Python NumPy: How to Save NumPy Arrays to CSV in Python

Learn how to export NumPy arrays to CSV using np.savetxt() for numeric data or Pandas for labeled datasets with headers, including formatting, performance comparisons, and best practices for different use cases.

Exporting NumPy arrays to CSV files enables data sharing with spreadsheets, other programs, and collaborators. NumPy and Pandas each offer methods suited to different requirements.

Using np.savetxt() - Pure NumPy

The simplest approach for numeric data without dependencies:

import numpy as np

arr = np.array([
    [1.234, 5.678],
    [9.012, 3.456]
])

np.savetxt('data.csv', arr, delimiter=',', fmt='%.2f')

Result in data.csv:

1.23,5.68
9.01,3.46

Formatting Options

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

np.savetxt('default.csv', arr, delimiter=',')

np.savetxt('integers.csv', arr, delimiter=',', fmt='%d')

np.savetxt('floats.csv', arr, delimiter=',', fmt='%.2f')

np.savetxt('mixed.csv', arr, delimiter=',', fmt=['%d', '%.1f', '%.3f'])

Adding Headers and Comments

import numpy as np

arr = np.array([[1, 100], [2, 200], [3, 300]])

np.savetxt(
    'with_header.csv',
    arr,
    delimiter=',',
    fmt='%d',
    header='ID,Value',
    comments=''  # Remove default '# ' prefix
)

Result:

ID,Value
1,100
2,200
3,300

Using Pandas - With Column Headers

Best for labeled data that needs to be shared:

import pandas as pd
import numpy as np

arr = np.array([
    [1, 100, 10.5],
    [2, 200, 20.3],
    [3, 300, 30.1]
])

df = pd.DataFrame(arr, columns=['ID', 'Value', 'Rate'])
df.to_csv('report.csv', index=False)

Result in report.csv:

ID,Value,Rate
1.0,100.0,10.5
2.0,200.0,20.3
3.0,300.0,30.1

Controlling Data Types

import pandas as pd
import numpy as np

arr = np.array([[1, 100], [2, 200], [3, 300]])

df = pd.DataFrame(arr, columns=['ID', 'Value'])

df['ID'] = df['ID'].astype(int)
df['Value'] = df['Value'].astype(int)

df.to_csv('clean.csv', index=False)

Result:

ID,Value
1,100
2,200
3,300

Adding Row Labels

import pandas as pd
import numpy as np

arr = np.array([[85, 90], [78, 88], [92, 95]])

df = pd.DataFrame(
    arr,
    columns=['Math', 'Science'],
    index=['Alice', 'Bob', 'Charlie']
)

df.to_csv('scores.csv', index_label='Student')

Result:

Student,Math,Science
Alice,85,90
Bob,78,88
Charlie,92,95

Binary Format - For Speed

When data stays within Python/NumPy, binary formats are much faster:

import numpy as np

arr = np.random.rand(1000, 1000)

np.save('data.npy', arr)

loaded = np.load('data.npy')
print(np.array_equal(arr, loaded))  # True

Multiple Arrays

import numpy as np

x = np.array([1, 2, 3])
y = np.array([4, 5, 6])

np.savez('multiple.npz', x=x, y=y)

data = np.load('multiple.npz')
print(data['x'])  # [1 2 3]
print(data['y'])  # [4 5 6]

np.savez_compressed('compressed.npz', x=x, y=y)

Performance Comparison

import numpy as np
import pandas as pd
import timeit

arr = np.random.rand(10000, 100)

def save_numpy():
    np.savetxt('test_np.csv', arr, delimiter=',')

def save_pandas():
    pd.DataFrame(arr).to_csv('test_pd.csv', index=False)

def save_binary():
    np.save('test.npy', arr)

print(f"np.savetxt: {timeit.timeit(save_numpy, number=5):.2f}s")
print(f"pandas:     {timeit.timeit(save_pandas, number=5):.2f}s")
print(f"np.save:    {timeit.timeit(save_binary, number=5):.4f}s")

Typical output:

np.savetxt: 8.52s
pandas:     4.31s
np.save:    0.08s

Loading CSV Files

Read CSV files back into NumPy arrays:

import numpy as np

arr = np.loadtxt('data.csv', delimiter=',')

arr = np.loadtxt('with_header.csv', delimiter=',', skiprows=1)

import pandas as pd
df = pd.read_csv('report.csv')
arr = df.values  # or df.to_numpy()

Handling Different Data Types

String Data

import numpy as np

names = np.array(['Alice', 'Bob', 'Charlie'])
scores = np.array([85, 90, 78])

combined = np.column_stack([names, scores])
np.savetxt('names.csv', combined, delimiter=',', fmt='%s')

import pandas as pd
df = pd.DataFrame({'Name': names, 'Score': scores})
df.to_csv('names_pd.csv', index=False)

Complex Numbers

import numpy as np

complex_arr = np.array([1+2j, 3+4j, 5+6j])

np.savetxt(
    'complex.csv',
    np.column_stack([complex_arr.real, complex_arr.imag]),
    delimiter=',',
    header='real,imag',
    comments=''
)

Summary

Method	Speed	Headers	Best For
`np.savetxt()`	Medium	Manual	Raw numeric data
Pandas	Medium	✅ Easy	Labeled, shareable data
`np.save()`	⚡ Fast	N/A	Internal storage

Format	Human Readable	Precision	File Size
CSV	✅ Yes	Limited	Large
`.npy`	❌ No	Exact	Medium
`.npz` compressed	❌ No	Exact	Small

Use np.savetxt() for simple numeric exports, Pandas when you need headers and compatibility with spreadsheets, and .npy binary format for internal storage where speed and precision matter.