There are many examples when we have to look up data in an Excel table.

Typically, it’s done by a family of **LOOKUP** functions. I’m not a fan of them, because they are completely not transparent — unless you see the table against which the **LOOKUP** function is applied, you can’t understand what exactly it does.

An alternative is a combination of **INDEX** and **MATCH** functions, which I call *an INDEX–MATCH approach*. The easiest and simplest case is when we have to find the value of a column in a row where another column has a specific value:

=INDEX(Data[

resulting column], MATCH(value to find, Data[filtering column], 0))

Note how readable this formula is.

Another alternative is to use the **SUMIFS** function. There are two drawbacks of this approach:

- If, due to any error, there are several rows where the filtering column has the value we’re searching for, the function will return the sum of the resulting column over these rows, while the
**INDEX**—**MATCH**approach will return the value in the first such row - When there are no such rows, the
**SUMIFS**function will return zero, while the**INDEX**—**MATCH**approach will result in an error. If the resulting column does not contain zeroes, this is not a problem, but otherwise, you’ll be unable to determine whether zero is a legitimate sum or no rows were found

When there are several filtering columns, the **SUMIFS** function can be used straightforwardly, as it was built to be scalable. That’s not the case with the **INDEX–MATCH** approach, however, as the **MATCH** function can search only in one column. In database parlance, the **MATCH **function can work only with *simple keys* but we want it to make it work also with *compound keys.*

However, nothing precludes us from generating a new column in the data that uniquely identifies rows for our purposes. I typically call this column **UID** for *unique identifier*. Let’s take a look at an example:

Product |
Month |
Sales |

A | Jan | 49752 |

A | Feb | 50259 |

A | Mar | 49573 |

B | Jan | 54834 |

B | Feb | 50356 |

B | Mar | 52300 |

C | Jan | 54579 |

C | Feb | 54361 |

C | Mar | 53924 |

D | Jan | 51562 |

D | Feb | 47391 |

D | Mar | 54883 |

Here we have monthly sales of products in a table called **Data**. We want to reshape it into the following table:

Sales of \ in |
Jan |
Feb |
Mar |

A |
|||

B |
|||

C |
|||

D |

Because we’ll look up values in the **Data** table by product and month, we generate the **UID** column in the **Data** table to uniquely identify each row using the following formula:

=[@Product] & “-” & [@Month]

Here’s what you get as a result:

Then in the results table, we just have to apply the **INDEX–MATCH** approach. To create a nicer formula, first name the top row of the results table containing month names as **Months** and the first column containing product names as **Products**. Then the formula that should fill the empty cells in the results table is:

=INDEX(Data[Sales], MATCH(Products & “-” & Months, Data[UID], 0))

Note that in this example, the value the **MATCH** function will try to find is determined by an *implicit intersection* of two ranges (**Products** and **Months**).

Here is the results table:

## 3 thoughts on “Looking Up Data by Several Columns”