** An selection is basically a one- or multi-dimensional grid of values.** In a Numpy array, in particular,

*all values space from the same type (integer, float)*. How we room going to specify a Numpy array? because that a Numpy array, we have actually the adhering to definitions:

**Rank**: The number of dimensions an array has.

Now let"s obtain started through Python. We begin with the most usual approach. Let"s define produce a Numpy array from a list:

# income Numpy libraryimport numpy as np# specify a Python listmylist = <1, 2, 4, 8># create a Numpy range from the listnumpy_array = np.array(mylist)print("Array: ", numpy_array) Array: <1 2 4 8>

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**np.array**role to transform a list right into a Numpy array.

What if us

*do not define a list and also just input the numbers*together below:

# income Numpy libraryimport numpy as np# Naively entry the numbersnumpy_array = np.array(1,2,4,8)print("Array: ", numpy_array) ---------------------------------------------------------------------------ValueError Traceback (most recent call last) in () 2 3 # Naively entry the numbers----> 4 numpy_array = np.array(1,2,4,8) 5 print("Array: ", numpy_array)ValueError: only 2 non-keyword debates accepted as you deserve to see above, **Python complains**!!

Now, let"s specify a ** two-dimensional** array:

# import Numpy libraryimport numpy as np# Naively input the numbersrow1 = <2,4,6,8>row2 = <1,3,5,7>numpy_array = np.array(

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Let"s take it a look at the above code once again. We identified a matrix. The dispute inside

**np.array**is a list that each of its facets is one more list (see figure above)! The within lists denoted together

**row1**and also

**row2**creates the rows that the matrix and MUST have actually the exact same size!

**Think why?**the was a simple example to showcase how we can produce arrays. I used

**.shape**method to return the Numpy array shape. The output above shows we have actually a matrix v two rows and also four columns.