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load data (reduce memory usage)
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""" | |
load data(reduce memory usage) | |
https://www.kaggle.com/gemartin/load-data-reduce-memory-usage | |
""" | |
import pandas as pd | |
import numpy as np | |
def reduce_mem_usage(df): | |
""" iterate through all the columns of a dataframe and modify the data type | |
to reduce memory usage. | |
""" | |
start_mem = df.memory_usage().sum() / 1024**2 | |
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) | |
for col in df.columns: | |
col_type = df[col].dtype | |
if col_type != object: | |
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) | |
else: | |
df[col] = df[col].astype('category') | |
end_mem = df.memory_usage().sum() / 1024**2 | |
print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) | |
print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) | |
return df | |
def import_data(file): | |
"""create a dataframe and optimize its memory usage""" | |
df = pd.read_csv(file, parse_dates=True, keep_date_col=True) | |
df = reduce_mem_usage(df) | |
return df | |
print('-' * 80) | |
print('train') | |
train = import_data('../input/application_train.csv') | |
print('-' * 80) | |
print('test') | |
test = import_data('../input/application_test.csv') |
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