pandas API Reference

http://pandas.pydata.org/pandas-docs/stable/api.html#api-reference

 

API Reference

This page gives an overview of all public pandas objects, functions and methods. All classes and functions exposed in pandas.* namespace are public.

Some subpackages are public which include pandas.errors, pandas.plotting, and pandas.testing. Public functions in pandas.io and pandas.tseries submodules are mentioned in the documentation. pandas.api.types subpackage holds some public functions related to data types in pandas.

Warning

The pandas.core, pandas.compat, and pandas.util top-level modules are PRIVATE. Stable functionality in such modules is not guaranteed.

Input/Output

Pickling

read_pickle(path[, compression])

Load pickled pandas object (or any object) from file.

Flat File

read_table(filepath_or_buffer[, sep, …])	Read general delimited file into DataFrame
read_csv(filepath_or_buffer[, sep, …])	Read CSV (comma-separated) file into DataFrame
read_fwf(filepath_or_buffer[, colspecs, widths])	Read a table of fixed-width formatted lines into DataFrame
read_msgpack(path_or_buf[, encoding, iterator])	Load msgpack pandas object from the specified file path

Clipboard

read_clipboard([sep])

Read text from clipboard and pass to read_table.

Excel

read_excel(io[, sheet_name, header, names, …])	Read an Excel table into a pandas DataFrame
ExcelFile.parse([sheet_name, header, names, …])	Parse specified sheet(s) into a DataFrame

JSON

read_json([path_or_buf, orient, typ, dtype, …])

Convert a JSON string to pandas object

json_normalize(data[, record_path, meta, …])	“Normalize” semi-structured JSON data into a flat table
build_table_schema(data[, index, …])	Create a Table schema from data.

HTML

read_html(io[, match, flavor, header, …])

Read HTML tables into a list of DataFrame objects.

HDFStore: PyTables (HDF5)

read_hdf(path_or_buf[, key, mode])	Read from the store, close it if we opened it.
HDFStore.put(key, value[, format, append])	Store object in HDFStore
HDFStore.append(key, value[, format, …])	Append to Table in file.
HDFStore.get(key)	Retrieve pandas object stored in file
HDFStore.select(key[, where, start, stop, …])	Retrieve pandas object stored in file, optionally based on where criteria
HDFStore.info()	print detailed information on the store
HDFStore.keys()	Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore.

Feather

read_feather(path[, nthreads])

Load a feather-format object from the file path

Parquet

read_parquet(path[, engine, columns])

Load a parquet object from the file path, returning a DataFrame.

SAS

read_sas(filepath_or_buffer[, format, …])

Read SAS files stored as either XPORT or SAS7BDAT format files.

SQL

read_sql_table(table_name, con[, schema, …])	Read SQL database table into a DataFrame.
read_sql_query(sql, con[, index_col, …])	Read SQL query into a DataFrame.
read_sql(sql, con[, index_col, …])	Read SQL query or database table into a DataFrame.

Google BigQuery

read_gbq(query[, project_id, index_col, …])

Load data from Google BigQuery.

STATA

read_stata(filepath_or_buffer[, …])

Read Stata file into DataFrame.

StataReader.data(**kwargs)	(DEPRECATED) Reads observations from Stata file, converting them into a dataframe
StataReader.data_label()	Returns data label of Stata file
StataReader.value_labels()	Returns a dict, associating each variable name a dict, associating each value its corresponding label
StataReader.variable_labels()	Returns variable labels as a dict, associating each variable name with corresponding label
StataWriter.write_file()

General functions

Data manipulations

melt(frame[, id_vars, value_vars, var_name, …])	“Unpivots” a DataFrame from wide format to long format, optionally leaving identifier variables set.
pivot(index, columns, values)	Produce ‘pivot’ table based on 3 columns of this DataFrame.
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 objects by performing a database-style join operation by columns or indexes.
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.

Testing

test([extra_args])

Series

Constructor

Series([data, index, dtype, name, copy, …])

One-dimensional ndarray with axis labels (including time series).

Attributes

Axes

Series.index

The index (axis labels) of the Series.

Series.values	Return Series as ndarray or ndarray-like depending on the dtype
Series.dtype	return the dtype object of the underlying data
Series.ftype	return if the data is sparse|dense
Series.shape	return a tuple of the shape of the underlying data
Series.nbytes	return the number of bytes in the underlying data
Series.ndim	return the number of dimensions of the underlying data, by definition 1
Series.size	return the number of elements in the underlying data
Series.strides	return the strides of the underlying data
Series.itemsize	return the size of the dtype of the item of the underlying data
Series.base	return the base object if the memory of the underlying data is shared
Series.T	return the transpose, which is by definition self
Series.memory_usage([index, deep])	Return the memory usage of the Series.
Series.hasnans	return if I have any nans; enables various perf speedups
Series.flags
Series.empty
Series.dtypes	return the dtype object of the underlying data
Series.ftypes	return if the data is sparse|dense
Series.data	return the data pointer of the underlying data
Series.is_copy
Series.name
Series.put(*args, **kwargs)	Applies the put method to its values attribute if it has one.

Conversion

Series.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
Series.infer_objects()	Attempt to infer better dtypes for object columns.
Series.convert_objects([convert_dates, …])	(DEPRECATED) Attempt to infer better dtype for object columns.
Series.copy([deep])	Make a copy of this object’s indices and data.
Series.bool()	Return the bool of a single element PandasObject.
Series.to_period([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed)
Series.to_timestamp([freq, how, copy])	Cast to datetimeindex of timestamps, at beginning of period
Series.tolist()	Return a list of the values.
Series.get_values()	same as values (but handles sparseness conversions); is a view

Indexing, iteration

Series.get(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
Series.at	Access a single value for a row/column label pair.
Series.iat	Access a single value for a row/column pair by integer position.
Series.loc	Access a group of rows and columns by label(s) or a boolean array.
Series.iloc	Purely integer-location based indexing for selection by position.
Series.__iter__()	Return an iterator of the values.
Series.iteritems()	Lazily iterate over (index, value) tuples
Series.items()	Lazily iterate over (index, value) tuples
Series.keys()	Alias for index
Series.pop(item)	Return item and drop from frame.
Series.item()	return the first element of the underlying data as a python scalar
Series.xs(key[, axis, level, drop_level])	Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.

For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.

Binary operator functions

Series.add(other[, level, fill_value, axis])	Addition of series and other, element-wise (binary operator add).
Series.sub(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator sub).
Series.mul(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator mul).
Series.div(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator truediv).
Series.truediv(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator truediv).
Series.floordiv(other[, level, fill_value, axis])	Integer division of series and other, element-wise (binary operator floordiv).
Series.mod(other[, level, fill_value, axis])	Modulo of series and other, element-wise (binary operator mod).
Series.pow(other[, level, fill_value, axis])	Exponential power of series and other, element-wise (binary operator pow).
Series.radd(other[, level, fill_value, axis])	Addition of series and other, element-wise (binary operator radd).
Series.rsub(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator rsub).
Series.rmul(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator rmul).
Series.rdiv(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator rtruediv).
Series.rtruediv(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator rtruediv).
Series.rfloordiv(other[, level, fill_value, …])	Integer division of series and other, element-wise (binary operator rfloordiv).
Series.rmod(other[, level, fill_value, axis])	Modulo of series and other, element-wise (binary operator rmod).
Series.rpow(other[, level, fill_value, axis])	Exponential power of series and other, element-wise (binary operator rpow).
Series.combine(other, func[, fill_value])	Perform elementwise binary operation on two Series using given function with optional fill value when an index is missing from one Series or the other
Series.combine_first(other)	Combine Series values, choosing the calling Series’s values first.
Series.round([decimals])	Round each value in a Series to the given number of decimals.
Series.lt(other[, level, fill_value, axis])	Less than of series and other, element-wise (binary operator lt).
Series.gt(other[, level, fill_value, axis])	Greater than of series and other, element-wise (binary operator gt).
Series.le(other[, level, fill_value, axis])	Less than or equal to of series and other, element-wise (binary operator le).
Series.ge(other[, level, fill_value, axis])	Greater than or equal to of series and other, element-wise (binary operator ge).
Series.ne(other[, level, fill_value, axis])	Not equal to of series and other, element-wise (binary operator ne).
Series.eq(other[, level, fill_value, axis])	Equal to of series and other, element-wise (binary operator eq).
Series.product([axis, skipna, level, …])	Return the product of the values for the requested axis
Series.dot(other)	Matrix multiplication with DataFrame or inner-product with Series objects.

Function application, GroupBy & Window

Series.apply(func[, convert_dtype, args])	Invoke function on values of Series.
Series.agg(func[, axis])	Aggregate using one or more operations over the specified axis.
Series.aggregate(func[, axis])	Aggregate using one or more operations over the specified axis.
Series.transform(func, *args, **kwargs)	Call function producing a like-indexed NDFrame and return a NDFrame with the transformed values
Series.map(arg[, na_action])	Map values of Series using input correspondence (a dict, Series, or function).
Series.groupby([by, axis, level, as_index, …])	Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.
Series.rolling(window[, min_periods, …])	Provides rolling window calculations.
Series.expanding([min_periods, center, axis])	Provides expanding transformations.
Series.ewm([com, span, halflife, alpha, …])	Provides exponential weighted functions
Series.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)

