How to Use Exploratory Data Analysis to Understand Seasonality in Data

Seasonality refers to patterns that repeat over a known, fixed period such as daily, weekly, monthly, or yearly cycles. Detecting and understanding seasonality in data is crucial for businesses and analysts, as it allows for better forecasting, strategy development, and decision-making. Exploratory Data Analysis (EDA) is a set of techniques used to summarize the main characteristics of a dataset, often with visual methods. Through EDA, you can uncover trends, patterns, anomalies, and most importantly, seasonal behaviors.

Understanding Seasonality Through EDA

1. Loading and Understanding the Dataset

The first step in EDA is loading the dataset and understanding its structure. You should examine the columns, data types, and any missing or anomalous values. For time series data, it’s essential to ensure that the time column is in the correct datetime format and set as the index if using tools like Pandas in Python.

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import pandas as pd

data = pd.read_csv('your_dataset.csv', parse_dates=['date'])
data.set_index('date', inplace=True)
print(data.info())
print(data.head())

This step helps confirm that the data is ready for time-based operations.

2. Descriptive Statistics

Generate summary statistics for each variable, especially the one you’re trying to analyze seasonality in. Use methods like .describe() to check for range, mean, median, standard deviation, and potential outliers.

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print(data['sales'].describe())

This provides a preliminary idea about the variability and scale of the data.

3. Visualizing the Time Series

Plotting the time series is the most direct way to visually assess seasonality. Line plots are particularly useful for observing periodic fluctuations.

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import matplotlib.pyplot as plt

data['sales'].plot(figsize=(15, 5), title='Sales Over Time')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.show()

Seasonality appears as repeating patterns over fixed periods. For example, retail sales might peak every December.

4. Resampling and Aggregation

Resample the data to different time granularities to expose seasonal patterns. For example, if you have daily data, resample it to monthly or weekly averages.

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monthly_data = data['sales'].resample('M').mean()
monthly_data.plot(title='Monthly Average Sales')
plt.show()

Aggregation helps smooth out short-term noise and highlights long-term patterns, making seasonal trends clearer.

5. Rolling Statistics

Applying moving averages can help in identifying seasonality by smoothing the time series. A common practice is to use rolling means and standard deviations.

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rolling_mean = data['sales'].rolling(window=12).mean()
rolling_std = data['sales'].rolling(window=12).std()

plt.figure(figsize=(15,5))
plt.plot(data['sales'], label='Original')
plt.plot(rolling_mean, label='Rolling Mean')
plt.plot(rolling_std, label='Rolling Std')
plt.legend()
plt.title('Rolling Statistics')
plt.show()

Rolling metrics help in identifying trends and seasonality by removing random fluctuations.

6. Decomposition of Time Series

Time series decomposition separates a series into trend, seasonality, and residuals. It’s one of the most effective ways to explicitly see the seasonal component.

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from statsmodels.tsa.seasonal import seasonal_decompose

result = seasonal_decompose(data['sales'], model='additive', period=12)
result.plot()
plt.show()

This decomposition helps understand how much of the variability in data is due to seasonality versus trends or random noise.

7. Box Plots by Time Period

Box plots grouped by time components (like month or day of the week) help analyze seasonal effects.

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data['month'] = data.index.month
data['year'] = data.index.year

import seaborn as sns
sns.boxplot(x='month', y='sales', data=data)
plt.title('Monthly Sales Distribution')
plt.show()

Box plots reveal variability and distribution of sales over months, making it easy to spot recurring patterns.

8. Autocorrelation and Partial Autocorrelation

Autocorrelation plots show how data points are related to past values. A strong correlation at seasonal lags indicates seasonality.

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from pandas.plotting import autocorrelation_plot

autocorrelation_plot(data['sales'])
plt.show()

Alternatively, use the ACF and PACF plots:

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from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

plot_acf(data['sales'], lags=50)
plot_pacf(data['sales'], lags=50)
plt.show()

Significant spikes at regular intervals in ACF plots often signal seasonal cycles.

9. Heatmaps for Temporal Patterns

Heatmaps are effective for detecting seasonality in data across two time dimensions (e.g., month vs. year).

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pivot = data.pivot_table(index=data.index.month, columns=data.index.year, values='sales')
sns.heatmap(pivot, cmap='YlGnBu')
plt.title('Sales Seasonality Heatmap')
plt.xlabel('Year')
plt.ylabel('Month')
plt.show()

This makes recurring high or low values across the same months or periods in different years visually obvious.

10. Seasonal Subseries Plots

A seasonal subseries plot shows the data for each season separately and helps identify whether seasonality is consistent over time.

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import matplotlib.dates as mdates

fig, ax = plt.subplots(figsize=(12,8))
for month in range(1, 13):
    monthly_data = data[data.index.month == month]
    ax.plot(monthly_data.index.year, monthly_data['sales'], label=f'Month {month}')

ax.legend()
plt.title('Seasonal Subseries Plot by Month')
plt.xlabel('Year')
plt.ylabel('Sales')
plt.show()

This reveals whether certain months are consistently higher or lower across years.

Conclusion

Exploratory Data Analysis offers a rich toolkit to discover seasonality in time series data. By visualizing trends, decomposing data, and leveraging statistical tools like autocorrelation and box plots, you can clearly identify and quantify seasonal patterns. This understanding is crucial not just for descriptive analytics but also for predictive modeling, such as building ARIMA or SARIMA models for forecasting.

Employing EDA before diving into complex models ensures that the insights are grounded in the actual behavior of the data, improving the reliability of forecasts and business strategies alike.