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Here are the results for my case:
Pandas: 1.0.3
NumPy: 1.18.1
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000000 entries, 0 to 9999999
Data columns (total 9 columns):
# Column Dtype
--- ------ -----
0 int64 int64
1 int32 int32
2 int16 int16
3 int8 int8
4 uint8 uint8
5 longdouble float128
6 float64 float64
7 float32 float32
8 float16 float16
dtypes: float128(1), float16(1), float32(1), float64(1), int16(1), int32(1), int64(1), int8(1), uint8(1)
memory usage: 438.7 MB
Working on int64
Working on int32
Working on int16
Working on int8
Working on uint8
Working on longdouble
Working on float64
Working on float32
Working on float16
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1800 entries, 0 to 1799
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 fn_name 1800 non-null object
1 col 1800 non-null object
2 t 1800 non-null float64
dtypes: float64(1), object(2)
memory usage: 42.3+ KB |
Windows 10 laptop, i7, 16GB ram
Linux (Ubuntu) computing blade, 16 core, 192GB ram:
pickle can't be uploaded here: https://asktom.be/python/tmp/ |
one more on a fresh env with only
|
with the last pre-1.0 version:
|
just for fun, ran the previous code (removing fstrings etc) on py27:
|
i7z is Intel only, hence
inxi -C
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