Computations / Descriptive Stats

Series.abs()	Return a Series/DataFrame with absolute numeric value of each element.
Series.all([axis, bool_only, skipna, level])	Return whether all elements are True, potentially over an axis.
Series.any([axis, bool_only, skipna, level])	Return whether any element is True over requested axis.
Series.autocorr([lag])	Lag-N autocorrelation
Series.between(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
Series.clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
Series.clip_lower(threshold[, axis, inplace])	Return copy of the input with values below a threshold truncated.
Series.clip_upper(threshold[, axis, inplace])	Return copy of input with values above given value(s) truncated.
Series.corr(other[, method, min_periods])	Compute correlation with other Series, excluding missing values
Series.count([level])	Return number of non-NA/null observations in the Series
Series.cov(other[, min_periods])	Compute covariance with Series, excluding missing values
Series.cummax([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
Series.cummin([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
Series.cumprod([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
Series.cumsum([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
Series.describe([percentiles, include, exclude])	Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
Series.diff([periods])	First discrete difference of element.
Series.factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable.
Series.kurt([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
Series.mad([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis
Series.max([axis, skipna, level, numeric_only])	This method returns the maximum of the values in the object.
Series.mean([axis, skipna, level, numeric_only])	Return the mean of the values for the requested axis
Series.median([axis, skipna, level, …])	Return the median of the values for the requested axis
Series.min([axis, skipna, level, numeric_only])	This method returns the minimum of the values in the object.
Series.mode()	Return the mode(s) of the dataset.
Series.nlargest([n, keep])	Return the largest n elements.
Series.nsmallest([n, keep])	Return the smallest n elements.
Series.pct_change([periods, fill_method, …])	Percentage change between the current and a prior element.
Series.prod([axis, skipna, level, …])	Return the product of the values for the requested axis
Series.quantile([q, interpolation])	Return value at the given quantile, a la numpy.percentile.
Series.rank([axis, method, numeric_only, …])	Compute numerical data ranks (1 through n) along axis.
Series.sem([axis, skipna, level, ddof, …])	Return unbiased standard error of the mean over requested axis.
Series.skew([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis Normalized by N-1
Series.std([axis, skipna, level, ddof, …])	Return sample standard deviation over requested axis.
Series.sum([axis, skipna, level, …])	Return the sum of the values for the requested axis
Series.var([axis, skipna, level, ddof, …])	Return unbiased variance over requested axis.
Series.kurtosis([axis, skipna, level, …])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
Series.unique()	Return unique values of Series object.
Series.nunique([dropna])	Return number of unique elements in the object.
Series.is_unique	Return boolean if values in the object are unique
Series.is_monotonic	Return boolean if values in the object are monotonic_increasing
Series.is_monotonic_increasing	Return boolean if values in the object are monotonic_increasing
Series.is_monotonic_decreasing	Return boolean if values in the object are monotonic_decreasing
Series.value_counts([normalize, sort, …])	Returns object containing counts of unique values.
Series.compound([axis, skipna, level])	Return the compound percentage of the values for the requested axis
Series.nonzero()	Return the integer indices of the elements that are non-zero
Series.ptp([axis, skipna, level, numeric_only])	Returns the difference between the maximum value and the

Reindexing / Selection / Label manipulation

Series.align(other[, join, axis, level, …])	Align two objects on their axes with the specified join method for each axis Index
Series.drop([labels, axis, index, columns, …])	Return Series with specified index labels removed.
Series.drop_duplicates([keep, inplace])	Return Series with duplicate values removed.
Series.duplicated([keep])	Indicate duplicate Series values.
Series.equals(other)	Determines if two NDFrame objects contain the same elements.
Series.first(offset)	Convenience method for subsetting initial periods of time series data based on a date offset.
Series.head([n])	Return the first n rows.
Series.idxmax([axis, skipna])	Return the row label of the maximum value.
Series.idxmin([axis, skipna])	Return the row label of the minimum value.
Series.isin(values)	Check whether values are contained in Series.
Series.last(offset)	Convenience method for subsetting final periods of time series data based on a date offset.
Series.reindex([index])	Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
Series.reindex_like(other[, method, copy, …])	Return an object with matching indices to myself.
Series.rename([index])	Alter Series index labels or name
Series.rename_axis(mapper[, axis, copy, inplace])	Alter the name of the index or columns.
Series.reset_index([level, drop, name, inplace])	Generate a new DataFrame or Series with the index reset.
Series.sample([n, frac, replace, weights, …])	Return a random sample of items from an axis of object.
Series.select(crit[, axis])	(DEPRECATED) Return data corresponding to axis labels matching criteria
Series.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
Series.take(indices[, axis, convert, is_copy])	Return the elements in the given positional indices along an axis.
Series.tail([n])	Return the last n rows.
Series.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
Series.where(cond[, other, inplace, axis, …])	Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
Series.mask(cond[, other, inplace, axis, …])	Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
Series.add_prefix(prefix)	Prefix labels with string prefix.
Series.add_suffix(suffix)	Suffix labels with string suffix.
Series.filter([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.

Missing data handling

Series.isna()	Detect missing values.
Series.notna()	Detect existing (non-missing) values.
Series.dropna([axis, inplace])	Return a new Series with missing values removed.
Series.fillna([value, method, axis, …])	Fill NA/NaN values using the specified method
Series.interpolate([method, axis, limit, …])	Interpolate values according to different methods.

Reshaping, sorting

Series.argsort([axis, kind, order])	Overrides ndarray.argsort.
Series.argmin([axis, skipna])	(DEPRECATED) .. deprecated:: 0.21.0
Series.argmax([axis, skipna])	(DEPRECATED) .. deprecated:: 0.21.0
Series.reorder_levels(order)	Rearrange index levels using input order.
Series.sort_values([axis, ascending, …])	Sort by the values.
Series.sort_index([axis, level, ascending, …])	Sort Series by index labels.
Series.swaplevel([i, j, copy])	Swap levels i and j in a MultiIndex
Series.unstack([level, fill_value])	Unstack, a.k.a.
Series.searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
Series.ravel([order])	Return the flattened underlying data as an ndarray
Series.repeat(repeats, *args, **kwargs)	Repeat elements of an Series.
Series.squeeze([axis])	Squeeze length 1 dimensions.
Series.view([dtype])	Create a new view of the Series.
Series.sortlevel([level, ascending, …])	(DEPRECATED) Sort Series with MultiIndex by chosen level.

Combining / joining / merging

Series.append(to_append[, ignore_index, …])	Concatenate two or more Series.
Series.replace([to_replace, value, inplace, …])	Replace values given in to_replace with value.
Series.update(other)	Modify Series in place using non-NA values from passed Series.

Time series-related

Series.asfreq(freq[, method, how, …])	Convert TimeSeries to specified frequency.
Series.asof(where[, subset])	The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame)
Series.shift([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
Series.first_valid_index()	Return index for first non-NA/null value.
Series.last_valid_index()	Return index for last non-NA/null value.
Series.resample(rule[, how, axis, …])	Convenience method for frequency conversion and resampling of time series.
Series.tz_convert(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
Series.tz_localize(tz[, axis, level, copy, …])	Localize tz-naive TimeSeries to target time zone.
Series.at_time(time[, asof])	Select values at particular time of day (e.g.
Series.between_time(start_time, end_time[, …])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
Series.tshift([periods, freq, axis])	Shift the time index, using the index’s frequency if available.
Series.slice_shift([periods, axis])	Equivalent to shift without copying data.

Datetimelike Properties

Series.dt can be used to access the values of the series as datetimelike and return several properties. These can be accessed like Series.dt..

Datetime Properties

Series.dt.date	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
Series.dt.time	Returns numpy array of datetime.time.
Series.dt.year	The year of the datetime
Series.dt.month	The month as January=1, December=12
Series.dt.day	The days of the datetime
Series.dt.hour	The hours of the datetime
Series.dt.minute	The minutes of the datetime
Series.dt.second	The seconds of the datetime
Series.dt.microsecond	The microseconds of the datetime
Series.dt.nanosecond	The nanoseconds of the datetime
Series.dt.week	The week ordinal of the year
Series.dt.weekofyear	The week ordinal of the year
Series.dt.dayofweek	The day of the week with Monday=0, Sunday=6
Series.dt.weekday	The day of the week with Monday=0, Sunday=6
Series.dt.dayofyear	The ordinal day of the year
Series.dt.quarter	The quarter of the date
Series.dt.is_month_start	Logical indicating if first day of month (defined by frequency)
Series.dt.is_month_end	Indicator for whether the date is the last day of the month.
Series.dt.is_quarter_start	Indicator for whether the date is the first day of a quarter.
Series.dt.is_quarter_end	Indicator for whether the date is the last day of a quarter.
Series.dt.is_year_start	Indicate whether the date is the first day of a year.
Series.dt.is_year_end	Indicate whether the date is the last day of the year.
Series.dt.is_leap_year	Boolean indicator if the date belongs to a leap year.
Series.dt.daysinmonth	The number of days in the month
Series.dt.days_in_month	The number of days in the month
Series.dt.tz
Series.dt.freq

Datetime Methods

Series.dt.to_period(*args, **kwargs)	Cast to PeriodIndex at a particular frequency.
Series.dt.to_pydatetime()	Return the data as an array of native Python datetime objects
Series.dt.tz_localize(*args, **kwargs)	Localize tz-naive DatetimeIndex to tz-aware DatetimeIndex.
Series.dt.tz_convert(*args, **kwargs)	Convert tz-aware DatetimeIndex from one time zone to another.
Series.dt.normalize(*args, **kwargs)	Convert times to midnight.
Series.dt.strftime(*args, **kwargs)	Convert to Index using specified date_format.
Series.dt.round(*args, **kwargs)	round the data to the specified freq.
Series.dt.floor(*args, **kwargs)	floor the data to the specified freq.
Series.dt.ceil(*args, **kwargs)	ceil the data to the specified freq.
Series.dt.month_name(*args, **kwargs)	Return the month names of the DateTimeIndex with specified locale.
Series.dt.day_name(*args, **kwargs)	Return the day names of the DateTimeIndex with specified locale.

Timedelta Properties

Series.dt.days	Number of days for each element.
Series.dt.seconds	Number of seconds (>= 0 and less than 1 day) for each element.
Series.dt.microseconds	Number of microseconds (>= 0 and less than 1 second) for each element.
Series.dt.nanoseconds	Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
Series.dt.components	Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.

Timedelta Methods

Series.dt.to_pytimedelta()	Return an array of native datetime.timedelta objects.
Series.dt.total_seconds(*args, **kwargs)	Return total duration of each element expressed in seconds.

String handling

Series.str can be used to access the values of the series as strings and apply several methods to it. These can be accessed like Series.str..

