Memory leak while plotting with pandas and pyplot

Question

I an trying to create a plot for all csvs in a directory. When I run the script below, my RAM memory consumption just goes up monotonically. The code is simple albeit a bit longer:

import multiprocessing
import os
from glob import glob
import pandas as pd
from matplotlib import pyplot as plt

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

root_data_dir = '/home/user1/data/20191121'
root_img_dir = os.path.join(root_data_dir, 'figures')

if not os.path.exists(root_img_dir):
    os.mkdir(root_img_dir)

def plot_file(file):
    print("Processing {}".format(file))
    df = pd.read_csv(file, parse_dates=['date'], index_col='date', compression='xz')
    plt.plot(df)

    base_file = os.path.splitext(os.path.basename(file))[0]
    img_file = os.path.join(root_img_dir, base_file + '.png')

    plt.title(base_file)
    plt.savefig(img_file, dpi=300)
    print("Saved {}".format(img_file))
    plt.close()

multiprocessing.Pool(16).map(plot_file, sorted(glob(os.path.join(root_data_dir, '*.csv.xz'))))

prob the DF was staying around and your system was not in need of free memory to reclaim it. Aggressive garbage collection may have negative impact on the performance. So I would let it decide on its own when to collect the memory though — user3237183
– user3237183, Commented Nov 28, 2019 at 21:05
@lssilva I tried adding del df after plt.close() but didn't work. The system has 32GB of RAM and I ended up consuming all of it within a short period of time after which the machine became unresponsive (I am running a recent version of linux for reference) — s5s
– s5s, Commented Nov 29, 2019 at 11:26
Do you have the issue if you do it sequentially instead of with 16 threads? — user3237183
– user3237183, Commented Nov 29, 2019 at 13:12

user3237183 · Accepted Answer · 2019-11-28 21:04:43Z

2

Add the following code

import gc

.. .. then inside plot_file gc.collect()

def plot_file(file):
    print("Processing {}".format(file))
    df = pd.read_csv(file, parse_dates=['date'], index_col='date', compression='xz')
    plt.plot(df)

    base_file = os.path.splitext(os.path.basename(file))[0]
    img_file = os.path.join(root_img_dir, base_file + '.png')

    plt.title(base_file)
    plt.savefig(img_file, dpi=300)
    print("Saved {}".format(img_file))
    plt.close()
    gc.collect()

answered Nov 28, 2019 at 21:04

user3237183

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