Import Convention¶

Standard Import¶

1. The np Alias¶

NumPy is conventionally imported with the alias np.

import numpy as np

This is the universal standard across the Python scientific computing community.

2. Why np¶

The alias provides several benefits:

Brevity: np.array() vs numpy.array()
Readability: Instantly recognizable
Convention: Expected in all codebases

3. Community Standard¶

Using np signals familiarity with best practices.

import numpy as np

def main():
    # Immediately recognizable as NumPy
    a = np.array([1, 2, 3])
    b = np.zeros((3, 3))
    c = np.linspace(0, 1, 10)

if __name__ == "__main__":
    main()

Alternative Imports¶

1. Full Name Import¶

Possible but not recommended.

import numpy

def main():
    # Verbose
    a = numpy.array([1, 2, 3])

if __name__ == "__main__":
    main()

2. Selective Import¶

Import specific functions directly.

from numpy import array, zeros, linspace

def main():
    a = array([1, 2, 3])
    b = zeros((3, 3))
    c = linspace(0, 1, 10)

if __name__ == "__main__":
    main()

3. Star Import¶

Avoid this—pollutes namespace.

# NOT recommended
from numpy import *

def main():
    # Unclear where functions come from
    a = array([1, 2, 3])  # numpy or builtin?

if __name__ == "__main__":
    main()

1. Common Pattern¶

Scientific Python libraries follow similar conventions.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

2. Full Stack Import¶

Typical scientific computing imports.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats

def main():
    data = np.random.randn(100)
    df = pd.DataFrame({'values': data})
    print(stats.describe(data))

if __name__ == "__main__":
    main()

3. Type Hints Import¶

For type annotations.

import numpy as np
from numpy.typing import NDArray

def normalize(arr: NDArray[np.float64]) -> NDArray[np.float64]:
    return arr / arr.sum()

def main():
    a = np.array([1.0, 2.0, 3.0])
    result = normalize(a)
    print(result)

if __name__ == "__main__":
    main()