Created
October 27, 2021 03:00
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def reduce_memory_usage(df, verbose=True): | |
numerics = ["int8", "int16", "int32", "int64", "float16", "float32", "float64"] | |
start_mem = df.memory_usage().sum() / 1024 ** 2 | |
for col in df.columns: | |
col_type = df[col].dtypes | |
if col_type in numerics: | |
c_min = df[col].min() | |
c_max = df[col].max() | |
if str(col_type)[:3] == "int": | |
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: | |
df[col] = df[col].astype(np.int8) | |
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: | |
df[col] = df[col].astype(np.int16) | |
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: | |
df[col] = df[col].astype(np.int32) | |
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: | |
df[col] = df[col].astype(np.int64) | |
else: | |
if ( | |
c_min > np.finfo(np.float16).min | |
and c_max < np.finfo(np.float16).max | |
): | |
df[col] = df[col].astype(np.float16) | |
elif ( | |
c_min > np.finfo(np.float32).min | |
and c_max < np.finfo(np.float32).max | |
): | |
df[col] = df[col].astype(np.float32) | |
else: | |
df[col] = df[col].astype(np.float64) | |
end_mem = df.memory_usage().sum() / 1024 ** 2 | |
if verbose: | |
print( | |
"Mem. usage decreased to {:.2f} Mb ({:.1f}% reduction)".format( | |
end_mem, 100 * (start_mem - end_mem) / start_mem | |
) | |
) | |
return df |
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