Pandas 基础学习
2024-06-07 21:02:57
加载数据
Fun：pandas.read_csv
>>> import pandas
>>> food_info = pandas.read_csv("food_info.csv")
>>> print(food_info.dtypes)
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
>>> print(type(food_info))
<class 'pandas.core.frame.DataFrame'>
取数据的头和尾
头：head
food_info.head(1)
尾：tail
food_info.tail(1)
shape
>>> food_info.shape
(8618, 36)
取数据
指定行数据
>>> print(food_info.loc[0])
NDB_No 1001
Shrt_Desc BUTTER WITH SALT
Water_(g) 15.87
Energ_Kcal 717
Protein_(g) 0.85
Lipid_Tot_(g) 81.11
Ash_(g) 2.11
Carbohydrt_(g) 0.06
Fiber_TD_(g) 0
Sugar_Tot_(g) 0.06
Calcium_(mg) 24
Iron_(mg) 0.02
Magnesium_(mg) 2
Phosphorus_(mg) 24
Potassium_(mg) 24
Sodium_(mg) 643
Zinc_(mg) 0.09
Copper_(mg) 0
Manganese_(mg) 0
Selenium_(mcg) 1
Vit_C_(mg) 0
Thiamin_(mg) 0.005
Riboflavin_(mg) 0.034
Niacin_(mg) 0.042
Vit_B6_(mg) 0.003
Vit_B12_(mcg) 0.17
Vit_A_IU 2499
Vit_A_RAE 684
Vit_E_(mg) 2.32
Vit_D_mcg 1.5
Vit_D_IU 60
Vit_K_(mcg) 7
FA_Sat_(g) 51.368
FA_Mono_(g) 21.021
FA_Poly_(g) 3.043
Cholestrl_(mg) 215
Name: 0, dtype: object
取范围数据
>>> print(food_info.loc[1:2])NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
1 1002 BUTTER WHIPPED WITH SALT 15.87 717 0.85
2 1003 BUTTER OIL ANHYDROUS 0.24 876 0.28Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) \
1 81.11 2.11 0.06 0.0 0.06
2 99.48 0.00 0.00 0.0 0.00... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU \
1 ... 2499.0 684.0 2.32 1.5 60.0
2 ... 3069.0 840.0 2.80 1.8 73.0Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_Poly_(g) Cholestrl_(mg)
1 7.0 50.489 23.426 3.012 219.0
2 8.6 61.924 28.732 3.694 256.0取列数据
>>> print(food_info["NDB_No"])
0 1001
1 1002
2 1003
3 1004
4 1005
5 1006
6 1007
7 1008
8 1009
9 1010
10 1011
11 1012
12 1013
13 1014
14 1015
15 1016
16 1017
17 1018
18 1019
19 1020
20 1021
21 1022
22 1023
23 1024
24 1025
25 1026
26 1027
27 1028
28 1029
29 1030...
8588 43544
8589 43546
8590 43550
8591 43566
8592 43570
8593 43572
8594 43585
8595 43589
8596 43595
8597 43597
8598 43598
8599 44005
8600 44018
8601 44048
8602 44055
8603 44061
8604 44074
8605 44110
8606 44158
8607 44203
8608 44258
8609 44259
8610 44260
8611 48052
8612 80200
8613 83110
8614 90240
8615 90480
8616 90560
8617 93600
Name: NDB_No, Length: 8618, dtype: int64
取多个列的数据
>>> print(food_info[["NDB_No","Shrt_Desc"]])NDB_No Shrt_Desc
0 1001 BUTTER WITH SALT
1 1002 BUTTER WHIPPED WITH SALT
2 1003 BUTTER OIL ANHYDROUS
3 1004 CHEESE BLUE
4 1005 CHEESE BRICK
5 1006 CHEESE BRIE
6 1007 CHEESE CAMEMBERT
7 1008 CHEESE CARAWAY
8 1009 CHEESE CHEDDAR
9 1010 CHEESE CHESHIRE
10 1011 CHEESE COLBY
11 1012 CHEESE COTTAGE CRMD LRG OR SML CURD
12 1013 CHEESE COTTAGE CRMD W/FRUIT
13 1014 CHEESE COTTAGE NONFAT UNCRMD DRY LRG OR SML CURD
14 1015 CHEESE COTTAGE LOWFAT 2% MILKFAT
15 1016 CHEESE COTTAGE LOWFAT 1% MILKFAT
16 1017 CHEESE CREAM
17 1018 CHEESE EDAM
18 1019 CHEESE FETA
19 1020 CHEESE FONTINA
20 1021 CHEESE GJETOST
21 1022 CHEESE GOUDA
22 1023 CHEESE GRUYERE
23 1024 CHEESE LIMBURGER
24 1025 CHEESE MONTEREY
25 1026 CHEESE MOZZARELLA WHL MILK
26 1027 CHEESE MOZZARELLA WHL MILK LO MOIST
27 1028 CHEESE MOZZARELLA PART SKIM MILK
28 1029 CHEESE MOZZARELLA LO MOIST PART-SKIM
29 1030 CHEESE MUENSTER
... ... ...
