Random Data Hacks

nctoolkit features a number of useful methods to tweak data.

Shifting time

Sometimes the times in datasets are not quite what we want, and we need some way to adjust time. An example of this is when you are missing a year of data, so want to copy data from the prior year and use it. But first you would need to shift the times in that year forward by a year. You can do this with the shift method. This let’s you shift time forward by a specified number of hours, days, months or years. You just need to supply hours, days, months or years as an argument. So, if you wanted to shift time backward by one year, you would do the following:

ds.shift(years = -1)

If you wanted to shift time forward by 12 hours, this would do it:

ds.shift(hours = 12)

Note: this method allows partial matches to the arguments, so you could use hour, day, month or year just as easily.

Adding cell areas to a dataset

You can add grid cell areas to a dataset as follows:


By default, this will add the cell area (in square metres) to the dataset. If you want the dataset to only include cell areas you need to set the join argument to False:

ds.cell_area(join = False)

Of course, this method will only if it is possible to calculate the areas the grid cells.

Changing the format of the netCDF files in a dataset

Sometimes you will want to change the format of the files in a dataset. You can do this using the format method. This let’s you set the format, with the following options:

  • netCDF = “nc1”

  • netCDF version 2 (64-bit offset) = “nc2”/”nc”

  • netCDF4 (HDF5) = “nc4”

  • netCDF4-classi = “nc4c”

  • netCDF version 5 (64-bit data) = “nc5”

So, if you want to set the format to netCDF4, you would do the following:


Getting rid of dimensions with only one value

Sometimes you will have a dataset that has a dimension with only one value, and you might want to get rid of that dimension. For example, you might only have one one timestep and keeping it may have no value. Getting rid of that dimension can be done using the reduce_dims method. It works as follows:


Removing leap days

If you want to remove a leap day from a dataset, just do the following:

ds.drop(month = 2, day = 29)

Renaming variables

If you want to rename variables, you can use the rename method. Just provide a dictionary where the keys are the original variable names and the values are the new names. So if you wanted to rename a variable x to y, you would do this: