[빅분기] 유형1_Filtering

작성일자 : 2023-10-30

Ver 0.1.1

 

1 ~ 32 문제 풀이 : https://youtu.be/PxTIbZJ3xrA?si=VnVsvEjvH9sRM_mS
33 ~ 100 문제 풀이 : https://youtu.be/00rctVVSSoA?si=Du3KDMiQeHq7Gp-F

In [31]:

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

2. Filtering & Sorting¶

Q20. 데이터를 로드하라.
URL : https://raw.githubusercontent.com/Datamanim/pandas/main/chipo.csv

In [ ]:

df3 = pd.read_csv('https://raw.githubusercontent.com/Datamanim/pandas/main/chipo.csv')
df3.head()

Out[ ]:

	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

In [ ]:

type(df3)

Out[ ]:

pandas.core.frame.DataFrame

Q21. quantity컬럼 값이 3인 데이터를 추출하여 첫 5행을 출력하라

In [ ]:

df3.loc[(df3['quantity'] == 3)].head()

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price
409	178	3	Chicken Bowl	[[Fresh Tomato Salsa (Mild), Tomatillo-Green C...	$32.94
445	193	3	Bowl	[Braised Carnitas, Pinto Beans, [Sour Cream, C...	$22.20
689	284	3	Canned Soft Drink	[Diet Coke]	$3.75
818	338	3	Bottled Water	NaN	$3.27
850	350	3	Canned Soft Drink	[Sprite]	$3.75

Q22. quantity컬럼 값이 3인 데이터를 추출하여 index를 0부터 정렬하고 첫 5행을 출력하라

In [ ]:

df3.loc[(df3['quantity'] == 3)].reset_index(drop = True).head()

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price
0	178	3	Chicken Bowl	[[Fresh Tomato Salsa (Mild), Tomatillo-Green C...	$32.94
1	193	3	Bowl	[Braised Carnitas, Pinto Beans, [Sour Cream, C...	$22.20
2	284	3	Canned Soft Drink	[Diet Coke]	$3.75
3	338	3	Bottled Water	NaN	$3.27
4	350	3	Canned Soft Drink	[Sprite]	$3.75

Q23. quantity , item_price 두개의 컬럼으로 구성된 새로운 데이터 프레임을 정의하라

In [ ]:

df4 = df3[['quantity', 'item_price']].copy()
df4

Out[ ]:

	quantity	item_price
0	1	$2.39
1	1	$3.39
2	1	$3.39
3	1	$2.39
4	2	$16.98
...	...	...
4617	1	$11.75
4618	1	$11.75
4619	1	$11.25
4620	1	$8.75
4621	1	$8.75

4622 rows × 2 columns

Q24. item_price 컬럼의 달러표시 문자를 제거하고 float 타입으로 저장하여 new_price 컬럼에 저장하라

In [ ]:

df3.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

In [ ]:

df3['item_price'].replace('$','') #바뀌지 않음

Out[ ]:

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: object

In [ ]:

df3['new_price']  =  df3['item_price'].str.replace('$','').astype(float)
df3

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39	2.39
1	1	1	Izze	[Clementine]	$3.39	3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39	3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39	2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98	16.98
...	...	...	...	...	...	...
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	$11.75	11.75
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75	11.75
4619	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$11.25	11.25
4620	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	$8.75	8.75
4621	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75	8.75

4622 rows × 6 columns

Q25. new_price 컬럼이 5이하의 값을 가지는 데이터프레임을 추출하고, 전체 갯수를 구하여라

In [ ]:

len(df3.loc[(df3['new_price'] <= 5)])

Out[ ]:

1652

In [ ]:

df3.loc[(df3['new_price'] <= 5)].shape[0]

Out[ ]:

1652

Q26. item_name명이 Chicken Salad Bowl 인 데이터 프레임을 추출하라고 index 값을 초기화 하여라

In [ ]:

df3.loc[(df3['item_name'] == 'Chicken Salad Bowl')].reset_index(drop = True)

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
0	20	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75	8.75
1	60	2	Chicken Salad Bowl	[Tomatillo Green Chili Salsa, [Sour Cream, Che...	$22.50	22.50
2	94	2	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$22.50	22.50
3	111	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	$8.75	8.75
4	137	2	Chicken Salad Bowl	[Fresh Tomato Salsa, Fajita Vegetables]	$17.50	17.50
...	...	...	...	...	...	...
105	1813	2	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$17.50	17.50
106	1822	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Black Beans, Cheese, Gua...	$11.25	11.25
107	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$11.25	11.25
108	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	$8.75	8.75
109	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75	8.75

