Pandas is great for working with tables, but sometimes you need to use the full force of a statistical package to get the job done. That’s where turning your DataFrame into a NumPy array comes.

Turning your DataFrame into a NumPy array means removing the DataFrame properties, and changing your data from a table to an array (or array of arrays).

pandas.DataFrame.to_numpy()

NumPy is a very powerful, very fast, statistical analysis package that is built on top of Python. In fact, NumPy is a dependency for Pandas, meaning you must have NumPy before you can install Pandas.

DataFrame To NumPy Array

This one is pretty simple, but let’s take a look at the parameters for .to_numpy()

• dtype – For if you need to specify the type of data that you’re passing to .to_numpy(). You likely won’t need to set this parameter
• copy (Default: False) – This parameter isn’t widely used either. Setting copy=True will return a full exact copy of a NumPy array. Copy=False will potentially return a view of your NumPy array instead. If you don’t know what the difference is, it’s ok and feel free not to worry about it.
• na_value – The value to use when you have NAs. By default Pandas will return the NA default for that column data type. If you wanted to specify another value, go ahead and get fancy.

import pandas as pd

df = pd.DataFrame([('Foreign Cinema', 'Restaurant', pd.NA),
                   ('Liho Liho', 'Restaurant', 224.0),
                   ('500 Club', 'bar', 80.5),
                   ('The Square', pd.NA, 25.30)],
           columns=('name', 'type', 'AvgBill')
                 )
df

x = df.to_numpy()
x

array([['Foreign Cinema', 'Restaurant', <NA>],
       ['Liho Liho', 'Restaurant', 224.0],
       ['500 Club', 'bar', 80.5],
       ['The Square', <NA>, 25.3]], dtype=object)

type(x)

numpy.ndarray

y = df.to_numpy(na_value='SF')
y

array([['Foreign Cinema', 'Restaurant', 'SF'],
       ['Liho Liho', 'Restaurant', 224.0],
       ['500 Club', 'bar', 80.5],
       ['The Square', 'SF', 25.3]], dtype=object)

Link to code above

Check out more Pandas functions on our Pandas Page

Official Documentation