** 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.

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