110 rows × 6 columns

Q27. new_price값이 9 이하이고 item_name 값이 Chicken Salad Bowl 인 데이터 프레임을 추출하라

In [ ]:

df3.loc[(df3['new_price'] <= 9) & (df3['item_name'] == 'Chicken Salad Bowl')].head()

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
44	20	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75	8.75
256	111	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	$8.75	8.75
526	220	1	Chicken Salad Bowl	[Roasted Chili Corn Salsa, [Black Beans, Sour ...	$8.75	8.75
528	221	1	Chicken Salad Bowl	[Tomatillo Green Chili Salsa, [Fajita Vegetabl...	$8.75	8.75
529	221	1	Chicken Salad Bowl	[Tomatillo Green Chili Salsa, [Fajita Vegetabl...	$8.75	8.75

Q28. df의 new_price 컬럼 값에 따라 오름차순으로 정리하고 index를 초기화 하여라

In [ ]:

df3.sort_values(by = 'new_price', ascending = True ).reset_index(drop = True).head()

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
0	471	1	Bottled Water	NaN	$1.09	1.09
1	338	1	Canned Soda	[Coca Cola]	$1.09	1.09
2	1575	1	Canned Soda	[Dr. Pepper]	$1.09	1.09
3	47	1	Canned Soda	[Dr. Pepper]	$1.09	1.09
4	1014	1	Canned Soda	[Coca Cola]	$1.09	1.09

Q29. df의 item_name 컬럼 값중 Chips 포함하는 경우의 데이터를 출력하라

In [ ]:

df3.loc[(df3['item_name'].str.contains('Chips'))]

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39	2.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39	2.39
6	3	1	Side of Chips	NaN	$1.69	1.69
10	5	1	Chips and Guacamole	NaN	$4.45	4.45
14	7	1	Chips and Guacamole	NaN	$4.45	4.45
...	...	...	...	...	...	...
4596	1826	1	Chips and Guacamole	NaN	$4.45	4.45
4600	1827	1	Chips and Guacamole	NaN	$4.45	4.45
4605	1828	1	Chips and Guacamole	NaN	$4.45	4.45
4613	1831	1	Chips	NaN	$2.15	2.15
4616	1832	1	Chips and Guacamole	NaN	$4.45	4.45

1084 rows × 6 columns

Q30. df의 짝수번째 컬럼만을 포함하는 데이터프레임을 출력하라

In [ ]:

#df.loc[ : , 시작: 끝: 스텝 ]
df3.loc[ : , : :2]

Out[ ]:

	order_id	item_name	item_price
0	1	Chips and Fresh Tomato Salsa	$2.39
1	1	Izze	$3.39
2	1	Nantucket Nectar	$3.39
3	1	Chips and Tomatillo-Green Chili Salsa	$2.39
4	2	Chicken Bowl	$16.98
...	...	...	...
4617	1833	Steak Burrito	$11.75
4618	1833	Steak Burrito	$11.75
4619	1834	Chicken Salad Bowl	$11.25
4620	1834	Chicken Salad Bowl	$8.75
4621	1834	Chicken Salad Bowl	$8.75

4622 rows × 3 columns

In [ ]:

#df.iloc[ : , 시작: 끝: 스텝 ]
df3.iloc[ : , : :2]

Out[ ]:

	order_id	item_name	item_price
0	1	Chips and Fresh Tomato Salsa	$2.39
1	1	Izze	$3.39
2	1	Nantucket Nectar	$3.39
3	1	Chips and Tomatillo-Green Chili Salsa	$2.39
4	2	Chicken Bowl	$16.98
...	...	...	...
4617	1833	Steak Burrito	$11.75
4618	1833	Steak Burrito	$11.75
4619	1834	Chicken Salad Bowl	$11.25
4620	1834	Chicken Salad Bowl	$8.75
4621	1834	Chicken Salad Bowl	$8.75

4622 rows × 3 columns

In [ ]:

#홀수 컬럼 추출하고 싶을때
df3.iloc[ : , 1: :2]

Out[ ]:

	quantity	choice_description	new_price
0	1	NaN	2.39
1	1	[Clementine]	3.39
2	1	[Apple]	3.39
3	1	NaN	2.39
4	2	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	16.98
...	...	...	...
4617	1	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	11.75
4618	1	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	11.75
4619	1	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	11.25
4620	1	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	8.75
4621	1	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	8.75

