General functions

Data manipulations

`melt`(frame[, id_vars, value_vars, var_name, ...])	“Unpivots” a DataFrame from wide format to long format, optionally leaving
`pivot`(index, columns, values)	Produce ‘pivot’ table based on 3 columns of this DataFrame.
`pivot_table`(data[, values, index, columns, ...])	Create a spreadsheet-style pivot table as a DataFrame.
`crosstab`(index, columns[, values, rownames, ...])	Compute a simple cross-tabulation of two (or more) factors.
`cut`(x, bins[, right, labels, retbins, ...])	Return indices of half-open bins to which each value of x belongs.
`qcut`(x, q[, labels, retbins, precision])	Quantile-based discretization function.
`merge`(left, right[, how, on, left_on, ...])	Merge DataFrame objects by performing a database-style join operation by columns or indexes.
`merge_ordered`(left, right[, on, left_on, ...])	Perform merge with optional filling/interpolation designed for ordered data like time series data.
`merge_asof`(left, right[, on, left_on, ...])	Perform an asof merge.
`concat`(objs[, axis, join, join_axes, ...])	Concatenate pandas objects along a particular axis with optional set logic along the other axes.
`get_dummies`(data[, prefix, prefix_sep, ...])	Convert categorical variable into dummy/indicator variables
`factorize`(values[, sort, order, ...])	Encode input values as an enumerated type or categorical variable

`isnull`(obj)	Detect missing values (NaN in numeric arrays, None/NaN in object arrays)
`notnull`(obj)	Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays.

to_numeric(arg[, errors, downcast]) Convert argument to a numeric type.

`to_datetime`(args, *kwargs)	Convert argument to datetime.
`to_timedelta`(args, *kwargs)	Convert argument to timedelta
`date_range`([start, end, periods, freq, tz, ...])	Return a fixed frequency datetime index, with day (calendar) as the default
`bdate_range`([start, end, periods, freq, tz, ...])	Return a fixed frequency datetime index, with business day as the default
`period_range`([start, end, periods, freq, name])	Return a fixed frequency datetime index, with day (calendar) as the default
`timedelta_range`([start, end, periods, freq, ...])	Return a fixed frequency timedelta index, with day as the default
`infer_freq`(index[, warn])	Infer the most likely frequency given the input index.

eval(expr[, parser, engine, truediv, ...]) Evaluate a Python expression as a string using various backends.

test Run tests for module using nose.