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# 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#RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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