4622 rows × 3 columns

Q31. df의 new_price 컬럼 값에 따라 내림차순으로 정리하고 index를 초기화 하여라

In [ ]:

df3.sort_values(by = 'new_price', ascending = False).reset_index(drop = True).head()

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
0	1443	15	Chips and Fresh Tomato Salsa	NaN	$44.25	44.25
1	1398	3	Carnitas Bowl	[Roasted Chili Corn Salsa, [Fajita Vegetables,...	$35.25	35.25
2	511	4	Chicken Burrito	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	$35.00	35.00
3	1443	4	Chicken Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Chees...	$35.00	35.00
4	1443	3	Veggie Burrito	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	$33.75	33.75

Q32. df의 item_name 컬럼 값이 Steak Salad 또는 Bowl 인 데이터를 인덱싱하라

In [ ]:

df3.loc[(df3['item_name'] == 'Steak Salad') | (df3['item_name'] == 'Bowl')]

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
445	193	3	Bowl	[Braised Carnitas, Pinto Beans, [Sour Cream, C...	$22.20	22.20
664	276	1	Steak Salad	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$8.99	8.99
673	279	1	Bowl	[Adobo-Marinated and Grilled Steak, [Sour Crea...	$7.40	7.40
752	311	1	Steak Salad	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$8.99	8.99
893	369	1	Steak Salad	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	$8.99	8.99
3502	1406	1	Steak Salad	[[Lettuce, Fajita Veggies]]	$8.69	8.69

Q33. df의 item_name 컬럼 값이 Steak Salad 또는 Bowl 인 데이터를 데이터 프레임화 한 후, item_name를 기준으로 중복행이 있으면 제거하되 첫번째 케이스만 남겨라

In [ ]:

df3.loc[(df3['item_name'] == 'Steak Salad') | (df3['item_name'] == 'Bowl')].drop_duplicates( subset = 'item_name')

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price
445	193	3	Bowl	[Braised Carnitas, Pinto Beans, [Sour Cream, C...	$22.20
664	276	1	Steak Salad	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$8.99

Q34. df의 item_name 컬럼 값이 Steak Salad 또는 Bowl 인 데이터를 데이터 프레임화 한 후, item_name를 기준으로 중복행이 있으면 제거하되 마지막 케이스만 남겨라

In [ ]:

df3.loc[(df3['item_name'] == 'Steak Salad') | (df3['item_name'] == 'Bowl')].drop_duplicates( subset = 'item_name', keep = 'last')

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price
673	279	1	Bowl	[Adobo-Marinated and Grilled Steak, [Sour Crea...	$7.40
3502	1406	1	Steak Salad	[[Lettuce, Fajita Veggies]]	$8.69

Q35. df의 데이터 중 new_price값이 new_price값의 평균값 이상을 가지는 데이터들을 인덱싱하라

In [ ]:

con1 = df3['new_price'] >= df3['new_price'].mean()
df3[con1].head()

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98	16.98
5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...	$10.98	10.98
7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	$11.75	11.75
8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...	$9.25	9.25
9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...	$9.25	9.25

Q36. df의 데이터 중 item_name의 값이 Izze 데이터를 Fizzy Lizzy로 수정하라

In [ ]:

df3['item_name'].unique()

Out[ ]:

array(['Chips and Fresh Tomato Salsa', 'Izze', 'Nantucket Nectar',
       'Chips and Tomatillo-Green Chili Salsa', 'Chicken Bowl',
       'Side of Chips', 'Steak Burrito', 'Steak Soft Tacos',
       'Chips and Guacamole', 'Chicken Crispy Tacos',
       'Chicken Soft Tacos', 'Chicken Burrito', 'Canned Soda',
       'Barbacoa Burrito', 'Carnitas Burrito', 'Carnitas Bowl',
       'Bottled Water', 'Chips and Tomatillo Green Chili Salsa',
       'Barbacoa Bowl', 'Chips', 'Chicken Salad Bowl', 'Steak Bowl',
       'Barbacoa Soft Tacos', 'Veggie Burrito', 'Veggie Bowl',
       'Steak Crispy Tacos', 'Chips and Tomatillo Red Chili Salsa',
       'Barbacoa Crispy Tacos', 'Veggie Salad Bowl',
       'Chips and Roasted Chili-Corn Salsa',
       'Chips and Roasted Chili Corn Salsa', 'Carnitas Soft Tacos',
       'Chicken Salad', 'Canned Soft Drink', 'Steak Salad Bowl',
       '6 Pack Soft Drink', 'Chips and Tomatillo-Red Chili Salsa', 'Bowl',
       'Burrito', 'Crispy Tacos', 'Carnitas Crispy Tacos', 'Steak Salad',
       'Chips and Mild Fresh Tomato Salsa', 'Veggie Soft Tacos',
       'Carnitas Salad Bowl', 'Barbacoa Salad Bowl', 'Salad',
       'Veggie Crispy Tacos', 'Veggie Salad', 'Carnitas Salad'],
      dtype=object)

