Extract one column from a Tabular object:

This is equivalent to using a list with only that column:

Part extraction returns different results using "date" or {"date"}:

Extract several columns from a Tabular object:

This is equivalent to using Part extraction:

ConstructColumns preserves the column order of the original, but Part uses the new given order:

Use functional notation to extract columns:

Using existing column names preserves order, but using new keys allows column reordering:

Use the operator form of ConstructColumns to extract columns:

Extract columns from a Dataset object:

Extract a column from a list of associations:

Missing entries in any of the rows will be reported as Missing expressions:

Extract columns from a matrix using a list of functions in the second argument of ConstructColumns:

Part extraction provides a more direct syntax:

Take a Tabular object with three columns:

Construct another Tabular object with two columns computed using given functions:

The functions receive each row as an association:

Take a Tabular object with three columns and no column keys:

Construct another Tabular object with columns computed as given by a list of functions of the rows:

Use Apply[f] instead of f to handle each column with a separate numbered slot:

The functions receive each row as a list of values:

Create a new column from a list of associations:

Use ColumnwiseValue to subtract the mean of a column from its values:

This is equivalent to first computing the mean value once and then subtracting it in every row:

Find which elements in a column are above the median in this Tabular object:

The median value is computed once at the beginning and then used during the computation at each row:

Use ColumnwiseThread to compute a vector-valued transformation of an entire column:

With ColumnwiseValue, you get the same list for each row:

Weather data from JFK airport in C, mbar and km/h:

Remove the rows containing at least one missing value:

Define the wind chill factor (adapt the formula to the units of data):

Create a Tabular object with a new column and a few selected columns from the original data:

Visualize:

Take a Tabular object with data of monthly ozone readings for Los Angeles:

Compute descriptive statistics for each year:

Plot the quartiles yearly series: