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.