How to Plot Multiple Gamma Distributions in Python with Matplotlib – A Step‑by‑Step Guide

1. Introduction

Hello everyone, I am PiPi. A few days ago a member asked a Python visualization question in a group chat, showing the following problem:

The original code was:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st

result_parameter_peak = pd.read_csv("result_parameter_peak.csv", encoding="utf_8_sig")

# Set canvas
fig = plt.figure(figsize=(20, 8))
axl = fig.add_subplot(1, 1, 1)

for i in range(len(result_parameter_peak)):
    x = np.arange(0, 400, 1)
    # Draw gamma curve
    y661 = st.gamma.pdf(x, result_parameter_peak.iloc[i, 1], scale=result_parameter_peak.iloc[i, 2])
    axl.plot(x, y661, 'r-.', label="α= 9.9028，β=10.4205")
    axl.set_xlabel('Time')
    axl.set_ylabel('Probility')
    axl.set_title('分布')
    plt.show()

Running this produced only a single plot:

2. Solution

A contributor provided a new approach and sample code that iterates over the CSV rows using itertuples() and plots each gamma curve on the same figure:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st

result_parameter_peak = pd.read_csv("result_parameter_peak.csv", encoding="utf_8_sig")

plt.figure()
for i, alpha, beta in result_parameter_peak.itertuples():
    x = np.arange(0, 300, 1)
    y661 = st.gamma.pdf(x, alpha, scale=beta)
    plt.plot(x, y661, '-.')
    plt.xlabel('Time')
    plt.ylabel('Probility')
    plt.title('分布')
plt.show()

The resulting plot correctly displayed multiple gamma curves:

Another contributor added legends and labels to make the figure more informative:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st

result_parameter_peak = pd.read_csv("result_parameter_peak.csv", encoding="utf_8_sig")

plt.figure()
for i, alpha, beta in result_parameter_peak.itertuples():
    x = np.arange(0, 300, 1)
    y661 = st.gamma.pdf(x, alpha, scale=beta)
    plt.plot(x, y661, '-.', label="α:" + str(alpha) + "β:" + str(beta))
    plt.xlabel('Time')
    plt.ylabel('Probility')
    plt.title('fenbu')
plt.legend()
plt.show()

The final plot with annotations looks like this:

3. Summary

This article examined a common matplotlib plotting problem, provided detailed analysis and three complete code examples, and demonstrated how to generate and annotate multiple gamma distribution curves, enabling readers to solve the issue effectively.