Incomplete sheet

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Matrices

2D-array operations

Create

Action	Code	Details
Undefined (size n x m)	np.empty((n, m))	Warning: don't use the initial values!
Zeros (size n x m)	np.zeros((n, m))
Ones (size n x m)	np.ones((n, m))
True values (size n x m)	np.full((n, m), fill_value=True)
False values (size n x m)	np.full((n, m), fill_value=False)
Fill with value v (size n x m)	np.full((n, m), fill_value=v)
Identity matrix of size n x m	np.eye(n, m)
Diagonal matrix from vector	np.diag(v)
From vectors as rows	np.row_stack((v1, v2))
From vectors as rows	np.vstack((v1, v2))
From vectors as rows	np.array([v1, v2])
From vectors as columns	np.column_stack((v1, v2))
From vectors as columns	np.stack((v1, v2), axis=1)
From vectors as columns	np.vstack((v1, v2)).transpose()

Extract

Action	Code	Details
Number of elements	m.size
Hash	hash(m.data.tobytes())
Element at row i, column j	m[i,j]

Update

All operations are in-place

Action	Code	Details
Update element at row i, column j	m[i,j] = v
Fill with scalar value	m.fill(v)
Fill with array values	m[...] = np.array(3)
Fill row i with scalar value	m[i] = v
Fill column j with scalar value	m[:,j] = v

Reshape

Action	Code	Details
Transpose	m.transpose()
Resize to shape, fill with zeros	m.resize(m, d)

Derive

All operations create a new instance

Map

Operations are element-wise and preserve the shape of the matrix.

Action	Code	Details
Increment all elements with scalar value	m + v
Increment elements by the respective element of another matrix	m + m2	Must have same shape
Find min between two matrices	np.fmin(m, m2)
Find max between two matrices	np.fmax(m, m2)
Clip (truncate) between [a, b]	np.clip(m, a_min=a, a_max=b)	a_min < a_max is not checked

Order

Operations preserve the shape of the matrix.

Action	Code	Details
Reverse elements	np.flip(m)	Flattened view in reverse order
Sort elements descending	np.sort(m)	NaNs are last
Sort elements by column descending	np.sort(m, axis=0)
Sort elements per row descending	np.sort(m, axis=1)
Reverse column order	np.fliplr(m)
Reverse row order	np.flipud(m)

Operations that increase the size of the matrix

Action	Code	Details
Pad with sclar value	np.pad(m, pad_width=1, constant_values=v)
Pad with edge element	np.pad(m, pad_width=1, mode='edge')

Shrink

Operations that reduce the shape of the matrix

Action	Code	Details
Diagonal vector	np.diag(m)

Aggregate

Summarize along an axis. Set axis=1 for per-row operation.

Action	Code	Details
Min per column	np.min(m, axis=0)	Use nanmin() to ignore NaNs
Max per column	np.max(m, axis=0)	Use nanmax() to ignore NaNs
Max - min, per column	np.ptp(m, axis=0)
Sum per column	np.sum(m, axis=0)	Use nansum() to ignore NaNs
Mean per column	np.mean(m, axis=0)	Use nanmean() to ignore NaNs
Median per column	np.median(m, axis=0)	Use nanmedian() to ignore NaNs

Reshape

Action	Code	Details
Transpose	m.T
Transpose	m.transpose()
Rotate	np.rot90(m)
Reshape to specified dimensions	m.reshape(d)

Convert

Action	Code	Details
1D array (concat rows)	m.flatten()
1D array (concat columns)	m.T.flatten()	More intuitive than specifying mode
1D array (concat columns)	m.flatten(mode='F')
List of rows	m.tolist()