Series.str.capitalize()	Convert strings in the Series/Index to be capitalized.
Series.str.cat([others, sep, na_rep, join])	Concatenate strings in the Series/Index with given separator.
Series.str.center(width[, fillchar])	Filling left and right side of strings in the Series/Index with an additional character.
Series.str.contains(pat[, case, flags, na, …])	Test if pattern or regex is contained within a string of a Series or Index.
Series.str.count(pat[, flags])	Count occurrences of pattern in each string of the Series/Index.
Series.str.decode(encoding[, errors])	Decode character string in the Series/Index using indicated encoding.
Series.str.encode(encoding[, errors])	Encode character string in the Series/Index using indicated encoding.
Series.str.endswith(pat[, na])	Test if the end of each string element matches a pattern.
Series.str.extract(pat[, flags, expand])	For each subject string in the Series, extract groups from the first match of regular expression pat.
Series.str.extractall(pat[, flags])	For each subject string in the Series, extract groups from all matches of regular expression pat.
Series.str.find(sub[, start, end])	Return lowest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].
Series.str.findall(pat[, flags])	Find all occurrences of pattern or regular expression in the Series/Index.
Series.str.get(i)	Extract element from each component at specified position.
Series.str.index(sub[, start, end])	Return lowest indexes in each strings where the substring is fully contained between [start:end].
Series.str.join(sep)	Join lists contained as elements in the Series/Index with passed delimiter.
Series.str.len()	Compute length of each string in the Series/Index.
Series.str.ljust(width[, fillchar])	Filling right side of strings in the Series/Index with an additional character.
Series.str.lower()	Convert strings in the Series/Index to lowercase.
Series.str.lstrip([to_strip])	Strip whitespace (including newlines) from each string in the Series/Index from left side.
Series.str.match(pat[, case, flags, na, …])	Determine if each string matches a regular expression.
Series.str.normalize(form)	Return the Unicode normal form for the strings in the Series/Index.
Series.str.pad(width[, side, fillchar])	Pad strings in the Series/Index with an additional character to specified side.
Series.str.partition([pat, expand])	Split the string at the first occurrence of sep, and return 3 elements containing the part before the separator, the separator itself, and the part after the separator.
Series.str.repeat(repeats)	Duplicate each string in the Series/Index by indicated number of times.
Series.str.replace(pat, repl[, n, case, …])	Replace occurrences of pattern/regex in the Series/Index with some other string.
Series.str.rfind(sub[, start, end])	Return highest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].
Series.str.rindex(sub[, start, end])	Return highest indexes in each strings where the substring is fully contained between [start:end].
Series.str.rjust(width[, fillchar])	Filling left side of strings in the Series/Index with an additional character.
Series.str.rpartition([pat, expand])	Split the string at the last occurrence of sep, and return 3 elements containing the part before the separator, the separator itself, and the part after the separator.
Series.str.rstrip([to_strip])	Strip whitespace (including newlines) from each string in the Series/Index from right side.
Series.str.slice([start, stop, step])	Slice substrings from each element in the Series/Index
Series.str.slice_replace([start, stop, repl])	Replace a positional slice of a string with another value.
Series.str.split([pat, n, expand])	Split strings around given separator/delimiter.
Series.str.rsplit([pat, n, expand])	Split each string in the Series/Index by the given delimiter string, starting at the end of the string and working to the front.
Series.str.startswith(pat[, na])	Test if the start of each string element matches a pattern.
Series.str.strip([to_strip])	Strip whitespace (including newlines) from each string in the Series/Index from left and right sides.
Series.str.swapcase()	Convert strings in the Series/Index to be swapcased.
Series.str.title()	Convert strings in the Series/Index to titlecase.
Series.str.translate(table[, deletechars])	Map all characters in the string through the given mapping table.
Series.str.upper()	Convert strings in the Series/Index to uppercase.
Series.str.wrap(width, **kwargs)	Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width.
Series.str.zfill(width)	Filling left side of strings in the Series/Index with 0.
Series.str.isalnum()	Check whether all characters in each string in the Series/Index are alphanumeric.
Series.str.isalpha()	Check whether all characters in each string in the Series/Index are alphabetic.
Series.str.isdigit()	Check whether all characters in each string in the Series/Index are digits.
Series.str.isspace()	Check whether all characters in each string in the Series/Index are whitespace.
Series.str.islower()	Check whether all characters in each string in the Series/Index are lowercase.
Series.str.isupper()	Check whether all characters in each string in the Series/Index are uppercase.
Series.str.istitle()	Check whether all characters in each string in the Series/Index are titlecase.
Series.str.isnumeric()	Check whether all characters in each string in the Series/Index are numeric.
Series.str.isdecimal()	Check whether all characters in each string in the Series/Index are decimal.
Series.str.get_dummies([sep])	Split each string in the Series by sep and return a frame of dummy/indicator variables.

Categorical

Pandas defines a custom data type for representing data that can take only a limited, fixed set of values. The dtype of a Categorical can be described by a pandas.api.types.CategoricalDtype.

api.types.CategoricalDtype([categories, ordered])

Type for categorical data with the categories and orderedness

api.types.CategoricalDtype.categories	An Index containing the unique categories allowed.
api.types.CategoricalDtype.ordered	Whether the categories have an ordered relationship

Categorical data can be stored in a pandas.Categorical

Categorical(values[, categories, ordered, …])

Represents a categorical variable in classic R / S-plus fashion

The alternative Categorical.from_codes() constructor can be used when you have the categories and integer codes already:

Categorical.from_codes(codes, categories[, …])

Make a Categorical type from codes and categories arrays.

The dtype information is available on the Categorical

Categorical.dtype	The CategoricalDtype for this instance
Categorical.categories	The categories of this categorical.
Categorical.ordered	Whether the categories have an ordered relationship
Categorical.codes	The category codes of this categorical.

np.asarray(categorical) works by implementing the array interface. Be aware, that this converts the Categorical back to a NumPy array, so categories and order information is not preserved!

Categorical.__array__([dtype])

The numpy array interface.

A Categorical can be stored in a Series or DataFrame. To create a Series of dtype category, use cat = s.astype(dtype) or Series(..., dtype=dtype) where dtype is either

• the string 'category'
• an instance of CategoricalDtype.

If the Series is of dtype CategoricalDtype, Series.cat can be used to change the categorical data. This accessor is similar to the Series.dt or Series.str and has the following usable methods and properties:

Series.cat.categories	The categories of this categorical.
Series.cat.ordered	Whether the categories have an ordered relationship
Series.cat.codes

Series.cat.rename_categories(*args, **kwargs)	Renames categories.
Series.cat.reorder_categories(*args, **kwargs)	Reorders categories as specified in new_categories.
Series.cat.add_categories(*args, **kwargs)	Add new categories.
Series.cat.remove_categories(*args, **kwargs)	Removes the specified categories.
Series.cat.remove_unused_categories(*args, …)	Removes categories which are not used.
Series.cat.set_categories(*args, **kwargs)	Sets the categories to the specified new_categories.
Series.cat.as_ordered(*args, **kwargs)	Sets the Categorical to be ordered
Series.cat.as_unordered(*args, **kwargs)	Sets the Categorical to be unordered

Plotting

Series.plot is both a callable method and a namespace attribute for specific plotting methods of the form Series.plot..

Series.plot([kind, ax, figsize, ….])

Series plotting accessor and method

Series.plot.area(**kwds)	Area plot
Series.plot.bar(**kwds)	Vertical bar plot
Series.plot.barh(**kwds)	Horizontal bar plot
Series.plot.box(**kwds)	Boxplot
Series.plot.density([bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.hist([bins])	Histogram
Series.plot.kde([bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
Series.plot.line(**kwds)	Line plot
Series.plot.pie(**kwds)	Pie chart

Series.hist([by, ax, grid, xlabelsize, …])

Draw histogram of the input series using matplotlib

Serialization / IO / Conversion

Series.to_pickle(path[, compression, protocol])	Pickle (serialize) object to file.
Series.to_csv([path, index, sep, na_rep, …])	Write Series to a comma-separated values (csv) file
Series.to_dict([into])	Convert Series to {label -> value} dict or dict-like object.
Series.to_excel(excel_writer[, sheet_name, …])	Write Series to an excel sheet
Series.to_frame([name])	Convert Series to DataFrame
Series.to_xarray()	Return an xarray object from the pandas object.
Series.to_hdf(path_or_buf, key, **kwargs)	Write the contained data to an HDF5 file using HDFStore.
Series.to_sql(name, con[, schema, …])	Write records stored in a DataFrame to a SQL database.
Series.to_msgpack([path_or_buf, encoding])	msgpack (serialize) object to input file path
Series.to_json([path_or_buf, orient, …])	Convert the object to a JSON string.
Series.to_sparse([kind, fill_value])	Convert Series to SparseSeries
Series.to_dense()	Return dense representation of NDFrame (as opposed to sparse)
Series.to_string([buf, na_rep, …])	Render a string representation of the Series
Series.to_clipboard([excel, sep])	Copy object to the system clipboard.
Series.to_latex([buf, columns, col_space, …])	Render an object to a tabular environment table.

Sparse

SparseSeries.to_coo([row_levels, …])	Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex.
SparseSeries.from_coo(A[, dense_index])	Create a SparseSeries from a scipy.sparse.coo_matrix.

DataFrame

Constructor

DataFrame([data, index, columns, dtype, copy])

Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Attributes and underlying data

Axes

DataFrame.index	The index (row labels) of the DataFrame.
DataFrame.columns	The column labels of the DataFrame.

