EDA Tutorial: Analyzing ZIM Stock Data from Yahoo Finance

In this tutorial, we’ll perform exploratory data analysis (EDA) using real-time stock data from Yahoo Finance. Our target is ZIM Integrated Shipping Services Ltd. (Ticker: ZIM).

We’ll use the Python yfinance package to pull data directly, making the process repeatable and easy to update.

Table of Contents

1. Install yfinance

First, install the yfinance package if you haven’t already:

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pip install yfinance

2. Load ZIM Stock Data from Yahoo Finance

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import yfinance as yf
import pandas as pd

# Download last 2 years of ZIM stock data
df = yf.download("ZIM", start="2023-01-01", end="2025-01-01")
df = df.reset_index()
df.head()

✅ Sample Columns:

DateOpenHighLowCloseAdj CloseVolume

3. Basic Info and Cleaning

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df.info()
df.describe()
df.isnull().sum()

Make sure the Date column is in datetime format (usually it is by default from yfinance):

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df['Date'] = pd.to_datetime(df['Date'])

4. Visualize Closing Price

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import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(12,6))
sns.lineplot(x='Date', y='Close', data=df)
plt.title("ZIM Closing Price Over Time")
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.show()

5. Volume Trend

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sns.lineplot(x='Date', y='Volume', data=df)
plt.title("ZIM Trading Volume Over Time")
plt.show()

This helps identify high-activity periods which might relate to earnings, dividends, or news events.

6. Monthly Average Price

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df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month

monthly_avg = df.groupby(['Year', 'Month'])['Close'].mean().reset_index()
monthly_avg['Date'] = pd.to_datetime(monthly_avg['Year'].astype(str) + '-' + monthly_avg['Month'].astype(str))

sns.lineplot(x='Date', y='Close', data=monthly_avg)
plt.title("Monthly Average Closing Price of ZIM")
plt.show()

7. Correlation Heatmap

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sns.heatmap(df[['Open', 'High', 'Low', 'Close', 'Volume']].corr(), annot=True, cmap='coolwarm')
plt.title("Correlation Between Stock Features")
plt.show()

8. Summary Insights

  • ZIM stock shows high volatility with steep peaks and drops.
  • Trading volume often spikes alongside price shifts.
  • Price-related columns (Open, High, Low, Close) show strong correlations, which matters when building predictive models.

Conclusion

In this tutorial, we explored ZIM stock data from Yahoo Finance using yfinance and performed a basic EDA using Pandas, Seaborn, and Matplotlib.

The same approach works for any other stock — just change the ticker symbol in yf.download("TICKER").

Stay tuned for upcoming EDA tutorials using real estate and e-commerce log data!

  • “ZIM Stock EDA: Pulling Data from Yahoo Finance with Python”
  • “How to Analyze Stock Data in Python (ZIM Case Study)”
  • “From Yahoo to Insight: ZIM Stock EDA with yfinance”