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read(fname,
delimiter=' , ' ,
skip_header=False)
read loads cell data, column headers and type information for each
column given a file path to a CSV formatted file. |
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map_names(table,
f)
new_rows executes f on every column header in the table, with three
arguments, in order: column type, column index, column name. |
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map_data(table,
f)
new_rows executes f on every cell of data with five arguments, in
order: column type, column name, row index, column index, contents. |
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cast(table)
cast type casts all of the values in 'rows' to their corresponding
types in types. |
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convert_missing_cells(table,
dstr='
' ,
dint=0,
dfloat=0.0)
convert_missing_cells changes the values of all NULL cells to the
values specified by dstr, dint and dfloat. |
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convert_columns(table,
**kwargs)
convert_columns executes converter functions on specific columns,
where the parameter names for kwargs are the column names, and the
parameter values are functions of one parameter that return a single
value. |
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convert_types(table,
fstr=None,
fint=None,
ffloat=None)
convert_types works just like convert_columns, but on types instead
of specific columns. |
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column(table,
colname)
column returns the column with name "colname", where the
column returned is a triple of the column type, the column name and a
NumPy array of cells in the column. |
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columns(table)
columns returns a list of all columns in the data set, where each
column is a triple of its type, name and a NumPy array of cells in
the column. |
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frequencies(column)
frequencies returns a dictionary where the keys are unique values in
the column, and the values correspond to the frequency of each value
in the column. |
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type_str(typ)
type_str returns a string representation of a column type. |
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cell_str(cell_contents)
cell_str is a convenience function for converting cell contents to a
string when there are still NULL values. |
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print_data_table(table)
print_data_table is a convenience function for pretty-printing the
data in tabular format, including header names and type annotations. |
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