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# General functions

Note

For an overview, see Reshaping and pivot tables.

## Data manipulations

`melt`(frame[, id_vars, value_vars, var_name, ...]) | Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
`pivot`(data, *, columns[, index, 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 a merge for ordered data with optional filling/interpolation.
`merge_asof`(left, right[, on, left_on, ...]) | Perform a merge by key distance.
`concat`(objs, *[, axis, join, ignore_index, ...]) | Concatenate pandas objects along a particular axis.
`get_dummies`(data[, prefix, prefix_sep, ...]) | Convert categorical variable into dummy/indicator variables.
`from_dummies`(data[, sep, default_category]) | Create a categorical `DataFrame` from a `DataFrame` of dummy variables.
`factorize`(values[, sort, use_na_sentinel, ...]) | Encode the object as an enumerated type or categorical variable.
`unique`(values) | Return unique values based on a hash table.
`lreshape`(data, groups[, dropna]) | Reshape wide-format data to long.
`wide_to_long`(df, stubnames, i, j[, sep, suffix]) | Unpivot a DataFrame from wide 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 dealing with numeric data

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

## Top-level dealing with datetimelike data

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

## Top-level dealing with Interval data

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

## Top-level evaluation

`col`(col_name) | Generate deferred object representing a column of a DataFrame.
`eval`(expr[, parser, engine, local_dict, ...]) | Evaluate a Python expression as a string using various backends.

## Datetime formats

`tseries.api.guess_datetime_format`(dt_str[, ...]) | Guess the datetime format of a given datetime string.

## 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.

## Importing from other DataFrame libraries

`api.interchange.from_dataframe`(df[, allow_copy]) | Build a `pd.DataFrame` from any DataFrame supporting the interchange protocol.