

import pandas as pd
import numpy as np

import os



file_path = os.path.abspath('data/chipotle.tsv')
file_path

'D:\\python_jupyter\\mine_python_notes\\07_Pandas\\data\\chipotle.tsv'



chipo = pd.read_csv(file_path, sep = '\t')



chipo



chipo.head(10)



chipo.shape

(4622, 5)



# 数据行数
chipo.shape[0]

4622



chipo.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4622 entries, 0 to 4621
Data columns (total 5 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   order_id            4622 non-null   int64 
 1   quantity            4622 non-null   int64 
 2   item_name           4622 non-null   object
 3   choice_description  3376 non-null   object
 4   item_price          4622 non-null   object
dtypes: int64(2), object(3)
memory usage: 180.7+ KB



chipo.shape[1]

5



chipo.columns

Index(['order_id', 'quantity', 'item_name', 'choice_description',
       'item_price'],
      dtype='object')



chipo.index

RangeIndex(start=0, stop=4622, step=1)



c = chipo.groupby('item_name')
c = c.sum('order_id')
c = c.sort_values(['quantity'], ascending=False)
c.head(10)



c = chipo.groupby('choice_description').sum('order_id')
c = c.sort_values(['quantity'], ascending=False)
c.head(10)



chipo.quantity.sum()

4972



chipo.item_price.dtype

dtype('O')

df.apply(func, axis=0)



func = lambda x : float(x[1:-1])
chipo.item_price = chipo.item_price.apply(func)
# 或者使用 列表推导式
# chipo.item_price = [float(val[1:]) for val in chipo.item_price]



chipo.item_price.dtype

dtype('float64')



chipo.item_price

0        2.39
1        3.39
2        3.39
3        2.39
4       16.98
        ...  
4617    11.75
4618    11.75
4619    11.25
4620     8.75
4621     8.75
Name: item_price, Length: 4622, dtype: float64



(chipo.quantity * chipo.item_price).sum()

39237.02



c = chipo.order_id.value_counts()
c

926     23
1483    14
205     12
759     11
1786    11
        ..
768      1
341      1
1048     1
94       1
1199     1
Name: order_id, Length: 1834, dtype: int64



c.count()

1834



chipo.item_name.value_counts().count()

50

DataFrame.drop_duplicates(subset=None, keep='first', inplace=False)



data = {'name': ['John', 'Mary', 'John', 'Peter'],
        'city': ['London', 'Paris', 'London', 'Berlin'],
         'age':[10, 20, 10 ,40]}
df = pd.DataFrame(data)

# 删除所有重复行
df.drop_duplicates(inplace=True)
df



chipo_filtered = chipo.drop_duplicates(['item_name','quantity','choice_description'])
# chipo_filtered = chipo_filtered[chipo_filtered.quantity==1]
chipo_filtered



# 查找产品数量等于1
chipo_one_prd = chipo_filtered[chipo_filtered.quantity==1]
chipo_one_prd



chipo_one_prd[chipo_one_prd['item_price']>10].item_name

5          Chicken Bowl
7         Steak Burrito
13         Chicken Bowl
23      Chicken Burrito
39        Barbacoa Bowl
             ...       
4593      Carnitas Bowl
4594      Barbacoa Bowl
4607      Steak Burrito
4610      Steak Burrito
4611     Veggie Burrito
Name: item_name, Length: 685, dtype: object



chipo_one_prd.query('item_price>10').item_name.nunique()

25



chipo[(chipo['item_name'] == 'Chicken Bowl') & (chipo['quantity'] == 1)]



chipo.item_name.sort_values()
# 或者
chipo.sort_values(by='item_name')



chipo.sort_values(by='item_price', ascending=False).head(1)



chipo_salad = chipo[chipo.item_name == "Veggie Salad Bowl"]
len(chipo_salad)

18



chipo_drink_steak_bowl = chipo[(chipo.item_name == "Canned Soda") & (chipo.quantity > 1)]
len(chipo_drink_steak_bowl)

20



chipo[chipo.item_name.str.startswith('C')]



# 根据列明来筛选
chipo[['item_name','item_price']]



chipo[(chipo['item_name'] != 'Steak Burrito') & (chipo.item_name != 'Chicken Salad Bowl')]



