Name: Electronic Card Transactions From 2017-2020
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Electronic Card Transactions From 2017-2020

Electronic Card Transactions from 2017-2020

Exploring Retail Spending Trends

By data.govt.nz [source]

About this dataset

This dataset contains valuable information about electronic card transactions from 2017-2020. The data is sourced from a trustable source and was last updated on 2020-08-10. It paints a comprehensive picture of electronic payments made during this period and includes important variables such as the series reference, period, data value, suppresed status, units magnitude, subject, group and five titles (series_title). This data is useful for exploring timely trends in electronic card spending activity over the 4 year period between 2017 to 2020 making it an invaluable asset for researchers looking to better understand purchasing habits in New Zealand. This dataset offers analysts unprecedented insight into consumer behavior allowing them to develop strategies aimed at increasing transaction volumes or customer loyalty initiatives

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How to use the dataset

This dataset contains information about electronic card transactions from 2017-2020. It includes data on the series reference, period, data value, suppressed status, units of measurement, magnitude, subject and group. Using this dataset can provide you with insightful information that can help you improve your business operations and make better decisions.

For example, you could use the data values to identify patterns and trends in sales over time. You could also use the period data to determine whether certain months have more sales than others. Finally, you could utilize the suppressed status to understand which transactions should or should not be included when making conclusions from your dataset.

In order to use this dataset effectively it would be helpful for you to be familiar with database software such as Microsoft Access or SQLite database manager which is free and open source software for managing a variety of databases. Once familiar with database software of your choice it is important to understand how different fields interact together in order create meaningful results from queries such as finding out what kind of expenditure is done during different times of year or discovering what kind of products contribute most revenue during certain periods etc..
It is also sensible for one become knowledgeable in some basic coding languages like Python - that have modules designed specifically for statistical analysis - so they are well equipped when it comes down crunching these kinds complex datasets into tangible insights!

Research Ideas

Visualizing changes in spending habits of consumers over time by analyzing the Data_value, UNITS and Magnitude columns.

Analyzing the correlation between areas with higher spending volumes and socioeconomic factors such as income level or population density by leveraging Subject and Group details.

Predicting future trends in electronic card transactions through machine learning algorithms that use historical data from the dataset to make predictions on changing patterns of consumer spending behaviors over time based on all columns in the dataset eg Series_title_1, STATUS etc.

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: electronic-card-transactions-feb-2018-csv-tables.csv

Column name	Description
Series_reference	Unique identifier for each series. (String)
Period	Date of the transaction. (Date)
Data_value	Value of the transaction. (Integer)
Suppressed	Boolean value indicating whether the data has been suppressed or not. (Boolean)
STATUS	Status of the transaction. (String)
UNITS	Units of the transaction. (String)
Magnitude	Magnitude of the transaction. (Integer)
Subject	Subject of the transaction. (String)
Group	Group of the transaction. (String)
Series_title_1	Title of the series. (String)
Series_title_2	Title of the series. (String)
Series_title_3	Title of the series. (String)
Series_title_4	Title of the series. (String)
Series_title_5	Title of the series. (String)

File: august-2019-ect-tables-69.csv

File: september-2018-ect-tables-3.csv

File: may-2019-ect-tables-28.csv

File: june-2018-ect-tables-16.csv

File: june-2019-ect-tables-14.csv

File: july-2019-ect-tables-17.csv

File: january-2019-ect-tables-45.csv

File: february-2018-ect-tables-7.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit data.govt.nz.

Tables

January 2020 Ect Tables 50

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.january_2020_ect_tables_50

7.64 KB
22 rows
10 columns


CREATE TABLE january_2020_ect_tables_50 (
  "index" BIGINT,
  "electronic_card_transactions_january_2020" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

July 2018 Ect Tables 18

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.july_2018_ect_tables_18

7.6 KB
21 rows
10 columns


CREATE TABLE july_2018_ect_tables_18 (
  "index" BIGINT,
  "electronic_card_transactions_july_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

July 2019 Ect Tables 17

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.july_2019_ect_tables_17

7.62 KB
22 rows
10 columns


CREATE TABLE july_2019_ect_tables_17 (
  "index" BIGINT,
  "electronic_card_transactions_july_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

July 2020 Ect Tables 68

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.july_2020_ect_tables_68

7.62 KB
22 rows
10 columns


CREATE TABLE july_2020_ect_tables_68 (
  "index" BIGINT,
  "electronic_card_transactions_july_2020" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

June 2018 Ect Tables 16

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.june_2018_ect_tables_16

7.67 KB
24 rows
10 columns


CREATE TABLE june_2018_ect_tables_16 (
  "index" BIGINT,
  "electronic_card_transactions_june_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