DataFrame.dtypes	Return the dtypes in the DataFrame.
DataFrame.ftypes	Return the ftypes (indication of sparse/dense and dtype) in DataFrame.
DataFrame.get_dtype_counts()	Return counts of unique dtypes in this object.
DataFrame.get_ftype_counts()	(DEPRECATED) Return counts of unique ftypes in this object.
DataFrame.select_dtypes([include, exclude])	Return a subset of the DataFrame’s columns based on the column dtypes.
DataFrame.values	Return a Numpy representation of the DataFrame.
DataFrame.get_values()	Return an ndarray after converting sparse values to dense.
DataFrame.axes	Return a list representing the axes of the DataFrame.
DataFrame.ndim	Return an int representing the number of axes / array dimensions.
DataFrame.size	Return an int representing the number of elements in this object.
DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
DataFrame.memory_usage([index, deep])	Return the memory usage of each column in bytes.
DataFrame.empty	Indicator whether DataFrame is empty.
DataFrame.is_copy

Conversion

DataFrame.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
DataFrame.convert_objects([convert_dates, …])	(DEPRECATED) Attempt to infer better dtype for object columns.
DataFrame.infer_objects()	Attempt to infer better dtypes for object columns.
DataFrame.copy([deep])	Make a copy of this object’s indices and data.
DataFrame.isna()	Detect missing values.
DataFrame.notna()	Detect existing (non-missing) values.
DataFrame.bool()	Return the bool of a single element PandasObject.

Indexing, iteration

DataFrame.head([n])	Return the first n rows.
DataFrame.at	Access a single value for a row/column label pair.
DataFrame.iat	Access a single value for a row/column pair by integer position.
DataFrame.loc	Access a group of rows and columns by label(s) or a boolean array.
DataFrame.iloc	Purely integer-location based indexing for selection by position.
DataFrame.insert(loc, column, value[, …])	Insert column into DataFrame at specified location.
DataFrame.insert(loc, column, value[, …])	Insert column into DataFrame at specified location.
DataFrame.__iter__()	Iterate over infor axis
DataFrame.items()	Iterator over (column name, Series) pairs.
DataFrame.keys()	Get the ‘info axis’ (see Indexing for more)
DataFrame.iteritems()	Iterator over (column name, Series) pairs.
DataFrame.iterrows()	Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.itertuples([index, name])	Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.
DataFrame.lookup(row_labels, col_labels)	Label-based “fancy indexing” function for DataFrame.
DataFrame.pop(item)	Return item and drop from frame.
DataFrame.tail([n])	Return the last n rows.
DataFrame.xs(key[, axis, level, drop_level])	Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
DataFrame.get(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
DataFrame.isin(values)	Return boolean DataFrame showing whether each element in the DataFrame is contained in values.
DataFrame.where(cond[, other, inplace, …])	Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
DataFrame.mask(cond[, other, inplace, axis, …])	Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
DataFrame.query(expr[, inplace])	Query the columns of a frame with a boolean expression.

For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.

Binary operator functions

DataFrame.add(other[, axis, level, fill_value])	Addition of dataframe and other, element-wise (binary operator add).
DataFrame.sub(other[, axis, level, fill_value])	Subtraction of dataframe and other, element-wise (binary operator sub).
DataFrame.mul(other[, axis, level, fill_value])	Multiplication of dataframe and other, element-wise (binary operator mul).
DataFrame.div(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator truediv).
DataFrame.truediv(other[, axis, level, …])	Floating division of dataframe and other, element-wise (binary operator truediv).
DataFrame.floordiv(other[, axis, level, …])	Integer division of dataframe and other, element-wise (binary operator floordiv).
DataFrame.mod(other[, axis, level, fill_value])	Modulo of dataframe and other, element-wise (binary operator mod).
DataFrame.pow(other[, axis, level, fill_value])	Exponential power of dataframe and other, element-wise (binary operator pow).
DataFrame.dot(other)	Matrix multiplication with DataFrame or Series objects.
DataFrame.radd(other[, axis, level, fill_value])	Addition of dataframe and other, element-wise (binary operator radd).
DataFrame.rsub(other[, axis, level, fill_value])	Subtraction of dataframe and other, element-wise (binary operator rsub).
DataFrame.rmul(other[, axis, level, fill_value])	Multiplication of dataframe and other, element-wise (binary operator rmul).
DataFrame.rdiv(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator rtruediv).
DataFrame.rtruediv(other[, axis, level, …])	Floating division of dataframe and other, element-wise (binary operator rtruediv).
DataFrame.rfloordiv(other[, axis, level, …])	Integer division of dataframe and other, element-wise (binary operator rfloordiv).
DataFrame.rmod(other[, axis, level, fill_value])	Modulo of dataframe and other, element-wise (binary operator rmod).
DataFrame.rpow(other[, axis, level, fill_value])	Exponential power of dataframe and other, element-wise (binary operator rpow).
DataFrame.lt(other[, axis, level])	Wrapper for flexible comparison methods lt
DataFrame.gt(other[, axis, level])	Wrapper for flexible comparison methods gt
DataFrame.le(other[, axis, level])	Wrapper for flexible comparison methods le
DataFrame.ge(other[, axis, level])	Wrapper for flexible comparison methods ge
DataFrame.ne(other[, axis, level])	Wrapper for flexible comparison methods ne
DataFrame.eq(other[, axis, level])	Wrapper for flexible comparison methods eq
DataFrame.combine(other, func[, fill_value, …])	Add two DataFrame objects and do not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame’s value (which might be NaN as well)
DataFrame.combine_first(other)	Combine two DataFrame objects and default to non-null values in frame calling the method.

Function application, GroupBy & Window

DataFrame.apply(func[, axis, broadcast, …])	Apply a function along an axis of the DataFrame.
DataFrame.applymap(func)	Apply a function to a Dataframe elementwise.
DataFrame.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
DataFrame.agg(func[, axis])	Aggregate using one or more operations over the specified axis.
DataFrame.aggregate(func[, axis])	Aggregate using one or more operations over the specified axis.
DataFrame.transform(func, *args, **kwargs)	Call function producing a like-indexed NDFrame and return a NDFrame with the transformed values
DataFrame.groupby([by, axis, level, …])	Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.
DataFrame.rolling(window[, min_periods, …])	Provides rolling window calculations.
DataFrame.expanding([min_periods, center, axis])	Provides expanding transformations.
DataFrame.ewm([com, span, halflife, alpha, …])	Provides exponential weighted functions

Computations / Descriptive Stats

DataFrame.abs()	Return a Series/DataFrame with absolute numeric value of each element.
DataFrame.all([axis, bool_only, skipna, level])	Return whether all elements are True, potentially over an axis.
DataFrame.any([axis, bool_only, skipna, level])	Return whether any element is True over requested axis.
DataFrame.clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
DataFrame.clip_lower(threshold[, axis, inplace])	Return copy of the input with values below a threshold truncated.
DataFrame.clip_upper(threshold[, axis, inplace])	Return copy of input with values above given value(s) truncated.
DataFrame.compound([axis, skipna, level])	Return the compound percentage of the values for the requested axis
DataFrame.corr([method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values
DataFrame.corrwith(other[, axis, drop])	Compute pairwise correlation between rows or columns of two DataFrame objects.
DataFrame.count([axis, level, numeric_only])	Count non-NA cells for each column or row.
DataFrame.cov([min_periods])	Compute pairwise covariance of columns, excluding NA/null values.
DataFrame.cummax([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
DataFrame.cummin([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
DataFrame.cumprod([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
DataFrame.cumsum([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
DataFrame.describe([percentiles, include, …])	Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
DataFrame.diff([periods, axis])	First discrete difference of element.
DataFrame.eval(expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
DataFrame.kurt([axis, skipna, level, …])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
DataFrame.kurtosis([axis, skipna, level, …])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
DataFrame.mad([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis
DataFrame.max([axis, skipna, level, …])	This method returns the maximum of the values in the object.
DataFrame.mean([axis, skipna, level, …])	Return the mean of the values for the requested axis
DataFrame.median([axis, skipna, level, …])	Return the median of the values for the requested axis
DataFrame.min([axis, skipna, level, …])	This method returns the minimum of the values in the object.
DataFrame.mode([axis, numeric_only])	Gets the mode(s) of each element along the axis selected.
DataFrame.pct_change([periods, fill_method, …])	Percentage change between the current and a prior element.
DataFrame.prod([axis, skipna, level, …])	Return the product of the values for the requested axis
DataFrame.product([axis, skipna, level, …])	Return the product of the values for the requested axis
DataFrame.quantile([q, axis, numeric_only, …])	Return values at the given quantile over requested axis, a la numpy.percentile.
DataFrame.rank([axis, method, numeric_only, …])	Compute numerical data ranks (1 through n) along axis.
DataFrame.round([decimals])	Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, …])	Return unbiased standard error of the mean over requested axis.
DataFrame.skew([axis, skipna, level, …])	Return unbiased skew over requested axis Normalized by N-1
DataFrame.sum([axis, skipna, level, …])	Return the sum of the values for the requested axis
DataFrame.std([axis, skipna, level, ddof, …])	Return sample standard deviation over requested axis.
DataFrame.var([axis, skipna, level, ddof, …])	Return unbiased variance over requested axis.
DataFrame.nunique([axis, dropna])	Return Series with number of distinct observations over requested axis.

Reindexing / Selection / Label manipulation

DataFrame.add_prefix(prefix)	Prefix labels with string prefix.
DataFrame.add_suffix(suffix)	Suffix labels with string suffix.
DataFrame.align(other[, join, axis, level, …])	Align two objects on their axes with the specified join method for each axis Index
DataFrame.at_time(time[, asof])	Select values at particular time of day (e.g.
DataFrame.between_time(start_time, end_time)	Select values between particular times of the day (e.g., 9:00-9:30 AM).
DataFrame.drop([labels, axis, index, …])	Drop specified labels from rows or columns.
DataFrame.drop_duplicates([subset, keep, …])	Return DataFrame with duplicate rows removed, optionally only considering certain columns
DataFrame.duplicated([subset, keep])	Return boolean Series denoting duplicate rows, optionally only considering certain columns
DataFrame.equals(other)	Determines if two NDFrame objects contain the same elements.
DataFrame.filter([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.
DataFrame.first(offset)	Convenience method for subsetting initial periods of time series data based on a date offset.
DataFrame.head([n])	Return the first n rows.
DataFrame.idxmax([axis, skipna])	Return index of first occurrence of maximum over requested axis.
DataFrame.idxmin([axis, skipna])	Return index of first occurrence of minimum over requested axis.
DataFrame.last(offset)	Convenience method for subsetting final periods of time series data based on a date offset.
DataFrame.reindex([labels, index, columns, …])	Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
DataFrame.reindex_axis(labels[, axis, …])	Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
DataFrame.reindex_like(other[, method, …])	Return an object with matching indices to myself.
DataFrame.rename([mapper, index, columns, …])	Alter axes labels.
DataFrame.rename_axis(mapper[, axis, copy, …])	Alter the name of the index or columns.
DataFrame.reset_index([level, drop, …])	For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc.
DataFrame.sample([n, frac, replace, …])	Return a random sample of items from an axis of object.
DataFrame.select(crit[, axis])	(DEPRECATED) Return data corresponding to axis labels matching criteria
DataFrame.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
DataFrame.set_index(keys[, drop, append, …])	Set the DataFrame index (row labels) using one or more existing columns.
DataFrame.tail([n])	Return the last n rows.
DataFrame.take(indices[, axis, convert, is_copy])	Return the elements in the given positional indices along an axis.
DataFrame.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.

