General functions

Data manipulations

melt(frame[, id_vars, value_vars, var_name, …])

Unpivot a DataFrame from wide to long format, optionally leaving identifiers 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])

Bin values into discrete intervals.

qcut(x, q[, labels])

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.

merge_asof(left, right[, on, left_on, …])

Perform an asof merge.

concat(objs, ForwardRef]], …[, axis, …])

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, na_sentinel, …)

Encode the object as an enumerated type or categorical variable.


Hash table-based unique.

wide_to_long(df, stubnames, i, j, sep, suffix)

Wide panel to long format.

Top-level missing data


Detect missing values for an array-like object.


Detect missing values for an array-like object.


Detect non-missing values for an array-like object.


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, 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.

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, truediv, local_dict, …])

Evaluate a Python expression as a string using various backends.


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.