How to find a memory leak in a python application?

Question

Python

Friends, hello everyone!
Each function call below eats up 5 meters of accumulated memory, and so on until overflow. How to find "bottleneck"?

df['horsepower'] = pd.to_numeric(df['horsepower'].replace('?', np.nan))
df['mpg'] = pd.cut(df['mpg'], [8, 16, 24, 32, 50])

def parralax():
  cols = ['displacement', 'cylinders', 'horsepower', 'weight', 'acceleration']
  x = [i for i, _ in enumerate(cols)]
  colours = ['#2e8ad8', '#cd3785', '#c64c00', '#889a00']

  colours = {df['mpg'].cat.categories[i]: colours[i] for i, _ in enumerate(df['mpg'].cat.categories)}

  fig, axes = plt.subplots(1, len(x)-1, sharey=False, figsize=(15,5))

  min_max_range = {}
  for col in cols:
    min_max_range[col] = [df[col].min(), df[col].max(), np.ptp(df[col])]
    df[col] = np.true_divide(df[col] - df[col].min(), np.ptp(df[col]))

  for i, ax in enumerate(axes):
    for idx in df.index:
      mpg_category = df.loc[idx, 'mpg']
      ax.plot(x, df.loc[idx, cols], colours[mpg_category])
    ax.set_xlim([x[i], x[i+1]])
    
  def set_ticks_for_axis(dim, ax, ticks):
    min_val, max_val, val_range = min_max_range[cols[dim]]
    step = val_range / float(ticks-1)
    tick_labels = [round(min_val + step * i, 2) for i in range(ticks)]
    norm_min = df[cols[dim]].min()
    norm_range = np.ptp(df[cols[dim]])
    norm_step = norm_range / float(ticks-1)
    ticks = [round(norm_min + norm_step * i, 2) for i in range(ticks)]
    ax.yaxis.set_ticks(ticks)
    ax.set_yticklabels(tick_labels)

  for dim, ax in enumerate(axes):
    ax.xaxis.set_major_locator(ticker.FixedLocator([dim]))
    set_ticks_for_axis(dim, ax, ticks=6)
    ax.set_xticklabels([cols[dim]])
    

  ax = plt.twinx(axes[-1])
  dim = len(axes)
  ax.xaxis.set_major_locator(ticker.FixedLocator([x[-2], x[-1]]))
  set_ticks_for_axis(dim, ax, ticks=6)
  ax.set_xticklabels([cols[-2], cols[-1]])


  plt.subplots_adjust(wspace=0)

  plt.legend(
    [plt.Line2D((0,1),(0,0), color=colours[cat]) for cat in df['mpg'].cat.categories],
    df['mpg'].cat.categories,
    bbox_to_anchor=(1.2, 1), loc=2, borderaxespad=0.)

  plt.title("Values of car attributes by MPG category")

  plt.show()

Answer 1

https://habr.com/ru/company/mailru/blog/202832/
memory_profiler

Answer 2

Finding using an object graph .