Created
January 4, 2019 22:31
-
-
Save eherrerosj/fdac2b3761a10332da163b1c90367890 to your computer and use it in GitHub Desktop.
Pandas reduce memory usage optimizing dtypes
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
def reduce_mem_usage(df, verbose=True): | |
numerics = ['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 {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem)) | |
return df |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment