A DataSeries is a one-dimensional sequence of data with a label for each data point. For example, you can keep track of nutritional energy values (in $⟦\mathrm{kJ}⟧$ per 100 $⟦g⟧$) of certain types of berries, as follows:

energy := DataSeries(<220, 288, 136>, labels = [Raspberry, Grape, Strawberry]);

This allows you to access the energy values by position (number) or label (name).

energy[2];

$288$

energy[Strawberry];

$136$

You can determine which values satisfy some criteria by using elementwise operators. The result is a DataSeries of true/false values.

energy >~ 200;

You can use this DataSeries to filter the entries in the original DataSeries.

energy[energy >~ 200];

A DataFrame is a two-dimensional rectangular table of data with a label for each column and for each row. For example, you can keep track of various properties of certain types of berries as follows:

genus := <"Rubus", "Vitis", "Fragaria">:

carbohydrates := <11.94, 18.1, 7.68>:

total_tons := < 543421, 58500118, 4594539 >:

top_producer := < Russia, China, USA >:

berry_data := DataFrame([genus, energy, carbohydrates, total_tons, top_producer], columns = [Genus, Energy, Carbohydrates, `Total tons`, `Top producer`], rows = Labels(energy));

You can access columns by indexing the berry DataFrame with a number, for the position, or a name. Each column is a DataSeries.

berry_data[4];

berry_data[Carbohydrates];

Because columns are DataSeries, you can test properties like for DataSeries.

berry_data[Energy] >~ 200;

You can also filter rows. This returns a new DataFrame with a subset of the data.

berry_data[berry_data[Energy] >~ 200];

By using the with command, you can simplify the syntax a little: the column names then represent the corresponding column directly, without the use of indexing.

with(berry_data);

$\left[\mathrm{Genus}\&comma;\mathrm{Energy}\&comma;\mathrm{Carbohydrates}\&comma;\mathrm{Total\; tons}\&comma;\mathrm{Top\; producer}\right]$

Carbohydrates;

berry_data[Energy >~ 200];

You can find more details and examples on the following help pages:

The DataFrame Guide gives a detailed list of many of the more popular operations supported by DataFrames.

The DataSeries indexing and DataFrame indexing help pages describe the many other ways to use indexing with these two data structures in some detail.

The DataSeries and DataFrame help pages give an overview of the data structures.

The Dataframe Statistics page gives an overview of many common statistical operations on data frames.