Missing data handling

DataFrame.dropna([axis, how, thresh, …])	Remove missing values.
DataFrame.fillna([value, method, axis, …])	Fill NA/NaN values using the specified method
DataFrame.replace([to_replace, value, …])	Replace values given in to_replace with value.
DataFrame.interpolate([method, axis, limit, …])	Interpolate values according to different methods.

Reshaping, sorting, transposing

DataFrame.pivot([index, columns, values])	Return reshaped DataFrame organized by given index / column values.
DataFrame.pivot_table([values, index, …])	Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.reorder_levels(order[, axis])	Rearrange index levels using input order.
DataFrame.sort_values(by[, axis, ascending, …])	Sort by the values along either axis
DataFrame.sort_index([axis, level, …])	Sort object by labels (along an axis)
DataFrame.nlargest(n, columns[, keep])	Return the first n rows ordered by columns in descending order.
DataFrame.nsmallest(n, columns[, keep])	Get the rows of a DataFrame sorted by the n smallest values of columns.
DataFrame.swaplevel([i, j, axis])	Swap levels i and j in a MultiIndex on a particular axis
DataFrame.stack([level, dropna])	Stack the prescribed level(s) from columns to index.
DataFrame.unstack([level, fill_value])	Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.
DataFrame.swapaxes(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately
DataFrame.melt([id_vars, value_vars, …])	“Unpivots” a DataFrame from wide format to long format, optionally leaving identifier variables set.
DataFrame.squeeze([axis])	Squeeze length 1 dimensions.
DataFrame.to_panel()	(DEPRECATED) Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
DataFrame.to_xarray()	Return an xarray object from the pandas object.
DataFrame.T	Transpose index and columns.
DataFrame.transpose(*args, **kwargs)	Transpose index and columns.

Combining / joining / merging

DataFrame.append(other[, ignore_index, …])	Append rows of other to the end of this frame, returning a new object.
DataFrame.assign(**kwargs)	Assign new columns to a DataFrame, returning a new object (a copy) with the new columns added to the original ones.
DataFrame.join(other[, on, how, lsuffix, …])	Join columns with other DataFrame either on index or on a key column.
DataFrame.merge(right[, how, on, left_on, …])	Merge DataFrame objects by performing a database-style join operation by columns or indexes.
DataFrame.update(other[, join, overwrite, …])	Modify in place using non-NA values from another DataFrame.

Time series-related

DataFrame.asfreq(freq[, method, how, …])	Convert TimeSeries to specified frequency.
DataFrame.asof(where[, subset])	The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame)
DataFrame.shift([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
DataFrame.slice_shift([periods, axis])	Equivalent to shift without copying data.
DataFrame.tshift([periods, freq, axis])	Shift the time index, using the index’s frequency if available.
DataFrame.first_valid_index()	Return index for first non-NA/null value.
DataFrame.last_valid_index()	Return index for last non-NA/null value.
DataFrame.resample(rule[, how, axis, …])	Convenience method for frequency conversion and resampling of time series.
DataFrame.to_period([freq, axis, copy])	Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed)
DataFrame.to_timestamp([freq, how, axis, copy])	Cast to DatetimeIndex of timestamps, at beginning of period
DataFrame.tz_convert(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
DataFrame.tz_localize(tz[, axis, level, …])	Localize tz-naive TimeSeries to target time zone.

Plotting

DataFrame.plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame.plot..

DataFrame.plot([x, y, kind, ax, ….])

DataFrame plotting accessor and method

DataFrame.plot.area([x, y])	Area plot
DataFrame.plot.bar([x, y])	Vertical bar plot.
DataFrame.plot.barh([x, y])	Make a horizontal bar plot.
DataFrame.plot.box([by])	Make a box plot of the DataFrame columns.
DataFrame.plot.density([bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
DataFrame.plot.hexbin(x, y[, C, …])	Generate a hexagonal binning plot.
DataFrame.plot.hist([by, bins])	Draw one histogram of the DataFrame’s columns.
DataFrame.plot.kde([bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
DataFrame.plot.line([x, y])	Plot DataFrame columns as lines.
DataFrame.plot.pie([y])	Generate a pie plot.
DataFrame.plot.scatter(x, y[, s, c])	Create a scatter plot with varying marker point size and color.

DataFrame.boxplot([column, by, ax, …])	Make a box plot from DataFrame columns.
DataFrame.hist([column, by, grid, …])	Make a histogram of the DataFrame’s.

Serialization / IO / Conversion

DataFrame.from_csv(path[, header, sep, …])	(DEPRECATED) Read CSV file.
DataFrame.from_dict(data[, orient, dtype, …])	Construct DataFrame from dict of array-like or dicts.
DataFrame.from_items(items[, columns, orient])	(DEPRECATED) Construct a dataframe from a list of tuples
DataFrame.from_records(data[, index, …])	Convert structured or record ndarray to DataFrame
DataFrame.info([verbose, buf, max_cols, …])	Print a concise summary of a DataFrame.
DataFrame.to_parquet(fname[, engine, …])	Write a DataFrame to the binary parquet format.
DataFrame.to_pickle(path[, compression, …])	Pickle (serialize) object to file.
DataFrame.to_csv([path_or_buf, sep, na_rep, …])	Write DataFrame to a comma-separated values (csv) file
DataFrame.to_hdf(path_or_buf, key, **kwargs)	Write the contained data to an HDF5 file using HDFStore.
DataFrame.to_sql(name, con[, schema, …])	Write records stored in a DataFrame to a SQL database.
DataFrame.to_dict([orient, into])	Convert the DataFrame to a dictionary.
DataFrame.to_excel(excel_writer[, …])	Write DataFrame to an excel sheet
DataFrame.to_json([path_or_buf, orient, …])	Convert the object to a JSON string.
DataFrame.to_html([buf, columns, col_space, …])	Render a DataFrame as an HTML table.
DataFrame.to_feather(fname)	write out the binary feather-format for DataFrames
DataFrame.to_latex([buf, columns, …])	Render an object to a tabular environment table.
DataFrame.to_stata(fname[, convert_dates, …])	Export Stata binary dta files.
DataFrame.to_msgpack([path_or_buf, encoding])	msgpack (serialize) object to input file path
DataFrame.to_gbq(destination_table, project_id)	Write a DataFrame to a Google BigQuery table.
DataFrame.to_records([index, convert_datetime64])	Convert DataFrame to a NumPy record array.
DataFrame.to_sparse([fill_value, kind])	Convert to SparseDataFrame
DataFrame.to_dense()	Return dense representation of NDFrame (as opposed to sparse)
DataFrame.to_string([buf, columns, …])	Render a DataFrame to a console-friendly tabular output.
DataFrame.to_clipboard([excel, sep])	Copy object to the system clipboard.
DataFrame.style	Property returning a Styler object containing methods for building a styled HTML representation fo the DataFrame.

Sparse

SparseDataFrame.to_coo()

Return the contents of the frame as a sparse SciPy COO matrix.

Panel

Constructor

Panel([data, items, major_axis, minor_axis, …])

(DEPRECATED) Represents wide format panel data, stored as 3-dimensional array

Attributes and underlying data

Axes

• items: axis 0; each item corresponds to a DataFrame contained inside
• major_axis: axis 1; the index (rows) of each of the DataFrames
• minor_axis: axis 2; the columns of each of the DataFrames

Panel.values	Return a Numpy representation of the DataFrame.
Panel.axes	Return index label(s) of the internal NDFrame
Panel.ndim	Return an int representing the number of axes / array dimensions.
Panel.size	Return an int representing the number of elements in this object.
Panel.shape	Return a tuple of axis dimensions
Panel.dtypes	Return the dtypes in the DataFrame.
Panel.ftypes	Return the ftypes (indication of sparse/dense and dtype) in DataFrame.
Panel.get_dtype_counts()	Return counts of unique dtypes in this object.
Panel.get_ftype_counts()	(DEPRECATED) Return counts of unique ftypes in this object.

Conversion

Panel.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
Panel.copy([deep])	Make a copy of this object’s indices and data.
Panel.isna()	Detect missing values.
Panel.notna()	Detect existing (non-missing) values.

Getting and setting

Panel.get_value(*args, **kwargs)	(DEPRECATED) Quickly retrieve single value at (item, major, minor) location
Panel.set_value(*args, **kwargs)	(DEPRECATED) Quickly set single value at (item, major, minor) location

Indexing, iteration, slicing

Panel.at	Access a single value for a row/column label pair.
Panel.iat	Access a single value for a row/column pair by integer position.
Panel.loc	Access a group of rows and columns by label(s) or a boolean array.
Panel.iloc	Purely integer-location based indexing for selection by position.
Panel.__iter__()	Iterate over infor axis
Panel.iteritems()	Iterate over (label, values) on info axis
Panel.pop(item)	Return item and drop from frame.
Panel.xs(key[, axis])	Return slice of panel along selected axis
Panel.major_xs(key)	Return slice of panel along major axis
Panel.minor_xs(key)	Return slice of panel along minor axis

For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.

