Hướng dẫn python q1 q3 - python q1 q3

Sự khác biệt chính của chữ ký giữa numpy.percentile và pandas.

Nội phân chính

• Làm thế nào để bạn tìm thấy Q1 và Q3 trong Python?
• Làm thế nào để bạn tìm thấy Q1 Q2 Q3 trong Python?
• Làm thế nào để bạn tính toán Q1 và Q3?
• Làm thế nào để python tính toán lượng tử?

Theo mặc định, cả hai đều sử dụng kỹ thuật nội suy linear để tìm số lượng như vậy. Thay vào đó,

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

0 có chữ ký ít linh hoạt hơn và chỉ cho phép sử dụng linear.

Trong numpy> = 1.22 tham số

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

3 được dùng hết và thay thế bằng

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

4.

Ở đây một ví dụ về việc sử dụng với phép nội suy tuyến tính: (hành vi mặc định)

import pandas as pd
import numpy as np


s =[18,45,66,70,76,83,88,90,90,95,95,98, 100]
print(pd.DataFrame(s).quantile(q=[.25, .50, .75]))
print(np.percentile(s, q=[25, 50, 75]))
print(pd.DataFrame(s).describe(percentiles=[.25, .5, .75])) # the parameter is redundant, it's the default behavior

Ở đây bằng cách sử dụng phép nội suy điểm giữa:

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

Quartiles: Bộ tứ là một loại lượng tử. Bộ tứ đầu tiên (Q1), được định nghĩa là số giữa giữa số nhỏ nhất và trung bình của tập dữ liệu, Bộ tứ thứ hai (Q2) - trung bình của tập dữ liệu đã cho trong khi Bộ tứ thứ ba (Q3), là số trung bình giữa trung bình và giá trị lớn nhất của tập dữ liệu.
A quartile is a type of quantile. The first quartile (Q1), is defined as the middle number between the smallest number and the median of the data set, the second quartile (Q2) – median of the given data set while the third quartile (Q3), is the middle number between the median and the largest value of the data set.

Thuật toán để tìm các bộ tứ: Quartiles được tính bằng sự trợ giúp của trung vị. Nếu số lượng mục nhập là số chẵn, tức là của Mẫu 2n, thì phần tư đầu tiên (Q1) bằng với trung bình của N Các mục nhỏ nhất và Bộ tứ thứ ba (Q3) bằng với trung bình của các mục lớn nhất N.
Quartiles are calculated by the help of the median. If the number of entries is an even number i.e. of the form 2n, then, first quartile (Q1) is equal to the median of the n smallest entries and the third quartile (Q3) is equal to the median of the n largest entries.

Nếu số lượng mục là số lẻ, tức là của biểu mẫu (2n + 1), thì

• Bộ tứ đầu tiên (q1) bằng với trung bình của n mục nhỏ nhấtn smallest entries
• Bộ tứ thứ ba (Q3) bằng với trung bình của các mục lớn nhất nn largest entries
• Bộ tứ thứ hai (Q2) giống như trung vị thông thường.

Phạm vi: Đó là sự khác biệt giữa giá trị lớn nhất và giá trị nhỏ nhất trong tập dữ liệu đã cho Bộ tứ (Q3) và Bộ tứ đầu tiên (Q1). Nó bao gồm trung tâm của phân phối và chứa 50% các quan sát. IQR = Q3 - Q1It is the difference between the largest value and the smallest value in the given data set.
Interquartile Range :
The interquartile range (IQR), also called as midspread or middle 50%, or technically H-spread is the difference between the third quartile (Q3) and the first quartile (Q1). It covers the center of the distribution and contains 50% of the observations. IQR = Q3 – Q1

Sử dụng:

• Phạm vi liên vùng có điểm phân tích là 25% do nó thường được ưa thích trong tổng phạm vi.
• IQR được sử dụng để xây dựng các ô hộp, biểu diễn đồ họa đơn giản của phân phối xác suất.
• IQR cũng có thể được sử dụng để xác định các ngoại lệ trong tập dữ liệu đã cho.
• IQR đưa ra xu hướng trung tâm của dữ liệu.

