How to Plot a Histogram in Python | Plotting Distribution of Returns (in under 2.5 minutes)
Visualizing stock returns has never been easier! In this quick and practical tutorial, you’ll learn how to plot the distribution of financial returns using Python with the help of Pandas and Matplotlib. We’ll guide you step-by-step through customizing your histogram to make your analysis clear, concise, and visually appealing.
What You’ll Learn in This Video:
Calculating daily returns using Pandas.
Plotting a histogram to visualize the distribution of returns.
Customizing the plot with titles, labels, and style for better readability.
Quick insights into return distribution for financial analysis.
🎯 Why This Tutorial? Understanding the distribution of returns is critical for portfolio analysis and risk management. This tutorial offers a fast and practical approach, perfect for financial analysts, data scientists, and Python enthusiasts.
📊 Who Is This For? Ideal for finance professionals, data science beginners, and anyone looking to combine Python programming with financial analysis.
🔗 Topics Covered:
Introduction to Pandas and Finance
Calculating financial returns
Customizing Matplotlib plots
Practical use of histograms in finance
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Code from Tutorial:
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import yfinance as yf
import matplotlib.pyplot as plt
data = yf.download('AAPL')['Adj Close']
a = data.pct_change().dropna()
plt.hist(a, bins = 100, edgecolor = 'black')
plt.xlim(-0.3, 0.3)
Add labels & title
plt.title('AAPL Daily Returns Distribution', fontsize=16, fontweight='bold')
plt.xlabel('Daily Returns (%)', fontsize=14)
plt.ylabel('Frequency', fontsize=14)
Make ticks visibile
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
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