Electronic Card Transactions: A Decade of Growth and Seasonal Swings

By abhishek.verma75000 · June 6, 2026

Exploring 25 years of electronic card transaction data revealed strong seasonality, significant category differences, notable growth trends, and…

The analysis compared average Data value between 'Actual' and 'Seasonally Adjusted' measures in the dataset. A bar chart was generated showing the average values side by side, along with supporting data tables for a detailed breakdown of each measure type.

A data table has been generated summarizing total electronic card transaction values across the available periods from 2000 to 2025. The table provides a breakdown of transaction values that you can explore to identify trends over time.

The analysis reveals which Group categories have the highest average Data value, indicating the top spending sectors. A horizontal bar chart and supporting tables were generated to visualize these results. The clear leader is 'Number of electronic card transactions A/S/T by division' with a massive average of 173,868,182.9 — far exceeding all other categories.

The analysis generated two data tables comparing Series title 2 categories across their earliest and latest periods. These tables show the growth patterns across different categories in the dataset, allowing you to identify which categories have expanded or contracted over time.

Yes, there are clear recurring monthly patterns in Data value. The analysis reveals strong seasonality with a 90.3% variation across months, indicating significant differences depending on the time of year. Two bar and line charts were generated showing these patterns visually, along with supporting data tables.

The analysis examined period-over-period changes in total Data value across all Groups to identify unusual spikes or drops. Two visualizations were generated: a trend line chart highlighting anomalous periods with green triangle-up markers for spikes and red triangle-down markers for drops, and a bar chart showing the percentage change between consecutive periods — with green bars for spikes and red bars for drops. These charts make it easy to visually spot which periods deviate significantly from the norm (beyond 2 standard deviations of the average change).

The analysis successfully ranked all Series title 2 categories by both their average and total cumulative Data value. A side-by-side horizontal bar chart has been generated showing both rankings simultaneously, making it easy to visually compare how categories shift between the two measures. The top categories by total volume are 'Total', 'RTS total industries', and 'RTS core industries', while the top categories by average transaction size are 'Total', 'Debit', and 'RTS total industries'. Notably, 'Debit' ranks highly on average transaction size but does not appear in the top 3 by total volume, suggesting it has large individual transactions but fewer of them. Conversely, 'RTS core industries' ranks in the top 3 by total but not by average, indicating high frequency/volume with moderate average values.

The analysis extracted years from the Period column and computed year-over-year percentage changes in total Data value for each Series title 2 category. A heatmap was generated showing boom (green) and bust (red) periods across all transaction segments and years, making it easy to visually identify growth and decline patterns at a glance.

A comprehensive statistical breakdown of the Data value column has been generated across all Series title 1 categories. The analysis produced a grouped bar chart comparing key statistics (Mean, Median, Standard Deviation, and IQR) side by side for each series type, as well as a box plot showing the spread, central tendency, and skewness of transaction values within each category. Data tables with the full summary statistics are also available for detailed review.

The analysis of Data value across 20,129 records reveals a highly right-skewed distribution. Two visualizations were generated: a histogram with KDE overlay annotated with mean and median markers, and a box plot segmenting values by Magnitude. The overall distribution spans from -51 to nearly 2 billion, with a mean of 16,500,505 but a median of only 1,294 — a massive gap indicating most values are small while a few extremely large values pull the average up dramatically.