Quyết định

• Bộ dữ liệu có giá trị cao hơn của phạm vi liên vùng (IQR) có nhiều biến đổi hơn.
• Bộ dữ liệu có giá trị thấp hơn của phạm vi liên vùng (IQR) là thích hợp hơn.

Giả sử nếu chúng ta có hai bộ dữ liệu và phạm vi liên vùng của chúng là IR1 và IR2 và nếu IR1> IR2 thì dữ liệu trong IR1 được cho là có nhiều biến đổi hơn dữ liệu trong IR2 và dữ liệu trong IR2 là thích hợp hơn.

Example:

• Sau đây là số lượng ứng cử viên đăng ký mỗi ngày trong 20 ngày qua cho khóa học Cấu trúc & Thuật toán-DSA trực tuyến 3 tại GeekSforGeek: 75, 69, 56, 46, 47, 79, 92, 97, 89, 88, 36, 36, 96, 105, 32, 116, 101, 79, 93, 91, 112
  Data Structures & Algorithms-DSA Online 3 at GeeksforGeeks:
  75, 69, 56, 46, 47, 79, 92, 97, 89, 88, 36, 96, 105, 32, 116, 101, 79, 93, 91, 112
• Sau khi sắp xếp bộ dữ liệu trên: 32, 36, 46, 47, 56, 69, 75, 79, 79, 88, 89, 91, 92, 93, 96, 97, 101, 105, 112, 116
  32, 36, 46, 47, 56, 69, 75, 79, 79, 88, 89, 91, 92, 93, 96, 97, 101, 105, 112, 116
• Ở đây tổng số điều khoản là 20.
• Bộ tứ thứ hai (Q2) hoặc trung bình của dữ liệu trên là (88 + 89) / 2 = 88.5
• Bộ tứ đầu tiên (Q1) là trung bình của N.E. 10 điều khoản (hoặc n tức là 10 giá trị nhỏ nhất) = 62.5
• Bộ tứ thứ ba (q3) là trung bình của n tức là 10 giá trị lớn nhất (hoặc n tức là 10 giá trị) = 96.5
• Sau đó, IQR = Q3 - Q1 = 96,5 - 62,5 = 34.0

Phạm vi liên vùng bằng cách sử dụng numpy.median

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

5

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

6

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

7

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

9

Output: 34.0

0

Output: 34.0

1__

Output: 17.0

2

Output: 17.0

3

Output: 34.0

1

Output: 17.0

5

Output: 34.0

1

Output: 17.0

7

Output: 34.0

1

Output: 17.0

9pandas0

pandas1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas3pandas44

pandas6

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas8pandas444

q1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas6q4 q5

q6q7

Output: 34.0

Phạm vi liên vùng bằng cách sử dụng numpy.percentile

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

5

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

6

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

7

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

9

Output: 34.0

0

Output: 34.0

1__

Output: 17.0

2

Output: 17.0

3

Output: 34.0

1

Output: 17.0

5

Output: 34.0

1

Output: 17.0

7

Output: 34.0

1

Output: 17.0

9pandas0

pandas1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

26

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

27

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

28

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

30

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

31

pandas6

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

26

Output: 34.0

2

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

28

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

30

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

31

q1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas6q4 q5

q6q7

Output: 34.0

Phạm vi liên vùng bằng cách sử dụng scipy.stats.iqr

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

47

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

48

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

5

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

50

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

7

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

9

Output: 34.0

0

Output: 34.0

1__

Output: 17.0

2

Output: 17.0

3

Output: 34.0

1

Output: 17.0

5

Output: 34.0

1

Output: 17.0

7

Output: 34.0

1

Output: 17.0

9pandas0

q1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

97

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

30

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

31

q6q7

Output: 34.0

Độ lệch của phần trăm tứ phân vị là một nửa sự khác biệt của Bộ tứ thứ ba (Q3) và Bộ tứ đầu tiên (Q1), tức là một nửa của phạm vi liên vùng (IQR). (Q3 - Q1) / 2 = IQR / 2
Quartile deviation is the half of the difference of third quartile (Q3) and first quartile (Q1) i.e. half of the interquartile range (IQR). (Q3 – Q1) / 2 = IQR / 2

