只不过这个条件是针对array类型的a的,第二个参数choicelist所要执行的操作是依据condlist而言的,即这里的 [a+10]里面的a+10这个操作,和 [a<6]这个里面的,a<6是对应关系的,即当第一条件里的每个元素满足条件的时候,即为True的时候,就执行相应的操作,如果为false,那么久不执行,而对于不满足的元素,则执行默
a = np.array([1,2,3,4,5,6,7,8,9,10]) result2 = np.select([a <6], [a +10], default=100)print(result2)# array([ 11, 12, 13, 14, 15, 100, 100, 100, 100, 100]) 对应元素满足条件执行操作,否则返回默认值。 4.多条件、多操作 a = np.array([[1,2,3,4,5], [6,7...
8. 数组中所有元素是否有0元素 (python check whether all elements in numpy is zero) 9. 数组找到满足一个条件或多个的数据(python numpy find data that satisfty one or multiple conditions) 10. 数组中所有元素相乘(python numpy multiple every element in an array) 内容: 1. 数组每一行除以这一行的...
(array([0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int64), array([2, 2, 2, 0, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2...
NumPy是一个高性能科学计算和数据分析基础包,同时也是其他数据分析模块(如SciPy与Pandas)的基础;其核心功能是高维数组ndarray: 数组对象本身具有大小固定、元素数据类型相同等特性; 提供了大量数值运算函数,以有效地进行向量、矩阵运算; 数组生成: 数组创建:np.array(ary_like, dtype=None, copy=True, order='K', ...
图表元素:Chart elements 折线图:line_chart 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import streamlit as st import pandas as pd import numpy as np chart_data = pd.DataFrame( np.random.randn(20, 3), columns=['a', 'b', 'c']) st.line_chart(chart_data) 折线面积图:area_chart...
SELECT sr_customer_sk, -- return order ratio count(distinct(sr_ticket_number)) as returns_count, -- return ss_item_sk ratio COUNT(sr_item_sk) as returns_items, -- return monetary amount ratio SUM( sr_return_amt ) AS returns_money ...
import numpy as np hv.extension('bokeh') # Sample matrix representing the export volumes between 5 countries export_data = np.array([[0, 50, 30, 20, 10], [10, 0, 40, 30, 20], [20, 10, 0, 35, 25], [30, 20, 10, 0, 40], ...
In the first example, all of the elements have been multiplied by 10. In the second, the corresponding values in each “cell” in the array have been added to each other. Note In this chapter and throughout the book, I use the standard NumPy convention of always using import numpy as ...
over the elements of the list and multiply each one by five. However, if your data is instead represented as an array, you can multiply every element in the array by five in a single bound. Behind the scenes, the highly optimized NumPy library is doing the iteration as fast as possible...