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>The Column class</TITLE
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TITLE="Library Reference"
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SUMMARY="Header navigation table"
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> User's Guide: Hierarchical datasets in Python - Release 1.3.2</TH
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>Chapter 4. Library Reference</TD
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NAME="ColumnClassDescr"
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>Each instance of this class is associated with one column
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of every table. These instances are mainly used to fetch and
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set actual data from the table columns, but there are a few
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other associated methods to deal with indexes.
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NAME="subsection4.9.1"
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CLASS="computeroutput"
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>The name of the associated
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>The complete pathname of the
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associated column. This is mainly useful in nested
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columns; for non-nested ones this value is the same a
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CLASS="computeroutput"
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>The data type of the column.
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>The shape of the column.
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>The associated <SAMP
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HREF="x4983.html#IndexClassDescr"
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CLASS="computeroutput"
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> if does not exist).
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>Whether the index is dirty or not
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NAME="subsection4.9.2"
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NAME="subsubsection4.9.2.1"
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NAME="createIndexColumnDescr"
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CLASS="computeroutput"
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HREF="x4983.html#IndexClassDescr"
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NAME="subsubsection4.9.2.2"
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NAME="reIndexColumnDescr"
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>Recompute the index associated with this column. This
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can be useful when you suspect that, for any reason, the
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index information is no longer valid and want to rebuild
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NAME="subsubsection4.9.2.3"
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>reIndexDirty()</SPAN
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NAME="reIndexDirtyColumnDescr"
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>Recompute the existing index only if it is dirty. This
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can be useful when you have set the <SAMP
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CLASS="computeroutput"
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parameter to 0 in <SAMP
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CLASS="computeroutput"
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HREF="x4983.html#IndexPropsInitDescr"
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table and want to update the column's index after a
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invalidating index operation
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CLASS="computeroutput"
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>Table.removeRows</SAMP
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NAME="subsubsection4.9.2.4"
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NAME="removeIndexColumnDescr"
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>Delete the associated column's index. After doing that,
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you will loose the indexation information on
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disk. However, you can always re-create it using the
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CLASS="computeroutput"
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HREF="x3737.html#createIndexColumnDescr"
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NAME="subsection4.9.3"
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NAME="subsubsection4.9.3.1"
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>__getitem__(key)</SPAN
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NAME="Column.__getitem__"
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>Returns a column element or slice. It takes different
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actions depending on the type of the <SPAN
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CLASS="computeroutput"
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element in the column is returned as a scalar object
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> object, depending on
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determined by this slice is returned as a
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CLASS="computeroutput"
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> print "Column handlers:"
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for name in table.colnames:
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print table.cols[name]
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print "Some selections:"
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print "Select table.cols.name[1]-->", table.cols.name[1]
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print "Select table.cols.name[1:2]-->", table.cols.name[1:2]
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print "Select table.cols.lati[1:3]-->", table.cols.lati[1:3]
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print "Select table.cols.pressure[:]-->", table.cols.pressure[:]
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print "Select table.cols['temperature'][:]-->", table.cols['temperature'][:]
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and the output of this for a certain arbitrary table is:
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> Column handlers:
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/table.cols.name (Column(1,), CharType)
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/table.cols.lati (Column(2,), Int32)
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/table.cols.longi (Column(1,), Int32)
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/table.cols.pressure (Column(1,), Float32)
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/table.cols.temperature (Column(1,), Float64)
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Select table.cols.name[1]--> Particle: 11
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Select table.cols.name[1:2]--> ['Particle: 11']
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Select table.cols.lati[1:3]--> [[11 12]
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Select table.cols.pressure[:]--> [ 90. 110. 132.]
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Select table.cols['temperature'][:]--> [ 100. 121. 144.]
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CLASS="computeroutput"
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>examples/table2.py</SAMP
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NAME="subsubsection4.9.3.2"
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>4.9.3.2. __setitem__(key, value)</A
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NAME="Column.__setitem__"
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>It takes different actions depending on the
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CLASS="computeroutput"
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CLASS="computeroutput"
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element in the column is set to
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> must be a scalar or
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depending on column's shape.
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CLASS="computeroutput"
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CLASS="computeroutput"
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CLASS="computeroutput"
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> # Modify row 1
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table.cols.col1[1] = -1
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# Modify rows 1 and 3
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table.cols.col1[1::2] = [2,3]
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>Which is equivalent to:</P
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> # Modify row 1
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table.modifyColumns(start=1, columns=[[-1]], names=["col1"])
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# Modify rows 1 and 3
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columns = numarray.records.fromarrays([[2,3]], formats="i4")
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table.modifyColumns(start=1, step=2, columns=columns, names=["col1"])
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