In [ ]:

#loc를 통해 값 수정
df3.loc[df3['item_name'] == 'Izze', 'item_name'] =  'Fizzy Lizzy'
df3

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39	2.39
1	1	1	Fizzy Lizzy	[Clementine]	$3.39	3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39	3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39	2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98	16.98
...	...	...	...	...	...	...
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	$11.75	11.75
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75	11.75
4619	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$11.25	11.25
4620	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	$8.75	8.75
4621	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75	8.75

4622 rows × 6 columns

Q37. df의 데이터 중 choice_description 값이 NaN 인 데이터의 갯수를 구하여라

In [ ]:

df3['choice_description'].isnull().sum()

Out[ ]:

1246

Q38 .df의 데이터 중 choice_description 값이 NaN 인 데이터를 NoData 값으로 대체하라(loc 이용)

In [ ]:

#방법1 (loc 사용)
df3.loc[df3['choice_description'].isnull(), 'choice_description'] = 'NoData'
print(df3['choice_description'].isnull().sum())
print(len(df3['choice_description'] == 'Nodata'))

0
4622

In [ ]:

#방법2 (fillna 사용)
df3.loc[ :,'choice_description'] = df3['choice_description'].fillna('NoData')

Q39. df의 데이터 중 choice_description 값에 Black이 들어가는 경우를 인덱싱하라

In [ ]:

print(len(df3.loc[df3['choice_description'].str.contains('Black')]))
df3.loc[df3['choice_description'].str.contains('Black')].head()

1353

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price	NoData
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98	16.98	NaN
7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables...	$11.75	11.75	NaN
9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...	$9.25	9.25	NaN
11	6	1	Chicken Crispy Tacos	[Roasted Chili Corn Salsa, [Fajita Vegetables,...	$8.75	8.75	NaN
12	6	1	Chicken Soft Tacos	[Roasted Chili Corn Salsa, [Rice, Black Beans,...	$8.75	8.75	NaN

Q40. df의 데이터 중 choice_description 값에 Vegetables 들어가지 않는 경우의 갯수를 출력하라

In [ ]:

df3['choice_description'].str.contains('Vegetables')

Out[ ]:

0       False
1       False
2       False
3       False
4       False
        ...  
4617    False
4618    False
4619     True
4620     True
4621     True
Name: choice_description, Length: 4622, dtype: bool

In [ ]:

#Boolean 결과 바꾸기
~df3['choice_description'].str.contains('Vegetables')

Out[ ]:

0        True
1        True
2        True
3        True
4        True
        ...  
4617     True
4618     True
4619    False
4620    False
4621    False
Name: choice_description, Length: 4622, dtype: bool

In [ ]:

print(df3.loc[~df3['choice_description'].str.contains('Vegetables')].shape[0])
df3.loc[~df3['choice_description'].str.contains('Vegetables')]

3900

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price	NoData
0	1	1	Chips and Fresh Tomato Salsa	NoData	$2.39	2.39	True
1	1	1	Fizzy Lizzy	[Clementine]	$3.39	3.39	NaN
2	1	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
3	1	1	Chips and Tomatillo-Green Chili Salsa	NoData	$2.39	2.39	True
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98	16.98	NaN
...	...	...	...	...	...	...	...
4614	1831	1	Bottled Water	NoData	$1.50	1.50	True
4615	1832	1	Chicken Soft Tacos	[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]]	$8.75	8.75	NaN
4616	1832	1	Chips and Guacamole	NoData	$4.45	4.45	True
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	$11.75	11.75	NaN
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75	11.75	NaN

