The with command, applied to a DataFrame object, makes columns of the DataFrame available as variables at the interactive level.

The unwith command undoes the effects of prior calls to with.

The with command is effective only at the top level, and intended primarily for interactive use.  Because with operates by using lexical scoping, it does not work inside the bodies of procedures, module definitions, or within statements.  See the Examples section below.

DataFrames are collections of named columns of data called DataSeries. They are explained in more detail on the DataFrame help page.

Only column labels that are symbols can be used as variables. Columns with labels that are not symbols (for example, indexed names or other algebraic expressions) are ignored by with.

When with is called with a DataFrame as its argument, it binds the column names of the DataFrame to the global namespace.  This makes the column names available to the interactive user at the top level of the Maple interaction loop.

The unwith command undoes the effects of prior calls to with. That is, when it is called with as its argument a DataFrame that was previously submitted to with, unwith unbinds the column names in the global namespace, any previous bindings of those names are restored.

The restart command clears the Maple internal memory, clearing any bindings made by with.

The with command is also used for making the exports of a package accessible in the global namespace. This use is explained on the with help page. This is different from using the command on a DataFrame object in several ways: the exports of a package are (typically) procedures, whereas the columns in a DataFrame for example, are not.

$\mathrm{df}≔\mathrm{DataFrame}\left(⟨⟨1|2⟩\,⟨3|4⟩⟩\,\mathrm{columns}=\left[a\,b\right]\right)$

$\mathrm{df}≔\left[\begin{array}{ccc}& a& b\\ 1& 1& 2\\ 2& 3& 4\end{array}\right]$

$\mathrm{df2}≔\mathrm{DataFrame}\left(⟨⟨5|6⟩\,⟨7|8⟩⟩\,\mathrm{columns}=\left[a\,c\right]\right)$

$\mathrm{df2}≔\left[\begin{array}{ccc}& a& c\\ 1& 5& 6\\ 2& 7& 8\end{array}\right]$

$\mathrm{df}\left[a\right]$

$\left[\begin{array}{cc}1& 1\\ 2& 3\end{array}\right]$

$a$

$a$

$\mathrm{with}\left(\mathrm{df}\right)$

$\left[a\,b\right]$

$a$

$\left[\begin{array}{cc}1& 1\\ 2& 3\end{array}\right]$

$\mathrm{unwith}\left(\mathrm{df}\right)$

$a$

$a$

$a≔12.3$

$a≔12.3$

$\mathrm{with}\left(\mathrm{df}\right)$

$\left[a\,b\right]$

$a$

$\left[\begin{array}{cc}1& 1\\ 2& 3\end{array}\right]$

$:-a$

$12.3$

$\mathrm{with}\left(\mathrm{df2}\right)$

$\left[a\,c\right]$

$a$

$\left[\begin{array}{cc}1& 5\\ 2& 7\end{array}\right]$

$\mathrm{df}\left[a\right]$

Error, (in DataFrame:-`?[]`) invalid row index into DataFrame: non-Boolean DataSeries

$\mathrm{df}\left[:-a\right]$

Error, (in DataFrame:-`?[]`) invalid index (12.3) into DataFrame

$\mathrm{df}\left[':-a'\right]$

$\left[\begin{array}{cc}1& 1\\ 2& 3\end{array}\right]$

The DataFrame/with and DataFrame/unwith commands were introduced in Maple 2016.

For more information on Maple 2016 changes, see Updates in Maple 2016.