我在日常工作中使用 pandas，并且我使用的一些数据框非常大（大约数亿行乘以数百列）。有什么方法可以减少 RAM 内存消耗吗？
您可以使用此功能。它通过将数据类型限制为每列所需的最小值来减少数据的大小。
该代码不是我的，我从以下链接复制了它，并根据我的需要进行了调整。https://www.mikulskibartosz.name/how-to-reduce-memory-usage-in-pandas/
def reduce_mem_usage(df, int_cast=True, obj_to_category=False, subset=None):
"""
Iterate through all the columns of a dataframe and modify the data type to reduce memory usage.
:param df: dataframe to reduce (pd.DataFrame)
:param int_cast: indicate if columns should be tried to be casted to int (bool)
:param obj_to_category: convert non-datetime related objects to category dtype (bool)
:param subset: subset of columns to analyse (list)
:return: dataset with the column dtypes adjusted (pd.DataFrame)
"""
start_mem = df.memory_usage().sum() / 1024 ** 2;
gc.collect()
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
cols = subset if subset is not None else df.columns.tolist()
for col in tqdm(cols):
col_type = df[col].dtype
if col_type != object and col_type.name != 'category' and 'datetime' not in col_type.name:
c_min = df[col].min()
c_max = df[col].max()
# test if column can be converted to an integer
treat_as_int = str(col_type)[:3] == 'int'
if int_cast and not treat_as_int:
treat_as_int = check_if_integer(df[col])
if treat_as_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.uint8).min and c_max < np.iinfo(np.uint8).max:
df[col] = df[col].astype(np.uint8)
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.uint16).min and c_max < np.iinfo(np.uint16).max:
df[col] = df[col].astype(np.uint16)
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.uint32).min and c_max < np.iinfo(np.uint32).max:
df[col] = df[col].astype(np.uint32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
elif c_min > np.iinfo(np.uint64).min and c_max < np.iinfo(np.uint64).max:
df[col] = df[col].astype(np.uint64)
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)
elif 'datetime' not in col_type.name and obj_to_category:
df[col] = df[col].astype('category')
gc.collect()
end_mem = df.memory_usage().sum() / 1024 ** 2
print('Memory usage after optimization is: {:.3f} MB'.format(end_mem))
print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
return df
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