General functions — pandas 2.2.3 documentation (original) (raw)
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#
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. |