Pandas DataFrame – Convert Row to Numpy Array

Pandas DataFrame – Convert Row to Numpy Array

In Pandas, to convert a row in a DataFrame to a numpy array, you can use to_numpy() method.

Each row of a DataFrame can be accessed as a Series object, and Series objects have a method to_numpy() that returns the numpy array representation of the row (Series object).

The syntax to use to_numpy() method to convert a DataFrame row to a numpy array is

row.to_numpy()

In this tutorial, we will go through some examples, with step by step explanation of how to convert a given row in DataFrame to a Numpy Array.

Examples

1. Convert third row in given DataFrame to numpy array

In this example, we are given a DataFrame in df_input. We have to convert the third row in this DataFrame to a numpy.

Steps

1. Given a DataFrame in df_input with three columns ‘A’, ‘B’, and ‘C’, and five rows.

df_input = pd.DataFrame({
    'A': [2, 4, 6, 8, 10],
    'B': [1, 3, 5, 7, 9],
    'C': [10, 20, 30, 40, 50]})

2. Select the third row using iloc property, and store it in a variable.

row = df_input.iloc[2]

3. Call to_numpy() method on row (Series) object, and store the returned numpy array in a variable row_np_array.

row_np_array = row.to_numpy()

4. You may print the numpy array to output using print() function.

print(row_np_array)

Program

The complete program to convert a row in a given DataFrame to a numpy array .

import pandas as pd

# Take a DataFrame
df_input = pd.DataFrame({
    'A': [2, 4, 6, 8, 10],
    'B': [1, 3, 5, 7, 9],
    'C': [10, 20, 30, 40, 50]})

# Select row
row = df_input.iloc[2]

# Convert row to numpy array
row_np_array = row.to_numpy()

# Print the numpy array
print(row_np_array)

Output

[ 6  5 30]

The row is returned as a one dimensional numpy array.

Summary

In this Pandas Tutorial, we learned how to convert a given row in a DataFrame to a Numpy Array using to_numpy() method, with examples.