Binary operator functions

Panel.add(other[, axis])	Addition of series and other, element-wise (binary operator add).
Panel.sub(other[, axis])	Subtraction of series and other, element-wise (binary operator sub).
Panel.mul(other[, axis])	Multiplication of series and other, element-wise (binary operator mul).
Panel.div(other[, axis])	Floating division of series and other, element-wise (binary operator truediv).
Panel.truediv(other[, axis])	Floating division of series and other, element-wise (binary operator truediv).
Panel.floordiv(other[, axis])	Integer division of series and other, element-wise (binary operator floordiv).
Panel.mod(other[, axis])	Modulo of series and other, element-wise (binary operator mod).
Panel.pow(other[, axis])	Exponential power of series and other, element-wise (binary operator pow).
Panel.radd(other[, axis])	Addition of series and other, element-wise (binary operator radd).
Panel.rsub(other[, axis])	Subtraction of series and other, element-wise (binary operator rsub).
Panel.rmul(other[, axis])	Multiplication of series and other, element-wise (binary operator rmul).
Panel.rdiv(other[, axis])	Floating division of series and other, element-wise (binary operator rtruediv).
Panel.rtruediv(other[, axis])	Floating division of series and other, element-wise (binary operator rtruediv).
Panel.rfloordiv(other[, axis])	Integer division of series and other, element-wise (binary operator rfloordiv).
Panel.rmod(other[, axis])	Modulo of series and other, element-wise (binary operator rmod).
Panel.rpow(other[, axis])	Exponential power of series and other, element-wise (binary operator rpow).
Panel.lt(other[, axis])	Wrapper for comparison method lt
Panel.gt(other[, axis])	Wrapper for comparison method gt
Panel.le(other[, axis])	Wrapper for comparison method le
Panel.ge(other[, axis])	Wrapper for comparison method ge
Panel.ne(other[, axis])	Wrapper for comparison method ne
Panel.eq(other[, axis])	Wrapper for comparison method eq

Function application, GroupBy

Panel.apply(func[, axis])	Applies function along axis (or axes) of the Panel
Panel.groupby(function[, axis])	Group data on given axis, returning GroupBy object

Computations / Descriptive Stats

Panel.abs()	Return a Series/DataFrame with absolute numeric value of each element.
Panel.clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
Panel.clip_lower(threshold[, axis, inplace])	Return copy of the input with values below a threshold truncated.
Panel.clip_upper(threshold[, axis, inplace])	Return copy of input with values above given value(s) truncated.
Panel.count([axis])	Return number of observations over requested axis.
Panel.cummax([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
Panel.cummin([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
Panel.cumprod([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
Panel.cumsum([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
Panel.max([axis, skipna, level, numeric_only])	This method returns the maximum of the values in the object.
Panel.mean([axis, skipna, level, numeric_only])	Return the mean of the values for the requested axis
Panel.median([axis, skipna, level, numeric_only])	Return the median of the values for the requested axis
Panel.min([axis, skipna, level, numeric_only])	This method returns the minimum of the values in the object.
Panel.pct_change([periods, fill_method, …])	Percentage change between the current and a prior element.
Panel.prod([axis, skipna, level, …])	Return the product of the values for the requested axis
Panel.sem([axis, skipna, level, ddof, …])	Return unbiased standard error of the mean over requested axis.
Panel.skew([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis Normalized by N-1
Panel.sum([axis, skipna, level, …])	Return the sum of the values for the requested axis
Panel.std([axis, skipna, level, ddof, …])	Return sample standard deviation over requested axis.
Panel.var([axis, skipna, level, ddof, …])	Return unbiased variance over requested axis.

Reindexing / Selection / Label manipulation

Panel.add_prefix(prefix)	Prefix labels with string prefix.
Panel.add_suffix(suffix)	Suffix labels with string suffix.
Panel.drop([labels, axis, index, columns, …])
Panel.equals(other)	Determines if two NDFrame objects contain the same elements.
Panel.filter([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.
Panel.first(offset)	Convenience method for subsetting initial periods of time series data based on a date offset.
Panel.last(offset)	Convenience method for subsetting final periods of time series data based on a date offset.
Panel.reindex(*args, **kwargs)	Conform Panel to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
Panel.reindex_axis(labels[, axis, method, …])	Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
Panel.reindex_like(other[, method, copy, …])	Return an object with matching indices to myself.
Panel.rename([items, major_axis, minor_axis])	Alter axes input function or functions.
Panel.sample([n, frac, replace, weights, …])	Return a random sample of items from an axis of object.
Panel.select(crit[, axis])	(DEPRECATED) Return data corresponding to axis labels matching criteria
Panel.take(indices[, axis, convert, is_copy])	Return the elements in the given positional indices along an axis.
Panel.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.

Missing data handling

Panel.dropna([axis, how, inplace])

Drop 2D from panel, holding passed axis constant

Reshaping, sorting, transposing

Panel.sort_index([axis, level, ascending, …])	Sort object by labels (along an axis)
Panel.swaplevel([i, j, axis])	Swap levels i and j in a MultiIndex on a particular axis
Panel.transpose(*args, **kwargs)	Permute the dimensions of the Panel
Panel.swapaxes(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately
Panel.conform(frame[, axis])	Conform input DataFrame to align with chosen axis pair.

Combining / joining / merging

Panel.join(other[, how, lsuffix, rsuffix])	Join items with other Panel either on major and minor axes column
Panel.update(other[, join, overwrite, …])	Modify Panel in place using non-NA values from passed Panel, or object coercible to Panel.

Time series-related

Panel.asfreq(freq[, method, how, normalize, …])	Convert TimeSeries to specified frequency.
Panel.shift([periods, freq, axis])	Shift index by desired number of periods with an optional time freq.
Panel.resample(rule[, how, axis, …])	Convenience method for frequency conversion and resampling of time series.
Panel.tz_convert(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
Panel.tz_localize(tz[, axis, level, copy, …])	Localize tz-naive TimeSeries to target time zone.

Serialization / IO / Conversion

Panel.from_dict(data[, intersect, orient, dtype])	Construct Panel from dict of DataFrame objects
Panel.to_pickle(path[, compression, protocol])	Pickle (serialize) object to file.
Panel.to_excel(path[, na_rep, engine])	Write each DataFrame in Panel to a separate excel sheet
Panel.to_hdf(path_or_buf, key, **kwargs)	Write the contained data to an HDF5 file using HDFStore.
Panel.to_sparse(*args, **kwargs)	NOT IMPLEMENTED: do not call this method, as sparsifying is not supported for Panel objects and will raise an error.
Panel.to_frame([filter_observations])	Transform wide format into long (stacked) format as DataFrame whose columns are the Panel’s items and whose index is a MultiIndex formed of the Panel’s major and minor axes.
Panel.to_clipboard([excel, sep])	Copy object to the system clipboard.

Index

Many of these methods or variants thereof are available on the objects that contain an index (Series/DataFrame) and those should most likely be used before calling these methods directly.

Index

Immutable ndarray implementing an ordered, sliceable set.

Attributes

Index.values	return the underlying data as an ndarray
Index.is_monotonic	alias for is_monotonic_increasing (deprecated)
Index.is_monotonic_increasing	return if the index is monotonic increasing (only equal or increasing) values.
Index.is_monotonic_decreasing	return if the index is monotonic decreasing (only equal or decreasing) values.
Index.is_unique	return if the index has unique values
Index.has_duplicates
Index.hasnans	return if I have any nans; enables various perf speedups
Index.dtype	return the dtype object of the underlying data
Index.dtype_str	return the dtype str of the underlying data
Index.inferred_type	return a string of the type inferred from the values
Index.is_all_dates
Index.shape	return a tuple of the shape of the underlying data
Index.name
Index.names
Index.nbytes	return the number of bytes in the underlying data
Index.ndim	return the number of dimensions of the underlying data, by definition 1
Index.size	return the number of elements in the underlying data
Index.empty
Index.strides	return the strides of the underlying data
Index.itemsize	return the size of the dtype of the item of the underlying data
Index.base	return the base object if the memory of the underlying data is shared
Index.T	return the transpose, which is by definition self
Index.memory_usage([deep])	Memory usage of the values

Modifying and Computations

Index.all(*args, **kwargs)	Return whether all elements are True.
Index.any(*args, **kwargs)	Return whether any element is True.
Index.argmin([axis])	return a ndarray of the minimum argument indexer
Index.argmax([axis])	return a ndarray of the maximum argument indexer
Index.copy([name, deep, dtype])	Make a copy of this object.
Index.delete(loc)	Make new Index with passed location(-s) deleted
Index.drop(labels[, errors])	Make new Index with passed list of labels deleted
Index.drop_duplicates([keep])	Return Index with duplicate values removed.
Index.duplicated([keep])	Indicate duplicate index values.
Index.equals(other)	Determines if two Index objects contain the same elements.
Index.factorize([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable.
Index.identical(other)	Similar to equals, but check that other comparable attributes are also equal
Index.insert(loc, item)	Make new Index inserting new item at location.
Index.is_(other)	More flexible, faster check like is but that works through views
Index.is_boolean()
Index.is_categorical()	Check if the Index holds categorical data.
Index.is_floating()
Index.is_integer()
Index.is_interval()
Index.is_lexsorted_for_tuple(tup)
Index.is_mixed()
Index.is_numeric()
Index.is_object()
Index.min()	Return the minimum value of the Index.
Index.max()	Return the maximum value of the Index.
Index.reindex(target[, method, level, …])	Create index with target’s values (move/add/delete values as necessary)
Index.rename(name[, inplace])	Set new names on index.
Index.repeat(repeats, *args, **kwargs)	Repeat elements of an Index.
Index.where(cond[, other])	New in version 0.19.0.
Index.take(indices[, axis, allow_fill, …])	return a new Index of the values selected by the indices
Index.putmask(mask, value)	return a new Index of the values set with the mask
Index.set_names(names[, level, inplace])	Set new names on index.
Index.unique([level])	Return unique values in the index.
Index.nunique([dropna])	Return number of unique elements in the object.
Index.value_counts([normalize, sort, …])	Returns object containing counts of unique values.

