General functions

Data manipulations

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

Unpivot a DataFrame from wide format to long format, optionally leaving identifier variables 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, 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 merge with optional filling/interpolation designed for ordered data like time series data.

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

Perform an asof merge.

concat(objs[, axis, join, join_axes, …])

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, order, …])

Encode the object as an enumerated type or categorical variable.

unique(values)

Hash table-based unique.

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

Wide panel 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 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, box, 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, with day (calendar) as the default frequency

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, engine, truediv, …])

Evaluate a Python expression as a string using various backends.

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

Testing

test([extra_args])

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