.“`python
import numpy as np
import pandas as pd

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dates = pd.date_range(‘2020-01-01’, ‘2020-12-31′, freq=’M’)
values = np.random.randint(100, size=len(dates))
df = pd.DataFrame(‘Date’: dates, ‘Value’: values)

Perform a seasonal decomposition of time series on the ‘Value’ column

decomposition = seasonal_decompose(df[‘Value’], model=’additive’)

Plot the trend, seasonality, and residuals

decomposition.trend.plot()
decomposition.seasonal.plot()
decomposition.resid.plot()
plt.show()

1. **Data Preparation**:
   - It begins by importing NumPy (`import numpy as np`) and Pandas (`import pandas as pd`).
   - It creates a DataFrame (`df`) with two columns: 'Date' containing monthly dates from '2020-01-01' to '2020-12-31' and 'Value' containing randomly generated values.

2. **Seasonal Decomposition**:
   - It uses the `seasonal_decompose` function from statsmodels to perform a seasonal decomposition on the 'Value' column of `df`. This decomposition separates the time series into three components: trend, seasonality, and residuals.

3. **Plotting the Results**:
   - It plots three separate graphs:
     - Trend: This graph shows the long-term trend in the data, removing the seasonal and residual components.
     - Seasonality: This graph depicts the seasonal pattern, showing how the data fluctuates over the course of a year.
     - Residuals: This graph displays the remaining variation not explained by the trend or seasonality.

4. **Displaying the Plots**:
   - Finally, the code uses Matplotlib's `plt.show()` function to display these three plots.

By analyzing these plots, you can identify patterns and trends in the time series, which is valuable for forecasting, understanding data variability, and capturing seasonal effects.

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