Transform an existing column in a Tabular object:

Split an existing column in a Tabular object:

TransformColumns with a function f acts on whole rows as associations if columns are named:

On a Tabular object with anonymous columns, the rows are expressed as lists:

Use the operator form of TransformColumns:

Use "a"Nothing to remove column "a" from the result:

Remove three columns from the result:

Add the second and fourth columns to make a fifth column in a matrix:

With Apply around the function, there is an argument for each column:

Use Key in the Function body of Tabular transformations when the key is a general expression:

Transform a list of associations:

Modify an existing column:

Create a new column as a function of existing columns:

Transform a Dataset object:

Transform the columns of a normal matrix:

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

Find which elements in a column are above the median:

Include a constant column with the median value:

Compute the median only once by using the operator form:

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

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

Take a Tabular object:

TransformColumns by default operates sequentially on each row:

TransformColumns with ColumnwiseValue[expr] evaluates expr once first, then acts on each row:

TransformColumns with ColumnwiseThread[expr] evaluates expr once, acting on whole columns:

Take a Tabular object:

By default, TransformColumns places new columns at the end:

Use column "b" as an anchor column after which the new column is placed:

Place the new column after column "a":

Place the new column before column "a":

Use column names without a transformation to anchor column placement in the result:

By default, new columns are appended on the right:

Place "e" before "a" and "f" after "b":

Place "e" first and "f" after "b" and remove "a" from the result:

Use MissingFallback to fill in missing values when transforming a Tabular object:

Use row values from the previous column:

Use row values from the next column:

Use a fixed value:

Use functions of columns in MissingFallback to fill in missing values when transforming a Tabular object:

Use the sum of row values from other columns:

Use the mean of row values from neighboring columns:

This data contains information on kidney transplant patients—time in days to death or on-study time since transplant at the given age:

Use TransformColumns to give meaning to the numerically encoded data:

Now use the categories to compute mean survival time in years with PivotTable:

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 new column:

Visualize:

Time series of Lake Mead water levels:

Create a Tabular object:

Split the dates into year and month columns using TransformColumns and remove "Date" column:

Use PivotToColumns to convert into a more compact form for visualization: