numpy基礎屬性方法隨機整理（七）--- np.take() / np.where / np.argmax()/np.argmin()（對照pandas相應功能的屬性和方法對比學習）

Note1:
np.take(close_prices, np.where(weekdays==weekday))
pandas中參考 《pandas基礎屬性方法隨機整理（四）—實例梳理（多知識點）》有三種方法實現該條件查詢功能：

1. 方法1：輔助列 data[‘Mon’]， data[data[‘Mon’]==1].mean() # 掩碼

   即 數組[關係表達式]：
   關係表達式是一個布爾型書序，其中爲True的元素對應於數組中滿足關係表達式的元素，以上下標運算的值就是從數組中挑選與布爾數組中爲True的元素相對應的元素

2. 方法2：輔助列data[‘Mon’]， # 查詢.query, 功能與索引一樣，有時更方便
3. 方法3： 不添加輔助列，利用groupby()方法實現降採樣 data.groupby ( lambda x: x.month ). mean(). T[1]

numpy方法：
for循環中的條件查詢np.take(.., np.where(..))
np.where(關係表達式)：數組中滿足關係表達式的元素的下標數組
np.take(數組，下標數組)：數組中由下標數組所表示的元素集合

where(condition, [x, y])：
    -- x, y不爲空: condition爲True, 返回x; False, 返回y
    -- x, y爲空： 返回condition爲True的數組下標

average_prices = np.zeros(5)
for weekday in range(average_prices.size):
    average_prices[weekday] = np.take(close_prices,
    np.where(weekdays==weekday)).mean()
    # np.where(weekdays==weekday)獲得序列號的數組，作爲take取元素的下標

import numpy as np
c = ages + scores * 1j                              # j要用 1j 表示
d = np.sort_complex(c).real                         # float:[20. 22. 23. 27. 29.] <class 'numpy.ndarray'>
d = d.astype('int64')                               # int:[20 22 23 27 29] <class 'numpy.ndarray'>

index_names = []
for i, v in enumerate(d):
    print(np.where(ages ==v)[0][0], np.take(ages, np.where(ages == v))[0][0])
    index_names.append(np.where(ages ==v)[0][0])    # 安裝d的元素順序依次對比，取出ages的下標

namesComplexSorted = names[index_names]
print(namesComplexSorted)                           # ['Jue' 'Kity' 'Mike' 'scote' 'Tom']
print(np.where(namesComplexSorted == 'Kity')[0][0]) # (array([1], dtype=int64),)

Note2:
最大值 / 最小值對應的索引：

屬性描述	numpy	pandas
最大值的索引	argmax()	idxmax()
最小值的索引	argmin()	idxmin()

Note3:
print()內部的if…else…語句：

print(average_prices,
        '(max)' if(weekday == max_index) else
        '(min)' if(weekday == min_index) else '')

---

Code:

import os 
import sys
import datetime as dt 
import numpy as np 


g_weekdays = ('MON','Tue','WED','THU','FRI','SAT','SUN')

def dmy2weekday(dmy):
    return dt.datetime.strptime(str(dmy, encoding='utf-8'), '%d-%m-%Y').date().weekday()


def read_data(filename):
    weekdays, close_prices = np.loadtxt(filename, delimiter=',', usecols=(1,6),unpack=True,converters={1:dmy2weekday})
    return weekdays, close_prices

def cal_average_prices(weekdays, close_prices):
    average_prices = np.zeros(5)
    for weekday in range(average_prices.size):
        average_prices[weekday] = np.take(
            close_prices, np.where(weekdays==weekday)).mean()
        # np.take(close_prices, np.where().mean()) 類比pandas中的 df[df[weekday==1]].mean()
        # 參考 《pandas基礎屬性方法隨機整理（四）---實例梳理（多知識點）》 
    return average_prices

def main(argc, argv, envp):
    weekdays, close_prices = read_data('aapl.csv')
    average_prices = cal_average_prices(weekdays, close_prices)
    max_index = np.argmax(average_prices)
    # pandas中對應的屬性爲 idxmax/ idxmin
    min_index = np.argmin(average_prices)
    for weekday, average_prices in enumerate(average_prices):
        print(g_weekdays[weekday], ':', average_prices,
            # weekday 作爲g_weekday的索引號
            '(max)' if(weekday == max_index) else
            '(min)' if(weekday == min_index) else '')
        # print() print內部竟然還有這種if..else..操作
    return 0

if __name__ == '__main__':
    sys.exit(main(len(sys.argv), sys.argv, os.environ))