Missing Values

Index.fillna([value, downcast])	Fill NA/NaN values with the specified value
Index.dropna([how])	Return Index without NA/NaN values
Index.isna()	Detect missing values.
Index.notna()	Detect existing (non-missing) values.

Conversion

Index.astype(dtype[, copy])	Create an Index with values cast to dtypes.
Index.item()	return the first element of the underlying data as a python scalar
Index.map(mapper[, na_action])	Map values using input correspondence (a dict, Series, or function).
Index.ravel([order])	return an ndarray of the flattened values of the underlying data
Index.tolist()	Return a list of the values.
Index.to_native_types([slicer])	Format specified values of self and return them.
Index.to_series([index, name])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index
Index.to_frame([index])	Create a DataFrame with a column containing the Index.
Index.view([cls])

Sorting

Index.argsort(*args, **kwargs)	Return the integer indicies that would sort the index.
Index.searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
Index.sort_values([return_indexer, ascending])	Return a sorted copy of the index.

Time-specific operations

Index.shift([periods, freq])

Shift index by desired number of time frequency increments.

Combining / joining / set operations

Index.append(other)	Append a collection of Index options together
Index.join(other[, how, level, …])	this is an internal non-public method
Index.intersection(other)	Form the intersection of two Index objects.
Index.union(other)	Form the union of two Index objects and sorts if possible.
Index.difference(other)	Return a new Index with elements from the index that are not in other.
Index.symmetric_difference(other[, result_name])	Compute the symmetric difference of two Index objects.

Selecting

Index.asof(label)	For a sorted index, return the most recent label up to and including the passed label.
Index.asof_locs(where, mask)	where : array of timestamps mask : array of booleans where data is not NA
Index.contains(key)	return a boolean if this key is IN the index
Index.get_duplicates()	(DEPRECATED) Extract duplicated index elements.
Index.get_indexer(target[, method, limit, …])	Compute indexer and mask for new index given the current index.
Index.get_indexer_for(target, **kwargs)	guaranteed return of an indexer even when non-unique This dispatches to get_indexer or get_indexer_nonunique as appropriate
Index.get_indexer_non_unique(target)	Compute indexer and mask for new index given the current index.
Index.get_level_values(level)	Return an Index of values for requested level, equal to the length of the index.
Index.get_loc(key[, method, tolerance])	Get integer location, slice or boolean mask for requested label.
Index.get_slice_bound(label, side, kind)	Calculate slice bound that corresponds to given label.
Index.get_value(series, key)	Fast lookup of value from 1-dimensional ndarray.
Index.get_values()	Return Index data as an numpy.ndarray.
Index.set_value(arr, key, value)	Fast lookup of value from 1-dimensional ndarray.
Index.isin(values[, level])	Return a boolean array where the index values are in values.
Index.slice_indexer([start, end, step, kind])	For an ordered or unique index, compute the slice indexer for input labels and step.
Index.slice_locs([start, end, step, kind])	Compute slice locations for input labels.

Numeric Index

RangeIndex	Immutable Index implementing a monotonic integer range.
Int64Index	Immutable ndarray implementing an ordered, sliceable set.
UInt64Index	Immutable ndarray implementing an ordered, sliceable set.
Float64Index	Immutable ndarray implementing an ordered, sliceable set.

RangeIndex.from_range(data[, name, dtype])

create RangeIndex from a range (py3), or xrange (py2) object

CategoricalIndex

CategoricalIndex

Immutable Index implementing an ordered, sliceable set.

Categorical Components

CategoricalIndex.codes
CategoricalIndex.categories
CategoricalIndex.ordered
CategoricalIndex.rename_categories(*args, …)	Renames categories.
CategoricalIndex.reorder_categories(*args, …)	Reorders categories as specified in new_categories.
CategoricalIndex.add_categories(*args, **kwargs)	Add new categories.
CategoricalIndex.remove_categories(*args, …)	Removes the specified categories.
CategoricalIndex.remove_unused_categories(…)	Removes categories which are not used.
CategoricalIndex.set_categories(*args, **kwargs)	Sets the categories to the specified new_categories.
CategoricalIndex.as_ordered(*args, **kwargs)	Sets the Categorical to be ordered
CategoricalIndex.as_unordered(*args, **kwargs)	Sets the Categorical to be unordered
CategoricalIndex.map(mapper)	Map values using input correspondence (a dict, Series, or function).

IntervalIndex

IntervalIndex

Immutable Index implementing an ordered, sliceable set.

IntervalIndex Components

IntervalIndex.from_arrays(left, right[, …])	Construct from two arrays defining the left and right bounds.
IntervalIndex.from_tuples(data[, closed, …])	Construct an IntervalIndex from a list/array of tuples
IntervalIndex.from_breaks(breaks[, closed, …])	Construct an IntervalIndex from an array of splits
IntervalIndex.contains(key)	Return a boolean indicating if the key is IN the index
IntervalIndex.left	Return the left endpoints of each Interval in the IntervalIndex as an Index
IntervalIndex.right	Return the right endpoints of each Interval in the IntervalIndex as an Index
IntervalIndex.mid	Return the midpoint of each Interval in the IntervalIndex as an Index
IntervalIndex.closed	Whether the intervals are closed on the left-side, right-side, both or neither
IntervalIndex.length	Return an Index with entries denoting the length of each Interval in the IntervalIndex
IntervalIndex.values	Return the IntervalIndex’s data as a numpy array of Interval objects (with dtype=’object’)
IntervalIndex.is_non_overlapping_monotonic	Return True if the IntervalIndex is non-overlapping (no Intervals share points) and is either monotonic increasing or monotonic decreasing, else False
IntervalIndex.get_loc(key[, method])	Get integer location, slice or boolean mask for requested label.
IntervalIndex.get_indexer(target[, method, …])	Compute indexer and mask for new index given the current index.

MultiIndex

MultiIndex

A multi-level, or hierarchical, index object for pandas objects

IndexSlice

Create an object to more easily perform multi-index slicing

MultiIndex Constructors

MultiIndex.from_arrays(arrays[, sortorder, …])	Convert arrays to MultiIndex
MultiIndex.from_tuples(tuples[, sortorder, …])	Convert list of tuples to MultiIndex
MultiIndex.from_product(iterables[, …])	Make a MultiIndex from the cartesian product of multiple iterables

MultiIndex Attributes

MultiIndex.names	Names of levels in MultiIndex
MultiIndex.levels
MultiIndex.labels
MultiIndex.nlevels	Integer number of levels in this MultiIndex.
MultiIndex.levshape	A tuple with the length of each level.

MultiIndex Components

MultiIndex.set_levels(levels[, level, …])	Set new levels on MultiIndex.
MultiIndex.set_labels(labels[, level, …])	Set new labels on MultiIndex.
MultiIndex.to_hierarchical(n_repeat[, n_shuffle])	Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle.
MultiIndex.to_frame([index])	Create a DataFrame with the levels of the MultiIndex as columns.
MultiIndex.is_lexsorted()	Return True if the labels are lexicographically sorted
MultiIndex.sortlevel([level, ascending, …])	Sort MultiIndex at the requested level.
MultiIndex.droplevel([level])	Return Index with requested level removed.
MultiIndex.swaplevel([i, j])	Swap level i with level j.
MultiIndex.reorder_levels(order)	Rearrange levels using input order.
MultiIndex.remove_unused_levels()	create a new MultiIndex from the current that removing unused levels, meaning that they are not expressed in the labels
MultiIndex.unique([level])	Return unique values in the index.

MultiIndex Selecting

MultiIndex.get_loc(key[, method])	Get location for a label or a tuple of labels as an integer, slice or boolean mask.
MultiIndex.get_indexer(target[, method, …])	Compute indexer and mask for new index given the current index.
MultiIndex.get_level_values(level)	Return vector of label values for requested level, equal to the length of the index.

DatetimeIndex

DatetimeIndex

Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.

Time/Date Components

DatetimeIndex.year	The year of the datetime
DatetimeIndex.month	The month as January=1, December=12
DatetimeIndex.day	The days of the datetime
DatetimeIndex.hour	The hours of the datetime
DatetimeIndex.minute	The minutes of the datetime
DatetimeIndex.second	The seconds of the datetime
DatetimeIndex.microsecond	The microseconds of the datetime
DatetimeIndex.nanosecond	The nanoseconds of the datetime
DatetimeIndex.date	Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information).
DatetimeIndex.time	Returns numpy array of datetime.time.
DatetimeIndex.dayofyear	The ordinal day of the year
DatetimeIndex.weekofyear	The week ordinal of the year
DatetimeIndex.week	The week ordinal of the year
DatetimeIndex.dayofweek	The day of the week with Monday=0, Sunday=6
DatetimeIndex.weekday	The day of the week with Monday=0, Sunday=6
DatetimeIndex.quarter	The quarter of the date
DatetimeIndex.tz
DatetimeIndex.freq	Return the frequency object if it is set, otherwise None
DatetimeIndex.freqstr	Return the frequency object as a string if it is set, otherwise None
DatetimeIndex.is_month_start	Logical indicating if first day of month (defined by frequency)
DatetimeIndex.is_month_end	Indicator for whether the date is the last day of the month.
DatetimeIndex.is_quarter_start	Indicator for whether the date is the first day of a quarter.
DatetimeIndex.is_quarter_end	Indicator for whether the date is the last day of a quarter.
DatetimeIndex.is_year_start	Indicate whether the date is the first day of a year.
DatetimeIndex.is_year_end	Indicate whether the date is the last day of the year.
DatetimeIndex.is_leap_year	Boolean indicator if the date belongs to a leap year.
DatetimeIndex.inferred_freq	Tries to return a string representing a frequency guess, generated by infer_freq.

Selecting

DatetimeIndex.indexer_at_time(time[, asof])	Returns index locations of index values at particular time of day (e.g.
DatetimeIndex.indexer_between_time(…[, …])	Return index locations of values between particular times of day (e.g., 9:00-9:30AM).

