import os
import pandas as pd

DATA_DIR='../data'

data = dict()
for root, dirs, files in os.walk(DATA_DIR):
    for filename in files:
        asset = filename.split('.')[0]
        file_path = os.path.join(DATA_DIR, filename)
        series = pd.read_csv(file_path, sep=",", index_col="date")
        data[asset] = series

print("loaded")

# measure memory after loading
from memory_profiler import memory_usage
print(memory_usage())

48a0d24d4e176447b8d46f3ef820aa23113dcd6e
<mask token>

<mask token>
for root, dirs, files in os.walk(DATA_DIR):
    for filename in files:
        asset = filename.split('.')[0]
        file_path = os.path.join(DATA_DIR, filename)
        series = pd.read_csv(file_path, sep=',', index_col='date')
        data[asset] = series
print('loaded')
<mask token>
print(memory_usage())

<mask token>
DATA_DIR = '../data'
data = dict()
for root, dirs, files in os.walk(DATA_DIR):
    for filename in files:
        asset = filename.split('.')[0]
        file_path = os.path.join(DATA_DIR, filename)
        series = pd.read_csv(file_path, sep=',', index_col='date')
        data[asset] = series
print('loaded')
<mask token>
print(memory_usage())

import os
import pandas as pd
DATA_DIR = '../data'
data = dict()
for root, dirs, files in os.walk(DATA_DIR):
    for filename in files:
        asset = filename.split('.')[0]
        file_path = os.path.join(DATA_DIR, filename)
        series = pd.read_csv(file_path, sep=',', index_col='date')
        data[asset] = series
print('loaded')
from memory_profiler import memory_usage
print(memory_usage())

import os
import pandas as pd

DATA_DIR='../data'

data = dict()
for root, dirs, files in os.walk(DATA_DIR):
    for filename in files:
        asset = filename.split('.')[0]
        file_path = os.path.join(DATA_DIR, filename)
        series = pd.read_csv(file_path, sep=",", index_col="date")
        data[asset] = series

print("loaded")

# measure memory after loading
from memory_profiler import memory_usage
print(memory_usage())
