File I/O: save, load, savetxt

NumPy file I/O is the set of functions that write arrays to disk and read them back — np.save and np.load for compact binary .npy files, np.savez for bundling several arrays into one .npz archive, and np.savetxt and np.loadtxt for human-readable CSV and text. In this lesson you'll round-trip arrays through binary and text formats, save multiple arrays at once, control the CSV delimiter and number format, and learn when each format is the right choice.

Learn File I/O: save, load, savetxt in our free NumPy course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick…

Part of the free Numpy course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

np.save('name.npy', arr) writes an array to a compact binary file, and np.load('name.npy') reads it back. The .npy format stores the exact dtype and shape , so what you load is byte-for-byte identical to what you saved.

Expected output: the same 2×3 array, (2, 3) int64 , and True — the data round-trips perfectly.

Need to store several arrays together? np.savez('bundle.npz', x=a, y=b) packs them into one .npz archive. Loading returns a dictionary-like object; read each array back by the keyword name you gave it.

Expected output: ['x', 'y'] , then [0 1 2 3 4] and [1.5 2.5] .

When a human or another program (Excel, R, a database importer) must read the file, write text . np.savetxt('data.csv', arr, delimiter=',', fmt='%.2f') controls the separator and number format; np.loadtxt('data.csv', delimiter=',') reads it back into an array.

Handy savetxt options: delimiter=',' for CSV, fmt='%.2f' for two decimals, fmt='%d' for integers, and header='a,b,c' for a comment header line.

Expected output: the array reloaded as floats, with values rounded to 2 decimals, dtype float64 . (Note the tiny precision loss from text rounding.)

The choice comes down to who reads the file next . If it is your own NumPy program, use binary — it is faster, smaller, and lossless. If a person or a non-NumPy tool needs it, use text.

Expected output: the binary reload matches the original; the text reload is rounded to [0.33 0.67 0.14] .

Replace each ___ so the program saves an array to a binary file and loads it back unchanged.

Expected output: [ 88 92 79 100] then True . (Answers: save , load , loaded .)

❌ ValueError: could not convert string to float

np.loadtxt hit a header row or text it could not parse as a number.

✅ Fix: skip the header with skiprows=1 , or use np.genfromtxt for messy data.

❌ Wrong delimiter splits the file incorrectly

By default savetxt uses spaces; reading a comma file without delimiter=',' fails.

✅ Fix: pass the same delimiter to both savetxt and loadtxt.

Save a 2D integer table to a CSV with a header line, then load just the numbers back.

Lesson 20 complete — your arrays can live on disk!

You can now round-trip arrays through binary .npy and .npz files, write and read CSV text with custom delimiters and formats, and pick the right format for the job.

🚀 Up next: Performance — learn why vectorization beats Python loops and how to write fast code.

Practice quiz

• Browse all free courses
• Cheat sheets & quizzes
• Coding dictionary