Ra quyết định tập dữ liệu có giá trị độ lệch tứ tứ cao hơn có độ biến thiên cao hơn.
The data set having higher value of quartile deviation has higher variability.

Độ lệch tứ

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

5

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

6

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

7

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

9

Output: 34.0

0

Output: 34.0

1__

Output: 17.0

2

Output: 17.0

3

Output: 34.0

1

Output: 17.0

5

Output: 34.0

1

Output: 17.0

7

Output: 34.0

1

Output: 17.0

9pandas0

q1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

97

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

30

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

31

Độ lệch của phần trăm tứ phân vị là một nửa sự khác biệt của Bộ tứ thứ ba (Q3) và Bộ tứ đầu tiên (Q1), tức là một nửa của phạm vi liên vùng (IQR). (Q3 - Q1) / 2 = IQR / 2

q1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas6q4 q5

Phạm vi liên vùng bằng cách sử dụng scipy.stats.iqr

q6

Output: 34.0

70

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

47

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

48

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

5

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

50

How do you find Q1 and Q3 in Python?

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

7

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

9

Output: 34.0

0

Output: 34.0

1__np. percentile(samples, [25, 50, 75]) returns the actual values from the list: Out[1]: array([12., 14., 22.]) However, the quartiles are Q1=10.0, Median=14, Q3=24.5 (you can also use this link to find the quartiles and median online).

q1s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 8 s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 97s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 8 s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 30s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 31

Độ lệch của phần trăm tứ phân vị là một nửa sự khác biệt của Bộ tứ thứ ba (Q3) và Bộ tứ đầu tiên (Q1), tức là một nửa của phạm vi liên vùng (IQR). (Q3 - Q1) / 2 = IQR / 2.

Ra quyết định tập dữ liệu có giá trị độ lệch tứ tứ cao hơn có độ biến thiên cao hơn.

Độ lệch tứ

s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 5 s_even = [18,45,66,70,76,83,88,90,90,95,95,98] print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint')) print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22 s_odd = s_even + [100] # made it odd print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint')) print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22 print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22 6

pandas1

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas3pandas44.

pandas6

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 pandas8pandas444

Output: 34.0

64

s_even = [18,45,66,70,76,83,88,90,90,95,95,98]
print(pd.DataFrame(s_even).quantile(q=[.25, .5, .75], interpolation='midpoint'))
print(np.percentile(s_even, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_even, q=[25, 50, 75], method='midpoint')) # version >= 1.22

s_odd = s_even + [100] # made it odd
print(pd.DataFrame(s_odd).quantile(q=[.25, .50, .75], interpolation='midpoint'))
print(np.percentile(s_odd, q=[25, 50, 75], interpolation='midpoint')) # verion < 1.22
print(np.percentile(s_odd, q=[25, 50, 75], method='midpoint')) # version >= 1.22

8 q1

Output: 34.0

67

Output: 34.0

68

Output: 17.0

Bạn thấy Q1 và Q3 trong Python?

Chạy NP.Percentile (mẫu, [25, 50, 75]) trả về các giá trị thực từ danh sách: ra [1]: mảng ([12., 14., 22.]) Tuy nhiên, các bộ tứ là Q1 = 10.0, Median = 14, q3 = 24.5 (bạn cũng có thể sử dụng liên kết này để tìm các bộ tứ và trung bình trực tuyến).

Làm thế nào để bạn tìm thấy Q1 Q2 Q3 trong Python?the numpy. quantile() function takes an array and a number say q between 0 and 1. It returns the value at the q th quantile.