# :表示所有，0:3表示从0列到第3列
chipo.iloc[:, 0:3]



chipo.iloc[0:10, 0:3]



chipo.loc[chipo.item_name.isin(['Chicken Bowl','Steak Burrito']), ['item_name','item_price']]



chipo



chipo.loc[chipo.item_name == 'Chips and Fresh Tomato Salsa',]

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	Izze	[Clementine]	$3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98
5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	$10.98
6	3	1	Side of Chips	NaN	$1.69
7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	$11.75
8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...	$9.25
9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...	$9.25

	order_id	quantity
item_name
Chicken Bowl	713926	761
Chicken Burrito	497303	591
Chips and Guacamole	449959	506
Steak Burrito	328437	386
Canned Soft Drink	304753	351
Chips	208004	230
Steak Bowl	193752	221
Bottled Water	175944	211
Chips and Fresh Tomato Salsa	100419	130
Canned Soda	76396	126

	order_id	quantity
choice_description
[Diet Coke]	123455	159
[Coke]	122752	143
[Sprite]	80426	89
[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]]	43088	49
[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]]	36041	42
[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]]	37550	40
[Lemonade]	31892	36
[Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]]	24432	36
[Coca Cola]	19282	32
[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]]	29614	30

	order_id	quantity	item_name	choice_description	item_price
3389	1360	2	6 Pack Soft Drink	[Diet Coke]	12.98
341	148	1	6 Pack Soft Drink	[Diet Coke]	6.49
1849	749	1	6 Pack Soft Drink	[Coke]	6.49
1860	754	1	6 Pack Soft Drink	[Diet Coke]	6.49
2713	1076	1	6 Pack Soft Drink	[Coke]	6.49
...	...	...	...	...	...
2384	948	1	Veggie Soft Tacos	[Roasted Chili Corn Salsa, [Fajita Vegetables,...	8.75
781	322	1	Veggie Soft Tacos	[Fresh Tomato Salsa, [Black Beans, Cheese, Sou...	8.75
2851	1132	1	Veggie Soft Tacos	[Roasted Chili Corn Salsa (Medium), [Black Bea...	8.49
1699	688	1	Veggie Soft Tacos	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	11.25
1395	567	1	Veggie Soft Tacos	[Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...	8.49

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	2.39
1	1	1	Izze	[Clementine]	3.39
2	1	1	Nantucket Nectar	[Apple]	3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	16.98
...	...	...	...	...	...
4612	1831	1	Carnitas Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	9.25
4613	1831	1	Chips	NaN	2.15
4614	1831	1	Bottled Water	NaN	1.50
4615	1832	1	Chicken Soft Tacos	[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]]	8.75
4616	1832	1	Chips and Guacamole	NaN	4.45

526互联

pandas学习-基础用法

导入数据、数据的基本操作¶

导入libraries¶

导入数据¶

查看前10行数据¶

查看数据集中的数据量¶

查看数据集的列数¶

打印出列名称¶

数据集的索引¶

汇总排序¶

列quantity数量统计¶

查看列类型¶

使用lambda函数转化数据类型¶

统计总价¶

过滤和排序数据¶

统计超过$10的产品¶

多个条件查找¶

根据name排序¶

查询最贵的商品数量是多少¶

其他条件检索¶

使用.iloc通过传递的整数的位置进行切片¶

	order_id	quantity	item_name	choice_description	item_price
5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	10.98
13	7	1	Chicken Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	11.25
19	10	1	Chicken Bowl	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	8.75
26	13	1	Chicken Bowl	[Roasted Chili Corn Salsa (Medium), [Pinto Bea...	8.49
42	20	1	Chicken Bowl	[Roasted Chili Corn Salsa, [Rice, Black Beans,...	11.25
...	...	...	...	...	...
4590	1825	1	Chicken Bowl	[Roasted Chili Corn Salsa, [Rice, Black Beans,...	11.25
4591	1825	1	Chicken Bowl	[Tomatillo Red Chili Salsa, [Rice, Black Beans...	8.75
4595	1826	1	Chicken Bowl	[Tomatillo Green Chili Salsa, [Rice, Black Bea...	8.75
4599	1827	1	Chicken Bowl	[Roasted Chili Corn Salsa, [Cheese, Lettuce]]	8.75
4604	1828	1	Chicken Bowl	[Fresh Tomato Salsa, [Rice, Black Beans, Chees...	8.75

	name	city	age
0	John	London	10
1	Mary	Paris	20
3	Peter	Berlin	40