General functions¶

Data manipulations

`melt`(frame[, id_vars, value_vars, var_name, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
`crosstab`(index, columns[, values, rownames, ...])	Compute a simple cross tabulation of two (or more) factors.
`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`.
`cut`(x, bins[, right, labels, retbins, ...])	Bin values into discrete intervals.
`qcut`(x, q[, labels, retbins, precision, ...])	Quantile-based discretization function.
`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.
`merge`(left, right[, how, on, left_on, ...])	Merge DataFrame or named Series objects with a database-style join.
`merge_asof`(left, right[, on, left_on, ...])	Perform a merge by key distance.
`unique`(values)	Return unique values based on a hash table.

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

`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.
`to_datetime`(arg[, errors, dayfirst, ...])	Convert argument to datetime.

Hybrid Execution functions

Shows a log of all backend switching decisions made by Snowpark pandas.