字幕列表 影片播放 由 AI 自動生成 列印所有字幕 列印翻譯字幕 列印英文字幕 Hello everyone, a warm welcome to you. 大家好,熱烈歡迎你們的到來。 In today's video, we are going to see how to stock two arrays in NumPy. 在今天的視頻中,我們將瞭解如何在 NumPy 中存儲兩個數組。 So as you know, in NumPy, the arrays are the object, right? 大家都知道,在 NumPy 中,數組就是對象,對嗎? So if two arrays are given, how to stock those arrays or how to combine those two arrays. 是以,如果給定了兩個數組,如何將這兩個數組儲存起來,或者如何將這兩個數組組合起來。 So we have few functions in NumPy for that. 是以,NumPy 中有幾個函數可以實現這一功能。 So let's see how to do it. 讓我們來看看如何做到這一點。 So first, let me create two arrays. 首先,讓我創建兩個數組。 I'm importing NumPy. 我正在導入 NumPy。 And let me create two arrays here. 讓我在這裡創建兩個數組。 Let's take an object as AR1. 讓我們以 AR1 為對象。 And AR2. 還有 AR2。 So using array function, I'm creating two arrays here. 使用數組函數,我在這裡創建了兩個數組。 So here we have AR1 and AR2, two one-dimensional arrays, right? 這裡有 AR1 和 AR2 兩個一維數組,對嗎? And if I wanted to combine these two arrays, I can use stock function. 如果我想合併這兩個數組,可以使用股票函數。 So first, let me show you how to use stock function for that. 所以,首先讓我來教你如何使用股票功能。 So let's say stock and I need to pass both the arrays inside this function. 比方說,我需要在這個函數中傳遞股票和數組。 So this will combine these two arrays, right? 這樣就可以將這兩個數組合並起來,對嗎? So as you can see, it combines these two arrays row wise. 是以,正如你所看到的,它將這兩個數組按行排列組合在一起。 And I have given both one-dimensional array in the stock function. 我在股票函數中給出了兩個一維數組。 And if you see the output, it has returned as two dimension, right? 如果你看到輸出結果,它返回的是二維,對嗎? So if you check the shape of this, you can see that we have two dimension array here, right? 所以,如果你查看一下這個形狀,就會發現這裡有一個二維數組,對嗎? So by default, this function combines row wise. 是以,默認情況下,該函數會按行進行組合。 But let's say if I wanted to combine it column wise, then still I can use this function. 但如果我想按列進行組合,還是可以使用這個函數。 And there is a parameter called axis. 還有一個參數叫做軸。 So I can include this parameter where I can give as one. 是以,我可以把這個參數作為一個參數。 So by default, this parameter will take value as zero, which refers to rows, okay? 是以,默認情況下,這個參數的值為 0,也就是行數,明白嗎? When axis is zero, it refers to rows and when axis is one, it refers to columns. 當座標軸為 0 時,它指的是行,當座標軸為 1 時,它指的是列。 So here, if I try to combine these two arrays using stock function by giving axis as one, you can see that these two arrays are combined column wise, right? 是以,在這裡,如果我嘗試使用 stock 函數將這兩個數組合並在一起,將軸作為一個軸,你可以看到這兩個數組是按列合併的,對嗎? So you can use stock function to combine two arrays, okay? 是以,你可以使用股票函數來組合兩個數組,好嗎? And now let's see the other function like vstock. 現在我們來看看其他功能,比如 vstock。 So here I'm going to call this vstock. 在這裡,我把它叫做 vstock。 So vstock stands for vertical stock, okay? Vstock代表垂直庫存,明白嗎? If you wanted to combine two arrays vertically, right? 如果你想垂直組合兩個數組,對嗎? So you can use this vstock and let me pass both the arrays inside this function. 是以,你可以使用這個 vstock,讓我在這個函數中傳遞兩個數組。 So here it gives you again, if you see both are one dimensional array and the output which you got is a two dimensional array, right? 所以,這裡它又給了你一次機會,如果你看到兩個都是一維數組,而你得到的輸出是二維數組,對嗎? So if you check the shape of it, you can see it's two dimension, right? 所以,如果你檢查一下它的形狀,就會發現它是二維的,對嗎? So vstock gives you, it takes two one dimensional array and gives you a two dimensional array where it combines them vertically, right? 是以,vstock 可以獲取兩個一維數組,然後生成一個二維數組,將它們垂直組合起來,對嗎? And if you wanted to combine or stack the array horizontally, then we have function for that called hstock. 如果你想水準合併或堆疊數組,我們有一個名為 hstock 的函數。 So again, I'm going to pass both these arrays. 是以,我要再次傳遞這兩個數組。 And you can see that the two arrays are stacked horizontally, right? 你可以看到這兩個陣列是水準堆疊的,對嗎? And you can see that it's one dimensional array. 你可以看到這是一個一維數組。 So hstock by default gives you it when it takes two one dimensional array, it gives you one dimensional array as the output, right? 是以,默認情況下,當 hstock 使用兩個一維數組時,它的輸出是一維數組,對嗎? So these are the difference between stock, vstock and hstock. 是以,這些就是股票、Vstock 和 Hstock 之間的區別。 So you can stack two arrays using these functions. 是以,您可以使用這些函數堆疊兩個數組。 Thank you. 謝謝。
B2 中高級 中文 美國腔 函數 組合 股票 使用 合併 輸出 堆棧、Vstack 和 Hstack | Numpy 堆棧函數 | Python Numpy 教程 (Stack, Vstack and Hstack | Numpy Stack functions | Python Numpy Tutorial) 9 0 鄭力瑋 發佈於 2024 年 07 月 07 日 更多分享 分享 收藏 回報 影片單字