python pandas dataframe matplotlib combinedchart

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[b]
                    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'][i]
        h = daily_30['High'][i]
        l = daily_30['Low'][i]
        c = daily_30['Close'][i]
        if c > o:
            color = 'green'
            bottom = o
            height = c - o
        else:
            color = 'red'
            bottom = c
            height = o - c
        # Wick
        ax.vlines(xdates[i], l, h, color='black', linewidth=0.5)
        # Body
        ax.bar(xdates[i], 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?

Solution

Here is the closest I have been able to get to your expected output:

To accomplish this, we need to do a few changes:

Use a master .subplots instead of one .subplots for one regime, and use with gridspec (for no spacing between charts).

fig = plt.figure(figsize=(30, 6)) # (30, 6) is just what I think is a good size, feel free to change this.
gs = fig.add_gridspec(nrows=1, ncols=3, hspace=0, wspace=0) # hspace and wspace removes all space from between two regimes

axes = gs.subplots(sharey=True) # share the y axis

and change your for loop to also get the index of regime so we can use that for our axes list:

for index, regime in enumerate(df['Regime'].unique()):

and

axes[index]. # use instead of ax.

Instead of every chart having a long title of 30-min Candlestick with Volume Profile - Regime: {regime} three times, we can make the common part (30-min Candlestick with Volume Profile) the master title, and the regimes the minor titles:

fig.suptitle("30-min Candlestick with Volume Profile") # Assign master title

and:

axes[index].set_title(f'Regime: {regime}')

Since we don't want labels appearing for every chart, we can add a loop to the end which only keeps the needed labels (for shared axes):
```
for ax in axes:
    ax.label_outer()
```
Instead of calling plt.show() for every regime, call it at the end.

Final Code:


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

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)

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

fig = plt.figure(figsize=(30, 6))
gs = fig.add_gridspec(nrows=1, ncols=3, hspace=0, wspace=0)

axes = gs.subplots(sharey=True)
fig.suptitle("30-min Candlestick with Volume Profile")
# Group by regime
for index, regime in enumerate(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[b]
                    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

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

    # Plot POC
    axes[index].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'][i]
        h = daily_30['High'][i]
        l = daily_30['Low'][i]
        c = daily_30['Close'][i]
        if c > o:
            color = 'green'
            bottom = o
            height = c - o
        else:
            color = 'red'
            bottom = c
            height = o - c
        # Wick
        axes[index].vlines(xdates[i], l, h, color='black', linewidth=0.5)
        # Body
        axes[index].bar(xdates[i], height, candle_width, bottom, color=color, edgecolor='black')

    axes[index].set_xlim(-1, chart_width + 1)
    axes[index].set_ylim(min_price - bin_size, max_price + bin_size)
    axes[index].set_xticks(xdates)
    axes[index].set_xticklabels(daily_30['Date'].dt.strftime('%H:%M'), rotation=45)
    axes[index].set_title(f'Regime: {regime}')
    axes[index].set_xlabel('Time')
    axes[index].set_ylabel('Price')

for ax in axes:
    ax.label_outer()

plt.tight_layout()
plt.show()

If you want some modifications, I will be happy to help you with that!