[Python] Combine separate plots into one plot in Python

I have created the following pandas dataframe:

ds = {
'Date' : ['2025-08-22 16:00:00', '2025-08-22 16:01:00', '2025-08-22 16:02:00', '2025-08-22 16:03:00', '2025-08-22 16:04:00', '2025-08-22 16:05:00', '2025-08-22 16:06:00', '2025-08-22 16:07:00', '2025-08-22 16:08:00', '2025-08-22 16:09:00', '2025-08-22 16:10:00', '2025-08-22 16:11:00', '2025-08-22 16:12:00', '2025-08-22 16:13:00', '2025-08-22 16:14:00', '2025-08-22 16:15:00', '2025-08-22 16:16:00', '2025-08-22 16:17:00', '2025-08-22 16:18:00', '2025-08-22 16:19:00', '2025-08-22 16:20:00', '2025-08-22 16:21:00', '2025-08-22 16:22:00', '2025-08-22 16:23:00', '2025-08-22 16:24:00'],
'Open': [ 11717.9, 11717.95, 11716.6, 11717.4, 11719.5, 11727.25, 11725.55, 11724.35, 11725.45, 11724.15, 11728.2, 11726.6, 11727.6, 11729.1, 11724.1, 11722.8, 11721.8, 11720.8, 11718.8, 11716.7, 11716.9, 11722.5, 11721.6, 11727.8, 11728.1],
'Low': [ 11715.9, 11716, 11715.35, 11716.45, 11719.5, 11724.3, 11723.55, 11723.15, 11723.85, 11724.15, 11725.2, 11726.6, 11727.6, 11724.2, 11722.6, 11721.6, 11719.7, 11715.8, 11716.5, 11716, 11716.9, 11721.3, 11721.4, 11726.35, 11727],
'High': [ 11718.1, 11718.1, 11717.9, 11719.4, 11727.15, 11727.45, 11726, 11725.65, 11727.2, 11727.85, 11728.2, 11728.7, 11729.5, 11729.1, 11725.5, 11723.9, 11722, 11720.8, 11719.8, 11717.7, 11722.9, 11724.3, 11727.8, 11728.3, 11728.8],
'Close' : [11718.05, 11716.5, 11717, 11719.3, 11727.15, 11725.65, 11724.15, 11725.35, 11724.05, 11727.65, 11726.7, 11727.8, 11729.2, 11724.2, 11722.6, 11721.7, 11721.2, 11718.7, 11716.6, 11716.8, 11722.6, 11721.5, 11727.6, 11728, 11727.2],
'Volume': [ 130, 88, 125, 93, 154, 102, 118, 92, 105, 116, 84, 88, 108, 99, 82, 109, 98, 130, 71, 86, 96, 83, 80, 93, 73],
'Regime': [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3],
}

df = pd.DataFrame(data=ds)


The dataframe contains a field called Regime, which has three values:1,2 and 3.

I have created a volume profile plot for each of those Regimes after grouping the records by 5 minutes.

df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

# Resample to 5-minute intervals
df_30 = df.resample('5T').agg({'Open': 'first', 'High': 'max', 'Low': 'min', 'Close': 'last', 'Volume': 'sum','Regime':'last'})
df_30 = df_30.dropna()

# Add date column for grouping
df_30['date'] = df_30.index.date


# For volume profile, use the original 1min df
df['date'] = df.index.date

# Group by regime
for regime in df['Regime'].unique():
daily_30 = df_30[df_30['Regime'] == regime]
daily_1 = df[df['Regime'] == regime]

if daily_30.empty:
continue

# For candlestick plotting
daily_30 = daily_30.reset_index()
xdates = np.arange(len(daily_30))

# For volume profile
min_price = daily_1['Low'].min()
max_price = daily_1['High'].max()
num_bins = 200
bins = np.linspace(min_price, max_price, num_bins + 1)
bin_size = bins[1] - bins[0]
bin_centers = (bins[:-1] + bins[1:]) / 2
volume_profile = np.zeros(num_bins)

display(volume_profile)

for _, row in daily_1.iterrows():
low = row['Low']
high = row['High']
vol = row['Volume']
if high == low:
bin_idx = np.digitize(low, bins) - 1
if 0 <= bin_idx < num_bins:
volume_profile[bin_idx] += vol
else:
vol_per_unit = vol / (high - low)
start_bin = np.digitize(low, bins)
end_bin = np.digitize(high, bins)
for b in range(start_bin, end_bin + 1):
if b > 0 and b <= num_bins:
bin_start = bins[b - 1]
bin_end = bins
start = max(low, bin_start)
end = min(high, bin_end)
portion = (end - start) * vol_per_unit
volume_profile[b - 1] += portion

# Normalize for plotting (scale to chart width)
chart_width = len(daily_30)
if max(volume_profile) > 0:
scaled_volume = (volume_profile / max(volume_profile)) * chart_width
else:
scaled_volume = volume_profile

display(scaled_volume)
# POC (Point of Control)
poc_idx = np.argmax(volume_profile)
poc_price = bin_centers[poc_idx]

# print(poc_price)
# Plot
fig, ax = plt.subplots(figsize=(10, 6))

# Plot volume profile first (as background)
ax.fill_betweenx(bin_centers, 0, scaled_volume, color='blue', alpha=0.3, step='mid')

# Plot POC
ax.axhline(poc_price, color='red', linestyle='-', linewidth=1)

# Plot candlesticks on top
candle_width = 0.6
for i in range(len(daily_30)):
o = daily_30['Open']
h = daily_30['High']
l = daily_30['Low']
c = daily_30['Close']
if c > o:
color = 'green'
bottom = o
height = c - o
else:
color = 'red'
bottom = c
height = o - c
# Wick
ax.vlines(xdates, l, h, color='black', linewidth=0.5)
# Body
ax.bar(xdates, height, candle_width, bottom, color=color, edgecolor='black')

ax.set_xlim(-1, chart_width + 1)
ax.set_ylim(min_price - bin_size, max_price + bin_size)
ax.set_xticks(xdates)
ax.set_xticklabels(daily_30['Date'].dt.strftime('%H:%M'), rotation=45)
ax.set_title(f'30-min Candlestick with Volume Profile - Regime: {regime}')
ax.set_xlabel('Time')
ax.set_ylabel('Price')
plt.tight_layout()
plt.show()


The code creates three separate volume profile plots.



I need to combine the three plots into one single plot, such that on the x-axis Plot2 follows Plot 1, and Plot 3 follows Plot 2. Basically, I'd like to see a graph similar (but of course not exactly the same) to this.



Does someone know how to do it?

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