Crop Yield Volatility & Correlation
Treat the Kaggle yield panel as a time-series problem — measure year-over-year volatility per crop and cross-crop correlation structure.
2
Crop Yield Volatility & Correlation
Python / R • Step 2
Treat the Kaggle yield panel as a time-series problem — measure year-over-year volatility per crop and cross-crop correlation structure.
time_seriesvolatilitycorrelation
Learning Objectives
- →Pivot a long-format panel to a wide crop × year matrix
- →Compute year-over-year volatility per crop
- →Quantify cross-crop return correlation"
Dataset
View on Kaggle Matches Kaggle
FAO yield joined with rainfall, pesticides, and temperature per country/crop/year.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/yield_df.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named yield_df.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "yield_df.csv").
Dataset Columns
AreaItemYearhg/ha_yieldaverage_rain_fall_mm_per_yearpesticides_tonnesavg_tempCode Language
- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
Shape: Focus crops: Mean YoY yield volatility (%):
0/2
Export to GitHub
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