Pandas df.astype('float32') loses a lot of precision

Question

Why does a float64 value 123456789.0 in a Pandas.DataFrame gets converted to 123456792.0, preserving only 7 significant digits?

import pandas as pd

df = pd.DataFrame([123456789.0])

#              0
# 0  123456789.0

df = df.astype('float32')

#              0
# 0  123456792.0

Isn't it expected that you loss precision when converting to lower precision floating point? — Quang Hoang, Nov 29 '19 at 17:40
https://stackoverflow.com/questions/23770993/float64-to-float32-conversion-gives-unexpected-result — BENY, Nov 29 '19 at 17:41

Giorgos Myrianthous · Answer 1 · 2019-11-29T18:14:18.700

Essentially, float32 is numpy's dtype. The reason why you see some difference in the precision when converting float64 to float32 is because 123456789.0 cannot be accurately represented using float32 which is a 32-bit dtype (1 sign bit, 8 bits exponent, 23 bits mantissa).

In general, float32 requires half of the memory that float64 requires to represent a numerical value , however float32 can represent numbers less accurately compared to float64.

Note there is no workaround for this. If you need to represent particular numbers that cannot be represented using a 32-bit dtype like float32, then go for higher precision dtypes (float64).

Pandas df.astype('float32') loses a lot of precision

1 Answers1