# COUNTDISTINCTIF Function

Generates the count of distinct non-null values for rows in each group that meet a specific condition.

**Nota**

When added to a transformation, this function is applied to the current sample. If you change your sample or run the job, the computed values for this function are updated. Transformations that change the number of rows in subsequent recipe steps do not affect the values computed for this step.

To perform a simple counting of distinct non-nulls without conditionals, use the `COUNTDISTINCT`

function. See COUNTDISTINCT Function.

**Wrangle vs. SQL:** This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

## Basic Usage

countdistinctif(entries, entryValidation == 'Ok')

**Output:** Generates a two-column table containing the unique values for `City`

and the count of distinct non-null values in the `entries`

column for that `City`

value when the `entryValidation`

value is `'Ok'`

. The `limit`

parameter defines the maximum number of output columns.

## Syntax and Arguments

countdistinctif(col_ref, test_expression) [group:group_col_ref] [limit:limit_count]

Argument | Required? | Data Type | Description |
---|---|---|---|

col_ref | Y | string | Reference to the column you wish to evaluate. |

test_expression | Y | string | Expression that is evaluated. Must resolve to |

For more information on syntax standards, see Language Documentation Syntax Notes.

For more information on the `group`

and `limit`

parameter, see Pivot Transform.

### col_ref

Name of the column whose values you wish to use in the calculation. Column must be a numeric (Integer or Decimal) type.

** Usage Notes:**

Required? | Data Type | Example Value |
---|---|---|

Yes | String that corresponds to the name of the column | myValues |

### test_expression

This parameter contains the expression to evaluate. This expression must resolve to a Boolean (`true`

or `false`

) value.

** Usage Notes:**

Required? | Data Type | Example Value |
---|---|---|

Yes | String expression that evaluates to | (LastName == 'Mouse' && FirstName == 'Mickey') |

## Examples

**Suggerimento**

For additional examples, see Common Tasks.

### Example - Summarize Voter Registrations

This example illustrates how you can use conditional calculation functions.

**Functions**:

Item | Description |
---|---|

SUMIF Function | Generates the sum of rows in each group that meet a specific condition. |

COUNTDISTINCTIF Function | Generates the count of distinct non-null values for rows in each group that meet a specific condition. |

**Source:**

Here is some example polling data across 16 precincts in 8 cities across 4 counties, where registrations have been invalidated at the polling station, preventing voters from voting. Precincts where this issue has occurred previously have been added to a watch list (`precinctWatchList`

).

totalReg | invalidReg | precinctWatchList | precinctId | cityId | countyId |
---|---|---|---|---|---|

731 | 24 | y | 1 | 1 | 1 |

743 | 29 | y | 2 | 1 | 1 |

874 | 0 | 3 | 2 | 1 | |

983 | 0 | 4 | 2 | 1 | |

622 | 29 | 5 | 3 | 2 | |

693 | 0 | 6 | 3 | 2 | |

775 | 37 | y | 7 | 4 | 2 |

1025 | 49 | y | 8 | 4 | 2 |

787 | 13 | 9 | 5 | 3 | |

342 | 0 | 10 | 5 | 3 | |

342 | 39 | y | 11 | 6 | 3 |

387 | 28 | y | 12 | 6 | 3 |

582 | 59 | 13 | 7 | 4 | |

244 | 0 | 14 | 7 | 4 | |

940 | 6 | y | 15 | 8 | 4 |

901 | 4 | y | 16 | 8 | 4 |

**Transformation:**

First, you want to sum up the invalid registrations (`invalidReg`

) for precincts that are already on the watchlist (`precinctWatchList = y`

). These sums are grouped by city, which can span multiple precincts:

Transformation Name | |
---|---|

Parameter: Formula type | Single row formula |

Parameter: Formula | SUMIF(invalidReg, precinctWatchList == "y") |

Parameter: Group rows by | cityId |

Parameter: New column name | 'invalidRegbyCityId' |

The `invalidRegbyCityId`

column contains invalid registrations across the entire city.

Now, at the county level, you want to identify the number of precincts that were on the watch list and were part of a city-wide registration problem.

In the following step, the number of cities in each count are counted where invalid registrations within a city is greater than `60`

.

This step creates a pivot aggregation.

Transformation Name | |
---|---|

Parameter: Row labels | countyId |

Parameter: Values | COUNTDISTINCTIF(precinctId, invalidRegbyCityId > 60) |

Parameter: Max number of columns to create | 1 |

**Results:**

countyId | countdistinctif_precinctId |
---|---|

1 | 0 |

2 | 2 |

3 | 2 |

4 | 0 |

The voting officials in counties 2 and 3 should investigate their precinct registration issues.