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
您可以考虑给博主来个小小的打赏以资鼓励，您的肯定将是我最大的动力。thx.
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作 者： Angel_Kitty
出 处：http://www.cnblogs.com/ECJTUACM-873284962/
关于作者：潜心机器学习以及信息安全的综合研究。如有问题或建议，请多多赐教！
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