Tools and NumPy arrays

Tools and reproducibility

NumPy provides multidimensional numeric arrays and operations on them; pandas provides tables with labeled columns and rows; Matplotlib provides plotting tools. Official introductory guides: https://numpy.org/doc/stable/user/absolute_beginners.html, https://pandas.pydata.org/docs/user_guide/10min.html, https://matplotlib.org/stable/users/explain/quick_start.html. These libraries do not replace validation of data meaning: a correct mean call will not fix a mixture of meters and centimeters in one column.

Create a separate project with Python 3.14 and add dependencies to its environment. In the PyCharm terminal, verify that the command uses the project interpreter. For a uv project:

uv add numpy pandas matplotlib
uv add --dev pytest
uv run python -c "import numpy; print(numpy.__version__)"

For a venv environment, the equivalent installation is python -m pip install numpy pandas matplotlib pytest. Subsequent launch commands are written as python file.py; in a uv project, use uv run python file.py. Pin dependencies and the Python version, and save the original CSV and program text in Git. Do not add environment files or temporary images to the repository.

The examples use only synthetic learning datasets. They are included directly in the code or as CSV text, so another person can reproduce the results without an external website or account. If a file changes, an old report should not be silently overwritten: first define the results directory and naming rules. Do not name your own modules numpy.py or pandas.py, to avoid shadowing an installed library.

The ndarray array: values, type, and shape

An ndarray array (array) stores elements with a single dtype. The shape attribute is a tuple of axis lengths, ndim is the number of axes, and size is the total number of elements. A one-dimensional array has shape (n,), while a table has (rows, columns). Shape (n, 1) is not identical to (n,): the first has two axes, which matters for calculations.

import numpy as np

values = np.array([[10, 20, 30], [12, 22, 32]], dtype=float)
print(values.shape, values.ndim, values.size)
print(values.dtype)
print(values[1, 2])

Output: (2, 3) 2 6, float64, 32.0. For a nested list, access would look like items[1][2]; an array accepts comma-separated axis indices within square brackets. All rows of the example matrix have the same length. Do not try to hide uneven nested lists with an arbitrary dtype=object: that is a different model.

The zeros and ones functions create arrays of a given shape, arange generates values with a step, and linspace generates a given number of points between bounds. For a fractional step, the number of arange elements can depend on binary rounding; when the number of points and inclusion of the endpoint matter, choose linspace.

import numpy as np

print(np.zeros((2, 3), dtype=int))
print(np.arange(1, 7).reshape(2, 3))
print(np.linspace(0, 1, 5))

Output:

[[0 0 0]
 [0 0 0]]
[[1 2 3]
 [4 5 6]]
[0.   0.25 0.5  0.75 1.  ]

reshape preserves the element count: six values can be represented as (2, 3), but not (2, 4). One length may be specified as −1 so the library calculates it. Changing the shape does not necessarily create a copy; if data independence is part of the contract, call copy explicitly and check the shape after transformation.

Choosing dtype and handling errors

The int64 type has a limited range, unlike Python's arbitrary-precision int. The float64 type represents approximate real values. An array containing strings and numbers may convert the numbers to text if you rely only on automatic type inference. At the program boundary, explicitly define the expected columns and conversions.

For monetary totals, storing integer kopiykas and checking the permitted range is convenient, rather than adding binary fractions. For measurements, choose a unit and precision. np.isfinite excludes NaN and infinities; ordinary division by zero does not always raise a Python exception and may produce infinity with a warning. Do not suppress warnings before finding their cause.