reduce_memory_usage
The maximum and minimum values for each column are compared with the maximum and minimum values that can be expressed by each dtype, and the dtype is changed to one that uses as little memory as possible as long as the values remain the same.
ććăź dtype ă§èĄšçŸă§ăăæ性ć€ăæć°ć€ăæŻèŒăăă§ăăă ăăĄăąăȘăæ¶èČ»ăăȘă dtype ă«ć€æŽăăă
Install
pip install reduce_memory_usage
How to use
import reduce_memory_usage
df_origin = pd.read_csv('<FILE>')
df = reduce_memory_usage.reduce_memory_usage(df_origin)
### sample
import reduce_memory_usage
import numpy as np
import pandas as pd
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing()
df_origin = pd.DataFrame(data['data'], columns=data['feature_names'])
df = reduce_memory_usage.reduce_memory_usage(df_origin)
>>> Result
# (Origin) Mem. usage decreased to 1.26 Mb
# (New) Mem. usage decreased to 0.32 Mb (75.0% reduction)