3900 rows × 7 columns

Q41. df의 데이터 중 item_name 값이 N으로 시작하는 데이터를 모두 추출하라

In [ ]:

df3['item_name'].str[0] #각 행의 첫번째 문자열만 출력

Out[ ]:

0       C
1       F
2       N
3       C
4       C
       ..
4617    S
4618    S
4619    C
4620    C
4621    C
Name: item_name, Length: 4622, dtype: object

In [ ]:

print(df3.loc[df3['item_name'].str[0] == 'N'].shape)
df3.loc[df3['item_name'].str[0] == 'N']

(27, 7)

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price	NoData
2	1	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
22	11	1	Nantucket Nectar	[Pomegranate Cherry]	$3.39	3.39	NaN
105	46	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
173	77	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
205	91	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN
436	189	1	Nantucket Nectar	[Pomegranate Cherry]	$3.39	3.39	NaN
601	247	2	Nantucket Nectar	[Pineapple Orange Banana]	$6.78	6.78	NaN
925	381	1	Nantucket Nectar	[Pomegranate Cherry]	$3.39	3.39	NaN
1356	553	1	Nantucket Nectar	[Pomegranate Cherry]	$3.39	3.39	NaN
1585	641	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN
1626	656	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
1706	690	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
2162	872	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
2379	947	2	Nantucket Nectar	[Peach Orange]	$6.78	6.78	NaN
2381	947	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
2430	965	1	Nantucket Nectar	[Pomegranate Cherry]	$3.39	3.39	NaN
2653	1053	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
2818	1118	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
2838	1128	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN
2853	1133	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
2949	1172	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN
3318	1330	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN
3368	1351	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
3570	1433	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
3845	1541	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN
4019	1609	1	Nantucket Nectar	[Pineapple Orange Banana]	$3.39	3.39	NaN
4078	1632	1	Nantucket Nectar	[Peach Orange]	$3.39	3.39	NaN

Q42. df의 데이터 중 item_name 값의 단어갯수가 15개 이상인 데이터를 인덱싱하라 (띄어쓰기 포함)

In [ ]:

a = 'abc d e f'
len(a)

Out[ ]:

9

In [ ]:

df3.loc[df3['item_name'].str.len() >= 15]

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price	NoData
0	1	1	Chips and Fresh Tomato Salsa	NoData	$2.39	2.39	True
2	1	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
3	1	1	Chips and Tomatillo-Green Chili Salsa	NoData	$2.39	2.39	True
8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...	$9.25	9.25	NaN
10	5	1	Chips and Guacamole	NoData	$4.45	4.45	True
...	...	...	...	...	...	...	...
4615	1832	1	Chicken Soft Tacos	[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream]]	$8.75	8.75	NaN
4616	1832	1	Chips and Guacamole	NoData	$4.45	4.45	True
4619	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$11.25	11.25	NaN
4620	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...	$8.75	8.75	NaN
4621	1834	1	Chicken Salad Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...	$8.75	8.75	NaN

2373 rows × 7 columns

Q43. df의 데이터 중 new_price값이 lst에 해당하는 경우의 데이터 프레임을 구하고 그 갯수를 출력하라
lst =[1.69, 2.39, 3.39, 4.45, 9.25, 10.98, 11.75, 16.98]

In [ ]:

lst =[1.69, 2.39, 3.39, 4.45, 9.25, 10.98, 11.75, 16.98]
lst

Out[ ]:

[1.69, 2.39, 3.39, 4.45, 9.25, 10.98, 11.75, 16.98]

In [ ]:

df3.loc[df3['new_price'].isin(lst)]

Out[ ]:

	order_id	quantity	item_name	choice_description	item_price	new_price	NoData
0	1	1	Chips and Fresh Tomato Salsa	NoData	$2.39	2.39	True
1	1	1	Fizzy Lizzy	[Clementine]	$3.39	3.39	NaN
2	1	1	Nantucket Nectar	[Apple]	$3.39	3.39	NaN
3	1	1	Chips and Tomatillo-Green Chili Salsa	NoData	$2.39	2.39	True
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans...	$16.98	16.98	NaN
...	...	...	...	...	...	...	...
4610	1830	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75	11.75	NaN
4612	1831	1	Carnitas Bowl	[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...	$9.25	9.25	NaN
4616	1832	1	Chips and Guacamole	NoData	$4.45	4.45	True
4617	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...	$11.75	11.75	NaN
4618	1833	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...	$11.75	11.75	NaN

1393 rows × 7 columns