3069.0 840.0 2.80 1.8 73.0
3 ... 721.0 198.0 0.25 0.5 21.0
4 ... 1080.0 292.0 0.26 0.5 22.0 Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
0 7.0 51.368 21.021 3.043 215.0
1 7.0 50.489 23.426 3.012 219.0
2 8.6 61.924 28.732 3.694 256.0
3 2.4 18.669 7.778 0.800 75.0
4 2.5 18.764 8.598 0.784 94.0 [5 rows x 36 columns]
以上是我在运用中所用到的一些函数及用法，欢迎大家指正批评，如果有需要改进的地方，还希望不吝赐教，如果觉得本文对你有用，别忘记关注订阅推荐博主，谢谢大家的支持！！！
扩展阅读
- pandas用法大全：https://blog.csdn.net/liufang0001/article/details/77856255
- 十分钟搞定pandas：http://pandas.pydata.org/pandas-docs/stable/10min.html