June 2019 Ect Tables 14

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.june_2019_ect_tables_14

7.69 KB
25 rows
10 columns


CREATE TABLE june_2019_ect_tables_14 (
  "index" BIGINT,
  "electronic_card_transactions_june_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

June 2020 Ect Tables 23

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.june_2020_ect_tables_23

7.69 KB
25 rows
10 columns


CREATE TABLE june_2020_ect_tables_23 (
  "index" BIGINT,
  "electronic_card_transactions_june_2020" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

March 2018 Ect Tables 59

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.march_2018_ect_tables_59

7.67 KB
24 rows
10 columns


CREATE TABLE march_2018_ect_tables_59 (
  "index" BIGINT,
  "electronic_card_transactions_march_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

March 2020 Ect Tables 46

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.march_2020_ect_tables_46

7.77 KB
25 rows
10 columns


CREATE TABLE march_2020_ect_tables_46 (
  "index" BIGINT,
  "electronic_card_transactions_march_2020" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

March Ect Tables 55

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.march_ect_tables_55

7.69 KB
25 rows
10 columns


CREATE TABLE march_ect_tables_55 (
  "index" BIGINT,
  "electronic_card_transactions_march_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

May 2018 Ect Tables 30

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.may_2018_ect_tables_30

7.59 KB
21 rows
10 columns


CREATE TABLE may_2018_ect_tables_30 (
  "index" BIGINT,
  "electronic_card_transactions_may_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

May 2019 Ect Tables 28

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.may_2019_ect_tables_28

7.6 KB
22 rows
10 columns


CREATE TABLE may_2019_ect_tables_28 (
  "index" BIGINT,
  "electronic_card_transactions_may_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

May 2020 Ect Tables 40

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.may_2020_ect_tables_40

7.6 KB
22 rows
10 columns


CREATE TABLE may_2020_ect_tables_40 (
  "index" BIGINT,
  "electronic_card_transactions_may_2020" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

November 2017 Ect Tables 44

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.november_2017_ect_tables_44

7.65 KB
22 rows
10 columns


CREATE TABLE november_2017_ect_tables_44 (
  "index" BIGINT,
  "electronic_card_transactions_november_2017" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

November 2018 Ect Tables 10

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.november_2018_ect_tables_10

7.66 KB
22 rows
10 columns


CREATE TABLE november_2018_ect_tables_10 (
  "index" BIGINT,
  "electronic_card_transactions_november_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

November 2019 Ect Tables 12

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.november_2019_ect_tables_12

7.66 KB
22 rows
10 columns


CREATE TABLE november_2019_ect_tables_12 (
  "index" BIGINT,
  "electronic_card_transactions_november_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

October 2017 Ect Tables 11

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.october_2017_ect_tables_11

7.62 KB
21 rows
10 columns


CREATE TABLE october_2017_ect_tables_11 (
  "index" BIGINT,
  "electronic_card_transactions_october_2017" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

October 2018 Ect Tables 27

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.october_2018_ect_tables_27

7.64 KB
22 rows
10 columns


CREATE TABLE october_2018_ect_tables_27 (
  "index" BIGINT,
  "electronic_card_transactions_october_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

October 2019 Ect Tables 33

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.october_2019_ect_tables_33

7.63 KB
22 rows
10 columns


CREATE TABLE october_2019_ect_tables_33 (
  "index" BIGINT,
  "electronic_card_transactions_october_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

September 2017 Ect Tables 49

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.september_2017_ect_tables_49

7.71 KB
24 rows
10 columns


CREATE TABLE september_2017_ect_tables_49 (
  "index" BIGINT,
  "electronic_card_transactions_september_2017" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

September 2018 Ect Tables 3

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.september_2018_ect_tables_3

7.73 KB
25 rows
10 columns


CREATE TABLE september_2018_ect_tables_3 (
  "index" BIGINT,
  "electronic_card_transactions_september_2018" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);

September 2019 Quarter Ect Tables 4

@kaggle.thedevastator_uncovering_insights_from_electronic_card_transac.september_2019_quarter_ect_tables_4

7.6 KB
23 rows
10 columns


CREATE TABLE september_2019_quarter_ect_tables_4 (
  "index" BIGINT,
  "electronic_card_transactions_september_2019" VARCHAR,
  "unnamed_1" VARCHAR,
  "unnamed_2" VARCHAR,
  "unnamed_3" VARCHAR,
  "unnamed_4" VARCHAR,
  "unnamed_5" VARCHAR,
  "unnamed_6" VARCHAR,
  "unnamed_7" VARCHAR,
  "unnamed_8" VARCHAR
);