8588 43544 BABYFOOD CRL RICE W/ PEARS & APPL DRY INST
8589 43546 BABYFOOD BANANA NO TAPIOCA STR
8590 43550 BABYFOOD BANANA APPL DSSRT STR
8591 43566 SNACKS TORTILLA CHIPS LT (BAKED W/ LESS OIL)
8592 43570 CEREALS RTE POST HONEY BUNCHES OF OATS HONEY RSTD
8593 43572 POPCORN MICROWAVE LOFAT&NA
8594 43585 BABYFOOD FRUIT SUPREME DSSRT
8595 43589 CHEESE SWISS LOW FAT
8596 43595 BREAKFAST BAR CORN FLAKE CRUST W/FRUIT
8597 43597 CHEESE MOZZARELLA LO NA
8598 43598 MAYONNAISE DRSNG NO CHOL
8599 44005 OIL CORN PEANUT AND OLIVE
8600 44018 SWEETENERS TABLETOP FRUCTOSE LIQ
8601 44048 CHEESE FOOD IMITATION
8602 44055 CELERY FLAKES DRIED
8603 44061 PUDDINGS CHOC FLAVOR LO CAL INST DRY MIX
8604 44074 BABYFOOD GRAPE JUC NO SUGAR CND
8605 44110 JELLIES RED SUGAR HOME PRESERVED
8606 44158 PIE FILLINGS BLUEBERRY CND
8607 44203 COCKTAIL MIX NON-ALCOHOLIC CONCD FRZ
8608 44258 PUDDINGS CHOC FLAVOR LO CAL REG DRY MIX
8609 44259 PUDDINGS ALL FLAVORS XCPT CHOC LO CAL REG DRY MIX
8610 44260 PUDDINGS ALL FLAVORS XCPT CHOC LO CAL INST DRY...
8611 48052 VITAL WHEAT GLUTEN
8612 80200 FROG LEGS RAW
8613 83110 MACKEREL SALTED
8614 90240 SCALLOP (BAY&SEA) CKD STMD
8615 90480 SYRUP CANE
8616 90560 SNAIL RAW
8617 93600 TURTLE GREEN RAW[8618 rows x 2 columns]
取所有的列名
>>> food_info.columns.tolist()
['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)']
排序
升序
inplace = True代表在当前对象内直接排序，如果要返回一个新的对象 set False
food_info.sort_values("Water_(g)",inplace = True)
>>> food_info["Water_(g)"]
>>> 760 0.00
8599 0.00
654 0.00
631 0.00
630 0.00
629 0.00
611 0.00
610 0.00
655 0.00
673 0.00
663 0.00
671 0.00
670 0.00
669 0.00
633 0.00
668 0.00
700 0.00
665 0.00
664 0.00
662 0.00
656 0.00
661 0.00
660 0.00
659 0.00
658 0.00
657 0.00
699 0.00
737 0.00
8122 0.00
667 0.00...
4270 99.80
4411 99.85
4408 99.89
4357 99.90
4239 99.90
4356 99.90
4369 99.90
4347 99.90
4205 99.90
4203 99.93
4204 99.95
4208 99.95
4213 99.95
4374 99.96
4407 99.97
4379 99.97
4373 99.97
4404 99.98
4372 99.98
4377 100.00
4378 100.00
4348 100.00
4209 100.00
4376 100.00
6150 NaN
6067 NaN
6113 NaN
1983 NaN
7776 NaN
6095 NaN
降序
>>> food_info.sort_values("Water_(g)",inplace = True , ascending = False)
>>> food_info["Water_(g)"]
4376 100.00
4209 100.00
4348 100.00
4378 100.00
4377 100.00
4372 99.98
4404 99.98
4407 99.97
4379 99.97
4373 99.97
4374 99.96
4213 99.95
4208 99.95
4204 99.95
4203 99.93
4356 99.90
4357 99.90
4239 99.90
4205 99.90
4369 99.90
4347 99.90
4408 99.89
4411 99.85
4270 99.80
4252 99.80
4392 99.80
4260 99.80
4409 99.79
4255 99.74
4397 99.70...
