sq_t = df_stock_quantity_log[['is_stockout']].resample('T').ffill().fillna(0).astype(int).squeeze()

nrows =10

fig, axs = plt.subplots(nrows=nrows, figsize=(18, 40))

for idx, date in enumerate(df.index[:nrows]):
    s = sq_t[date.strftime('%Y-%m-%d')]
    if len(s) < 24*60:
        s = np.concatenate((np.zeros(24*60-len(s)),s))
    axs[idx].plot(s, label=idx)

plt.legend(loc='best')
plt.show()

fig, axs = plt.subplots(nrows=nrows, figsize=(18, 40))


for idx, date in enumerate(df.index[:nrows]):
    s = sq_t[date.strftime('%Y-%m-%d')]

    if len(s) < 24*60:
        s = np.concatenate((np.zeros(24*60-len(s)),s))
    axs[idx].plot(s, label=idx)
    axs[idx].set_xticks(s)
    axs[idx].set_xticklabels(['h %d' % (ii/ 60,) for ii in range(0, 24*60 , 60)])

plt.legend(loc='best')
plt.show()

sq_t = df_stock_quantity_log[['is_stockout']].resample('T').ffill().fillna(0).astype(int).squeeze()

nrows =10

fig, axs = plt.subplots(nrows=nrows, figsize=(18, 40))


for idx, date in enumerate(df.index[:nrows]):
    s = sq_t[date.strftime('%Y-%m-%d')]

    if len(s) < 24*60:
        s = pd.Series(np.concatenate((np.zeros(24*60-len(s)),s)))
    axs[idx].plot(s.reset_index(drop=True), label=idx)
    axs[idx].set_xticks(range(0, 24*60, 60))
    axs[idx].set_xticklabels(['h%d' % (ii/ 60,) for ii in range(0, 24*60 , 60)])

plt.legend(loc='best')
plt.show()