NumPy cheatsheet by Lavanya and
pip install numpy
*or*
conda install numpy**import** *numpy* **as** *np*|
ndarray.shape
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Tuple of array shape
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ndarray.ndim
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Number of array dimensions as interger
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ndarray.size
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Number of elements in the array
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ndarray,dtype
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Data type of array’s elements
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ndarray.base
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To check if object has its own memory
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arr[0]
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Returns the element at index 0
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arr[1,2]
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Returns array element on index [1][2]
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arr[0:3]
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Returns the elements at indices on outer dimension
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arr[0:3,2]
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Returns the elements on rows 0,1,2 at column 2
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arr<n
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Returns an array with boolean values
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~arr
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Returns an array with boolean values
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np.mean(arr,axis=0/1)
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Compute the arithmetic mean along the specified axis.
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arr.sum()
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Sum of array elements over a given axis
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arr.min()
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Return the minimum along a given axis
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arr.max()
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Return the maximum along a given axis
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np.var(arr)
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Compute the variance along the specified axis
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np.std(arr)
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Compute the standard deviation along the specified axis.
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arr.corrcoef()
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Return Pearson product-moment correlation coefficients
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np.array(object)
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Creates an array
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np.array([1,2,3])
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1D array
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np.array([(1,2,3),(4,5,6)])
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2D array
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np.zeros(shape)
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Return a new array of given shape and type, filled with zeros
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np.ones(shape)
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Return a new array of given shape and type, filled with ones
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np.eye(no. of rows)
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Return a 2-D array with ones on the diagonal and zeros elsewhere
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np.arange(start,stop,step)
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Return evenly spaced values within a given interval.
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np.random.rand(shape)
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Return array of random floats between 0–1 of fiven shape
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np.random.randint(low,high)
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Return random integers from low (inclusive) to high (exclusive)
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np.linspace(start, stop, n)
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Returns n evenly spaced numbers over a specified interval
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np.sort(arr)
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Returns a sorted copy of the array
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np.argsort(arr)
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Returns the indices that would sort an array
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np.resize(a, new_shape)
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Return a new array with the specified shape
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np.dot(arr1, arr2)
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Dot product of two arrays
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arr.copy()
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Returns a copy of the array
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arr.view()
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New view of array with the same data
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arr.flatten()
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Return a copy of the array collapsed into 1D
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arr.reshape(new_shape)
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Returns an array containing the same data with a new shape
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np.add(arr_1, arr_2)
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Add arguments element-wise
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np.subtract(arr_1, arr_2)
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Subtract arguments, element-wise
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np.multiply(arr_1, arr_2)
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Multiply arguments, element-wise
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np.divide(arr_1, arr_2)
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Divide arguments, element-wise
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np.power(arr_1, arr_2)
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First array elements raised to powers from second array, element-wise
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np.sqrt(arr)
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Return the non-negative square-root of an array, element-wise
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np.log(arr)
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Natural logarithm, element-wise
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np.ceil(arr)
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Rounds up to the nearest int , element-wise
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np.floor(arr)
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Rounds down to the nearest int ,element-wise
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np.abs(arr)
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Absolute value of each element in the array
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np.round(arr)
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Rounds to the nearest int
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