Precision
After running this snippet:

import numpy as np a = np.array([0.112233445566778899], dtype=np.float32) b = np.array([0.112233445566778899], dtype=np.float64) print(a, b) 
It print out:

[0.11223345] [0.11223345] 
Why np.float32 and np.float64 have the same output? The answer is: displaying of numpy array need to set options.
Let’s set option before print:

import numpy as np a = np.array([0.112233445566778899], dtype=np.float32) b = np.array([0.112233445566778899], dtype=np.float64) np.set_printoptions(precision=18) print(a, b) 
The result has became:

[0.112233445] [0.1122334455667789] 
which looks much reasonable.
Furthermore, why it prints out ‘0.1122334455667789’ which has only ’16’ precision instead of ’18’? Because the float64 only support about 15~16 precisions, as this reference said.
Hidden metadata
There are two parquet files which look different after using ‘cksum’ to compare. But after we export them as CSV files:

import pandas as pd df = pd.read_parquet("my.parquet") df.to_csv("my.csv") ... 
The two output CSV files are exactly the same.
Then what happened in those previous two parquet files? Dose parquet file have some hidden metadata in it?
As a matter of fact, parquet file will save the ‘index’ of a DataFrame of Pandas while CSV file will not. If we drop the index before writing out the parquet file:

df.reset_index(drop=True) df.to_parquet("my.parquet") ... 
These two parquet files would become identical.