Друзья, всем привет!
Каждый вызов функции ниже съедает 5 метров памяти с накоплением, и так до переполнения. Как найти "узкое место"?
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()
