One-Dimensional Table and Two-Dimensional Table

One-Dimensional Table

One-dimensional tables, commonly known as flat tables, typically are the most basic kind of tables, for example, the sales ledger table of a store. Such a table usually has fixed column names. When inputting data, you just need to add data row by row.

For example, the following table contains three field columns, each one of which has the same field type. Data is listed vertically in the table. After several different data records are added to the Product Name and Sales Volume columns, only the number of rows (rather than the number of columns) is added in the table.

Two-Dimensional Table

Two-dimensional table are relational tables where values in the data area are usually determined by rows and columns jointly.

For example, the values specified by the sole indicator Sales Volume are placed in multiple columns of the following table. Values of Sales Volume need to be determined by rows and columns jointly. Data is listed horizontally in the table. After several data records are added to the Product Name and Sales Volume columns, only the number of columns (rather than the number of rows) is added in the table.

In a one-dimensional table, the field name (column name) perfectly matches its corresponding data type. Such a table is more detailed and standardized. You can see that the sales volume data are placed in the Sales Volume column, not in other different columns. However, in a two-dimensional table, you can not directly specify what a certain value in the table represents, whether it represents sales volume, quantity, or profit. Without additional clarification added to the table externally, you can not know what the value represents. Such a table is still acceptable as a data source. Based on the content in the following two-dimensional table, if you expand the table by adding the Sales Quantity information, the sales quantity data will be added to the right side of that table, as shown in the following figure.

However, you are not advised to add more information items such as stores, profit, profit margin, and discounts to the table. In actual scenarios, adding more data records to a two-dimensional table will result in an ever-expanding number of field columns, which makes analysis less convenient.

A one-dimensional table is suitable for summarizing data by group and making charts. The field name perfectly matches its corresponding data type or value in such a table, so you can obtain the data you want once you find the corresponding field. In contrast, this requirement can not be achieved in a two-dimensional table. A one-dimensional table lists data originally. In other words, if you want to display data in the way that the data is naturally generated, you should use the one-dimensional table.

A two-dimensional table is not suitable for direct analysis. It is more appropriate for final report display after data classification and summarization.

Converting a one-dimensional table to a two-dimensional table is actually the process of data aggregating, interpreting, and understanding, namely, data pivoting. In Excel, you can create a pivot table to achieve the process, while in FineBI, you can use the Row to Column function to achieve the same effect with just one click.

For details, see Row-to-Column Conversion.

The process of converting a two-dimensional table to a one-dimensional table is the inverse of data pivoting. In Excel, this process is difficult to achieve without installing and using the Power Query (PQ) plugin. However, in FineBI, you can achieve the process with the Column to Row function.

For details, see Changing Columns to Rows.

Q: Why do I get a table with many blank cells instead of a standard two-dimensional table after performing the Row to Column operation on some fields in a detail table?

A: For the remaining fields that are not converted, if values in each row are different, those values cannot be aggregated into one row after conversion. Consequently, the converted values will be scattered, as shown in the following figure.

Q: Why does Row Field Converted specify column names during Row to Column operation?

A: When using the Row to Column function in FineBI, you need to drag fields as needed into Row Field Converted and Value of New Column. The values of each row specified by the dragged fields (namely column names) are converted to new fields. In this case, those identical values are grouped into the same column, causing the result taht the rows are reduced and columns added.

Q: What will happen if a chart is made by using a two-dimensional table directly (without row-column conversion) ?

A: To make a chart in FineBI, you need to drag the dimension and indicator fields into Horizontal Axis and Vertical Axis for the chart display. For example, the following line chart illustrates the sales volume trend based on a date dimension. In the chart, the horizontal axis represents the date, and the vertical axis represents the sales volume, with each date corresponding to a specific sales volume.

However, in a two-dimensional table, the values of each date will be used as field names. In that case, the sales revenues corresponding to different dates can not be displayed in a chart.