Skip to content
Back to top
View this page

# Homogenize rows

Fill in missing rows in a series. This can be used, for instance, to add rows for missing years in a time series.

## Create rows for missing values

We can insert a default row for each value that is missing in a table from a given sequence of values.

Starting with a table like this, we can fill in rows for all missing years:

year | female_count | male_count
--- | --- | ---
1997 | 2 | 1
2000 | 4 | 3
2002 | 4 | 5
2003 | 1 | 2

    key = 'year'
    expected_values = (1997, 1998, 1999, 2000, 2001, 2002, 2003)

    # Your default row should specify column values not in `key`
    default_row = (0, 0)

    new_table = table.homogenize(key, expected_values, default_row)

The result will be:

year | female_count | male_count
--- | --- | ---
1997 | 2 | 1
1998 | 0 | 0
1999 | 0 | 0
2000 | 4 | 3
2001 | 0 | 0
2002 | 4 | 5
2003 | 1 | 2

## Create dynamic rows based on missing values

We can also specify new row values with a value-generating function:

    key = 'year'
    expected_values = (1997, 1998, 1999, 2000, 2001, 2002, 2003)

    # If default row is a function, it should return a full row
    def default_row(missing_value):
      return (missing_value, missing_value-1997, missing_value-1997)

    new_table = table.homogenize(key, expected_values, default_row)

The new table will be:

year | female_count | male_count
--- | --- | ---
1997 | 2 | 1
1998 | 1 | 1
1999 | 2 | 2
2000 | 4 | 3
2001 | 4 | 4
2002 | 4 | 5
2003 | 1 | 2