# General functions

## Data manipulations

`melt`(frame[, id_vars, value_vars, var_name, …]) | Unpivot a DataFrame from wide format to long format, optionally leaving identifier variables set.
`pivot`(data[, index, columns, values]) | Return reshaped DataFrame organized by given index / column values.
`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, …]) | Bin values into discrete intervals.
`qcut`(x, q[, labels, retbins, precision, …]) | Quantile-based discretization function.
`merge`(left, right[, how, on, left_on, …]) | Merge DataFrame or named Series objects with a database-style join.
`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 the object as an enumerated type or categorical variable.
`unique`(values) | Hash table-based unique.
`wide_to_long`(df, stubnames, i, j[, sep, suffix]) | Wide panel to long format.

## Top-level missing data

`isna`(obj) | Detect missing values for an array-like object.
`isnull`(obj) | Detect missing values for an array-like object.
`notna`(obj) | Detect non-missing values for an array-like object.
`notnull`(obj) | Detect non-missing values for an array-like object.

## Top-level conversions

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

## Top-level dealing with datetimelike

`to_datetime`(arg[, errors, dayfirst, …]) | Convert argument to datetime.
`to_timedelta`(arg[, unit, box, errors]) | Convert argument to timedelta.
`date_range`([start, end, periods, freq, tz, …]) | Return a fixed frequency DatetimeIndex.
`bdate_range`([start, end, periods, freq, tz, …]) | Return a fixed frequency DatetimeIndex, with business day as the default frequency
`period_range`([start, end, periods, freq, name]) | Return a fixed frequency PeriodIndex, with day (calendar) as the default frequency
`timedelta_range`([start, end, periods, freq, …]) | Return a fixed frequency TimedeltaIndex, with day as the default frequency
`infer_freq`(index[, warn]) | Infer the most likely frequency given the input index.

## Top-level dealing with intervals

`interval_range`([start, end, periods, freq, …]) | Return a fixed frequency IntervalIndex

## Top-level evaluation

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

## Hashing

`util.hash_array`(vals[, encoding, hash_key, …]) | Given a 1d array, return an array of deterministic integers.
`util.hash_pandas_object`(obj[, index, …]) | Return a data hash of the Index/Series/DataFrame

## Testing

`test`([extra_args]) |

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