How can I convert a Pandas DataFrame with missing values into a NumPy array using `df.to_numpy()` and preserve data types?

Convert Pandas Dataframe with Missing Values to NumPy Array

Using df.to_numpy()

To convert a Pandas dataframe with missing values into a NumPy array with np.nan representing missing values, use the df.to_numpy() method. It provides a consistent and reliable way to obtain NumPy arrays from both dataframes and index/series objects.

<code class="python">import numpy as np
import pandas as pd

df = pd.DataFrame({
    "A": [np.nan, np.nan, np.nan, 0.1, 0.1, 0.1, 0.1],
    "B": [0.2, np.nan, 0.2, 0.2, 0.2, np.nan, np.nan],
    "C": [np.nan, 0.5, 0.5, np.nan, 0.5, 0.5, np.nan],
}, index=[1, 2, 3, 4, 5, 6, 7])

np_array = df.to_numpy()
print(np_array)</code>

This will output a NumPy array with missing values represented as np.nan:

[[ nan  0.2  nan]
 [ nan  nan  0.5]
 [ nan  0.2  0.5]
 [ 0.1  0.2  nan]
 [ 0.1  0.2  0.5]
 [ 0.1  nan  0.5]
 [ 0.1  nan  nan]]

Preserving Data Types

To preserve data types in the NumPy array, use the np.rec.fromrecords() function:

<code class="python">v = df.reset_index()
np_array_dtypes = np.rec.fromrecords(v, names=v.columns.tolist())
print(np_array_dtypes)</code>

This will output a NumPy array with the original data types preserved as follows:

rec.array([('1', 1, 0.2, 0.5), ('2', 2, np.nan, 0.5), ('3', 3, 0.2, 0.5),
           ('4', 4, 0.2, np.nan), ('5', 5, 0.2, 0.5), ('6', 6, np.nan, 0.5),
           ('7', 7, np.nan, np.nan)],
          dtype=[('index', '<U1'), ('A', '<f8'), ('B', '<f8'), ('C', '<f8')])

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