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, *, 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

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.