Put the Same Readings Into the Right Arrangement

Botie's logger sends six readings in order: all three greenhouses on Monday, then all three on Tuesday. The message has no row breaks. We need to restore the table before asking for a daily or greenhouse summary.

Reshaping changes how existing entries are grouped. Transposing exchanges axes. Joining combines separate collections. These operations can produce similar-looking shapes while assigning very different meanings to their positions.

The logger writes one complete day before starting another. Try a small list whose values you can recognise, then follow one greenhouse through the restored rows and columns.

The default reshape order reads the original entries in row order and puts them into the requested row order. It cannot create or discard entries. The product of the new dimensions must match the number of existing entries.

One dimension may be minus one, meaning infer this length from the remaining dimensions and the number of entries. It does not mean a missing observation.

import numpy as np

flat = np.array([10,20,30,14,22,36])
days = flat.reshape(2, 3)
print(days.tolist())
print(flat.reshape(-1, 3).shape)
print(days.ravel().tolist())

[[10, 20, 30], [14, 22, 36]]
(2, 3)
[10, 20, 30, 14, 22, 36]

Regrouping the sequence into three pairs is not the same as following each greenhouse across days. A transpose exchanges the roles of rows and columns while keeping each value attached to its original coordinates.

The T attribute is a convenient transpose for a matrix. On arrays with more axes, name the intended axis order explicitly rather than assuming that reversing all axes is what the task needs.

import numpy as np

days = np.array([[10,20,30],[14,22,36]])
print(days.reshape(3, 2).tolist())
print(days.T.tolist())

[[10, 20], [30, 14], [22, 36]]
[[10, 14], [20, 22], [30, 36]]

concatenate extends an existing axis. stack creates a new axis to keep its inputs separate. Suppose we have two daily vectors. Concatenating them makes a longer vector; stacking them makes a table of days.

When joining tables, all dimensions other than the concatenation axis must match. When stacking, the input shapes must match because each becomes a slice along the new axis.

import numpy as np

monday = np.array([10,20,30])
tuesday = np.array([14,22,36])
print(np.concatenate([monday, tuesday]).shape)
days = np.stack([monday, tuesday], axis=0)
print(days.tolist())
wednesday = np.array([[16,24,38]])
print(np.concatenate([days, wednesday], axis=0).tolist())

(6,)
[[10, 20, 30], [14, 22, 36]]
[[10, 20, 30], [14, 22, 36], [16, 24, 38]]

A model often receives a table with observations in rows and features in columns. column_stack turns equally long vectors into feature columns. A batch adds another meaning: a group of observations processed together.

When there are three or more axes, write down what each one means before manipulating the shape. Equal lengths do not make two axes interchangeable.

import numpy as np

temperature = np.array([12., 18., 24.])
humidity = np.array([50., 60., 70.])
features = np.column_stack([temperature, humidity])
batch = np.expand_dims(features, axis=0)
print(features.tolist())
print(batch.shape)
print(np.squeeze(batch, axis=0).shape)

[[12.0, 50.0], [18.0, 60.0], [24.0, 70.0]]
(1, 3, 2)
(3, 2)

Botie's logger sent two days of readings from three greenhouses. We can reshape those six numbers into several valid tables. But reshape only follows the sequence of entries and gives it new boundaries. It has no way to know which readings belong to Monday.

Transpose does a different job: it exchanges the existing axes, so the relationships between days and greenhouses remain attached to their positions. This becomes important when we flatten an image or a batch for a model. We need to remember the original axis order to reconstruct it; getting the dimensions right alone does not put every value back where it belongs.

import numpy as np
a = np.array([[10, 11, 12], [20, 21, 22]])
print(a.reshape(3, 2).tolist())
print(a.T.tolist())

[[10, 11], [12, 20], [21, 22]]
[[10, 20], [11, 21], [12, 22]]

concatenate joins along an existing axis. If its inputs are daily tables, joining along axis zero makes a longer table of days. stack creates a new axis, so equal-shaped weekly tables can remain separate weeks.

The choice affects later reductions. A mean over the new week axis compares corresponding positions between weeks; a mean over concatenated days treats all days as one collection. Neither is automatically correct. Use the structure required by the question, and keep calendar labels when a position alone does not identify the observation.