739 0.00
790 0.00
638 0.00
689 0.00
688 0.00
687 0.00
686 0.00
685 0.00
666 0.00
632 0.00
653 0.00
639 0.00
696 0.00
8455 0.00
791 0.00
675 0.00
8180 0.00
704 0.00
705 0.00
706 0.00
707 0.00
738 0.00
6417 0.00
760 0.00
6150 NaN
6067 NaN
6113 NaN
1983 NaN
7776 NaN
6095 NaN
转载于:https://www.cnblogs.com/zfcode/p/Pandas-ji-chu-xue-xi.html
Pandas 基础学习相关推荐
- Python科学计算之Pandas基础学习
Python科学计算之Pandas基础学习 导入Pandas 我们首先要导入我们的演出明星--Pandas. 这是导入Pandas的标准方式.显然,我们不希望每时每刻都在程序中写'pandas',但是 ...
- pandas基础学习笔记（简略版）
pandas基础学习笔记(简略版) 1.DataFrame 2.series 3.基本数据操作 4.DataFrame 运算 5.pandas绘图 6.文本的读取与存储 1.DataFrame 既有行 ...
- Pandas基础学习入门级别
1. 基础:数据导入,读取,索引 1.1 数据导入,读取 import pandas as pa#导入库 titan=pa.read_csv('./titanic_train911.csv')#读取文 ...
- 数据挖掘-3.Pandas基础
文章目录 Pandas基础 学习目标 1Pandas介绍 学习目标 1 Pandas介绍 2 为什么使用Pandas 3 小结 2 Pandas数据结构 学习目标 1.Series 1.1 Serie ...
- 快乐学习Pandas入门篇：Pandas基础
Datawhale学习 作者:杨煜,Datawhale成员 寄语:本文对Pandas基础内容进行了梳理,从文件读取与写入.Series及DataFrame基本数据结构.常用基本函数及排序四个模块快速入 ...
- pandas库基础学习
pandas库基础学习 1.Pandas模块的数据结构 Pandas模块的数据结构主要有两种: Series DataFrame Series 是一维数组,基于Numpy的ndarray 结构 Dat ...
- Datawhale pandas学习任务二：pandas基础
第二章Pandas基础 import numpy as np import pandas as pd pd.__version__ '1.1.3' 一.文件读取和写入 1.文件读取 read_csv ...
- pandas学习之pandas基础
感想 一.窗口对象 pandas 中有 3 类窗口,分别是滑动窗口 rolling .扩张窗口 expanding 以及指数加权窗口 ewm. 1.rolling():移动窗口,常用参数window ...
- pandas object转float_数据分析篇 | Pandas基础用法6【完结篇】
这是最后一篇,至此Pandas系列终于连载完了,有需要的也可以看看前面6篇,尽请收藏. 数据分析篇 | Pandas 概览 数据分析篇 | Pandas基础用法1数据分析篇 | Pandas基础用法2 ...
- 零基础学python多久可以工作-零基础学习python，要多久才可以学好并且找到工作？...
原标题:零基础学习python,要多久才可以学好并且找到工作? 零基础的你想学习python肯定很关注学习python的最短时间是多久,怎样才能快速学习python等问题,今天就为大家详细地回答一下这 ...
最新文章
- Java的字符串常量池
- 链表 - Part I
- IO之 Properties类加载文件
- Java中Comparable和Comparator接口的区别
- pandas 提取某几列_用pandas做数据清洗，我一般都这么干
- linux中Chmod + X
- 中孚计算机保密玩游戏,中孚计算机保密检查
- CVE-2021-40444 Microsoft MSHTML RCE简单复现
- 淘宝客APP带自营商城本地生活CPS外卖优惠电影票话费更新渠道跟单生活特权V3
- Hadoop——3.x安装部署
- 人工智能之不确定推理方法
- Photoshop是什么？
- 国空三区三线思考之：Arcgis自上而下从左到右进行编号
- anchor_free and anchor_based的简单理解
- String 和 StringBuider
- Open Inventor 10.11.1 Crack
- android textview 显示表情和文字 表情带超链接
- 秒杀系统----热点/秒杀商品如何处理？
- 计算机投诉信英语作文,投诉信英语作文（通用5篇）
- 嵌入式linux 更新源,openwrt如何修改为国内软件源
热门文章
- 2021-08-08 事务的提交，回滚
- python安装换源_Python切换pip安装源的方法详解
- 树莓派python蓝牙_怎样通过蓝牙仅使用Android手机为树莓派配置Wi-Fi网络
- Java TCP案例网络聊天室
- 安装composer并创建laravel项目流程
- javscript 简单拖拽（drag）拖放事件、dataTransfer详解，垃圾桶效果
- 十二、Shell脚本编程函数应用
- 小D课堂-SpringBoot 2.x微信支付在线教育网站项目实战_5-2.微信扫一扫功能开发前期准备...
- 阶段3 3.SpringMVC·_04.SpringMVC返回值类型及响应数据类型_1 搭建环境
- 阶段2 JavaWeb+黑马旅游网_15-Maven基础_第3节 maven标准目录结构和常用命令_07maven常用命令...