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Numpy ndarray

The numpy library¹ gives Python the ability to work with matrices and arrays.

1. https://numpy.org/doc/stable/

import numpy as np

Pandas started off as an extension to numpy.ndarray that provided more features suitable for data analysis. Since then, Pandas has evolved to the point that it shouldn’t be thought of as a collection of numpy arrays, since the two libraries are different.

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import pandas as pd

df = pd.read_csv('data/concat_1.csv')
print(df)

   A  B  C  D
0 a0 b0 c0 d0
1 a1 b1 c1 d1
2 a2 b2 c2 d2
3 a3 b3 c3 d3

If you do need to get the numpy.ndarray values from a Series or DataFrame, you can use the values attribute.

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a = df['A']
print(a)

0    a0
1    a1
2    a2
3    a3
Name: A, dtype: object

print(type(a))

<class 'pandas.core.series.Series'>

print(a.values)

['a0' 'a1' 'a2' 'a3']

print(type(a.values))

<class 'numpy.ndarray'>

This is particularly helpful when cleaning data in Pandas. You can then use your newly cleaned data in other Python libraries that do not fully support the Series and DataFrame objects. The Software-Carpentry Python Inflammation lesson² uses numpy and can be another good reference to learn about the library and Python as a whole.

2. https://swcarpentry.github.io/python-novice-inflammation/