- python - What does . shape [] do in for i in range (Y. shape [0 . . .
The shape attribute for numpy arrays returns the dimensions of the array If Y has n rows and m columns, then Y shape is (n,m) So Y shape[0] is n
- What does shape[0] and shape[1] do in python? - Stack Overflow
In python shape[0] returns the dimension but in this code it is returning total number of set Please can someone tell me work of shape[0] and shape[1]? Code: m_train = train_set_x_orig shape[0]
- arrays - what does numpy ndarray shape do? - Stack Overflow
yourarray shape or np shape() or np ma shape() returns the shape of your ndarray as a tuple; And you can get the (number of) dimensions of your array using yourarray ndim or np ndim() (i e it gives the n of the ndarray since all arrays in NumPy are just n-dimensional arrays (shortly called as ndarray s)) For a 1D array, the shape would be (n,) where n is the number of elements in your array
- python - x. shape [0] vs x [0]. shape in NumPy - Stack Overflow
On the other hand, x shape is a 2-tuple which represents the shape of x, which in this case is (10, 1024) x shape[0] gives the first element in that tuple, which is 10 Here's a demo with some smaller numbers, which should hopefully be easier to understand
- shape = 19, shape = 20 and shape=16 in R graphics [duplicate]
In R graphics and ggplot2 we can specify the shape of the points I am wondering what is the main difference between shape = 19, shape = 20 and shape = 16? Is it the size? This post might consider
- Making a combined legend for color and shape legend in ggplot
Now in the plot we get a legend that is split into shape and color However what I would want is a legend in which we get a unique shape AND color for each point
- python - What does range (y. shape [1]) mean in for i in range . . .
I'm trying to find out how this above-mentioned piece of code works in a layman sense? for context, this code contains Numpy, Seaborn, Pandas and matplotlib below is the line of code: dataset2 = d
- python - Why dataframe. shape [0] prints an integer, but dataframe . . .
train shape[0] python returned 1467 - an integer Curious how Pandas handles these two different inputs, and why they are different Is this a specific feature, or just a quirk?
|