PlotDockerStats.py

# https://naartti.medium.com/analyse-docker-stats-with-python-pandas-2c2ed735cfcd

import argparse
import pandas as pd
import re
import seaborn as sns
from matplotlib import pyplot as plt


def split_on_slash(df_col, split_index):
    return df_col.apply(lambda x: x.split(" / ")[split_index])


def percentage_to_float(df_col):
    return df_col.apply(lambda x: float(x[0:-1]))


def get_only_characters(string):
    return re.sub('[^a-zA-Z]+', '', string)


def get_only_numbers(string):
    return float(re.sub('[^\d\.]', '', string))


def to_bit(value):
    return int({
                   "b": get_only_numbers(value) * 1,
                   "kib": get_only_numbers(value) * 10e3,
                   "kb": get_only_numbers(value) * 10e3,
                   "mib": get_only_numbers(value) * 10e6,
                   "mb": get_only_numbers(value) * 10e6,
                   "gib": get_only_numbers(value) * 10e9,
                   "gb": get_only_numbers(value) * 10e9,
               }.get(get_only_characters(value).lower(), 0))


parser = argparse.ArgumentParser()
parser.add_argument('log')
args = parser.parse_args()

df = pd.read_csv(args.log, delimiter=r"\s\s+", engine="python")

# remove repeating headers
df = df[df.NAME != "NAME"]

# remove % from CPU & MEM
df["mem_percentage"] = percentage_to_float(df["MEM %"])
df["cpu_percentage"] = percentage_to_float(df["CPU %"])

fig, ax = plt.subplots(1, 1, figsize=(18, 7))

sns.lineplot(x=df.index, y="cpu_percentage", hue="NAME", data=df, drawstyle="steps")
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left')
plt.ylabel("CPU [%]")
plt.title(f"CPU")
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
#plt.ylim(0, 2400)
plt.grid()
plt.show()