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 |