Time-specific operations

DatetimeIndex.normalize()	Convert times to midnight.
DatetimeIndex.strftime(date_format)	Convert to Index using specified date_format.
DatetimeIndex.snap([freq])	Snap time stamps to nearest occurring frequency
DatetimeIndex.tz_convert(tz)	Convert tz-aware DatetimeIndex from one time zone to another.
DatetimeIndex.tz_localize(tz[, ambiguous, …])	Localize tz-naive DatetimeIndex to tz-aware DatetimeIndex.
DatetimeIndex.round(freq, *args, **kwargs)	round the data to the specified freq.
DatetimeIndex.floor(freq)	floor the data to the specified freq.
DatetimeIndex.ceil(freq)	ceil the data to the specified freq.
DatetimeIndex.month_name([locale])	Return the month names of the DateTimeIndex with specified locale.
DatetimeIndex.day_name([locale])	Return the day names of the DateTimeIndex with specified locale.

Conversion

DatetimeIndex.to_period([freq])	Cast to PeriodIndex at a particular frequency.
DatetimeIndex.to_perioddelta(freq)	Calculate TimedeltaIndex of difference between index values and index converted to periodIndex at specified freq.
DatetimeIndex.to_pydatetime()	Return DatetimeIndex as object ndarray of datetime.datetime objects
DatetimeIndex.to_series([keep_tz, index, name])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index
DatetimeIndex.to_frame([index])	Create a DataFrame with a column containing the Index.

TimedeltaIndex

TimedeltaIndex

Immutable ndarray of timedelta64 data, represented internally as int64, and which can be boxed to timedelta objects

Components

TimedeltaIndex.days	Number of days for each element.
TimedeltaIndex.seconds	Number of seconds (>= 0 and less than 1 day) for each element.
TimedeltaIndex.microseconds	Number of microseconds (>= 0 and less than 1 second) for each element.
TimedeltaIndex.nanoseconds	Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.
TimedeltaIndex.components	Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.
TimedeltaIndex.inferred_freq	Tries to return a string representing a frequency guess, generated by infer_freq.

Conversion

TimedeltaIndex.to_pytimedelta()	Return TimedeltaIndex as object ndarray of datetime.timedelta objects
TimedeltaIndex.to_series([index, name])	Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index
TimedeltaIndex.round(freq, *args, **kwargs)	round the data to the specified freq.
TimedeltaIndex.floor(freq)	floor the data to the specified freq.
TimedeltaIndex.ceil(freq)	ceil the data to the specified freq.
TimedeltaIndex.to_frame([index])	Create a DataFrame with a column containing the Index.

PeriodIndex

PeriodIndex

Immutable ndarray holding ordinal values indicating regular periods in time such as particular years, quarters, months, etc.

Attributes

PeriodIndex.day	The days of the period
PeriodIndex.dayofweek	The day of the week with Monday=0, Sunday=6
PeriodIndex.dayofyear	The ordinal day of the year
PeriodIndex.days_in_month	The number of days in the month
PeriodIndex.daysinmonth	The number of days in the month
PeriodIndex.end_time
PeriodIndex.freq	Return the frequency object if it is set, otherwise None
PeriodIndex.freqstr	Return the frequency object as a string if it is set, otherwise None
PeriodIndex.hour	The hour of the period
PeriodIndex.is_leap_year	Logical indicating if the date belongs to a leap year
PeriodIndex.minute	The minute of the period
PeriodIndex.month	The month as January=1, December=12
PeriodIndex.quarter	The quarter of the date
PeriodIndex.qyear
PeriodIndex.second	The second of the period
PeriodIndex.start_time
PeriodIndex.week	The week ordinal of the year
PeriodIndex.weekday	The day of the week with Monday=0, Sunday=6
PeriodIndex.weekofyear	The week ordinal of the year
PeriodIndex.year	The year of the period

Methods

PeriodIndex.asfreq([freq, how])	Convert the PeriodIndex to the specified frequency freq.
PeriodIndex.strftime(date_format)	Convert to Index using specified date_format.
PeriodIndex.to_timestamp([freq, how])	Cast to DatetimeIndex
PeriodIndex.tz_convert(tz)	Convert tz-aware DatetimeIndex from one time zone to another (using pytz/dateutil)
PeriodIndex.tz_localize(tz[, ambiguous])	Localize tz-naive DatetimeIndex to given time zone (using pytz/dateutil), or remove timezone from tz-aware DatetimeIndex

Scalars

Period

Period

Represents a period of time

Attributes

Period.day	Get day of the month that a Period falls on.
Period.dayofweek	Return the day of the week.
Period.dayofyear	Return the day of the year.
Period.days_in_month	Get the total number of days in the month that this period falls on.
Period.daysinmonth	Get the total number of days of the month that the Period falls in.
Period.end_time
Period.freq
Period.freqstr
Period.hour	Get the hour of the day component of the Period.
Period.is_leap_year
Period.minute	Get minute of the hour component of the Period.
Period.month
Period.ordinal
Period.quarter
Period.qyear
Period.second	Get the second component of the Period.
Period.start_time	Get the Timestamp for the start of the period.
Period.week	Get the week of the year on the given Period.
Period.weekday
Period.weekofyear
Period.year

Methods

Period.asfreq	Convert Period to desired frequency, either at the start or end of the interval
Period.now
Period.strftime	Returns the string representation of the Period, depending on the selected fmt.
Period.to_timestamp	Return the Timestamp representation of the Period at the target frequency at the specified end (how) of the Period

Timestamp

Timestamp

Pandas replacement for datetime.datetime

Properties

Timestamp.asm8
Timestamp.day
Timestamp.dayofweek
Timestamp.dayofyear
Timestamp.days_in_month
Timestamp.daysinmonth
Timestamp.fold
Timestamp.hour
Timestamp.is_leap_year
Timestamp.is_month_end
Timestamp.is_month_start
Timestamp.is_quarter_end
Timestamp.is_quarter_start
Timestamp.is_year_end
Timestamp.is_year_start
Timestamp.max
Timestamp.microsecond
Timestamp.min
Timestamp.minute
Timestamp.month
Timestamp.nanosecond
Timestamp.quarter
Timestamp.resolution
Timestamp.second
Timestamp.tz	Alias for tzinfo
Timestamp.tzinfo
Timestamp.value
Timestamp.week
Timestamp.weekofyear
Timestamp.year

Methods

Timestamp.astimezone	Convert tz-aware Timestamp to another time zone.
Timestamp.ceil	return a new Timestamp ceiled to this resolution
Timestamp.combine(date, time)	date, time -> datetime with same date and time fields
Timestamp.ctime	Return ctime() style string.
Timestamp.date	Return date object with same year, month and day.
Timestamp.day_name	Return the day name of the Timestamp with specified locale.
Timestamp.dst	Return self.tzinfo.dst(self).
Timestamp.floor	return a new Timestamp floored to this resolution
Timestamp.freq
Timestamp.freqstr
Timestamp.fromordinal(ordinal[, freq, tz])	passed an ordinal, translate and convert to a ts note: by definition there cannot be any tz info on the ordinal itself
Timestamp.fromtimestamp(ts)	timestamp[, tz] -> tz’s local time from POSIX timestamp.
Timestamp.isocalendar	Return a 3-tuple containing ISO year, week number, and weekday.
Timestamp.isoformat
Timestamp.isoweekday	Return the day of the week represented by the date.
Timestamp.month_name	Return the month name of the Timestamp with specified locale.
Timestamp.normalize	Normalize Timestamp to midnight, preserving tz information.
Timestamp.now([tz])	Returns new Timestamp object representing current time local to tz.
Timestamp.replace	implements datetime.replace, handles nanoseconds
Timestamp.round	Round the Timestamp to the specified resolution
Timestamp.strftime	format -> strftime() style string.
Timestamp.strptime	string, format -> new datetime parsed from a string (like time.strptime()).
Timestamp.time	Return time object with same time but with tzinfo=None.
Timestamp.timestamp	Return POSIX timestamp as float.
Timestamp.timetuple	Return time tuple, compatible with time.localtime().
Timestamp.timetz	Return time object with same time and tzinfo.
Timestamp.to_datetime64	Returns a numpy.datetime64 object with ‘ns’ precision
Timestamp.to_julian_date	Convert TimeStamp to a Julian Date.
Timestamp.to_period	Return an period of which this timestamp is an observation.
Timestamp.to_pydatetime	Convert a Timestamp object to a native Python datetime object.
Timestamp.today(cls[, tz])	Return the current time in the local timezone.
Timestamp.toordinal	Return proleptic Gregorian ordinal.
Timestamp.tz_convert	Convert tz-aware Timestamp to another time zone.
Timestamp.tz_localize	Convert naive Timestamp to local time zone, or remove timezone from tz-aware Timestamp.
Timestamp.tzname	Return self.tzinfo.tzname(self).
Timestamp.utcfromtimestamp(ts)	Construct a naive UTC datetime from a POSIX timestamp.
Timestamp.utcnow()	Return a new Timestamp representing UTC day and time.
Timestamp.utcoffset	Return self.tzinfo.utcoffset(self).
Timestamp.utctimetuple	Return UTC time tuple, compatible with time.localtime().
Timestamp.weekday	Return the day of the week represented by the date.

Interval

Interval

Immutable object implementing an Interval, a bounded slice-like interval.

Properties

Interval.closed	Whether the interval is closed on the left-side, right-side, both or neither
Interval.closed_left	Check if the interval is closed on the left side.
Interval.closed_right	Check if the interval is closed on the right side.
Interval.left	Left bound for the interval
Interval.length	Return the length of the Interval
Interval.mid	Return the midpoint of the Interval
Interval.open_left	Check if the interval is open on the left side.
Interval.open_right	Check if the interval is open on the right side.
Interval.right	Right bound for the interval

Timedelta

Timedelta

Represents a duration, the difference between two dates or times.

Properties

Timedelta.asm8	return a numpy timedelta64 array view of myself
Timedelta.components	Return a Components NamedTuple-like
Timedelta.days	Number of days.
Timedelta.delta	Return the timedelta in nanoseconds (ns), for internal compatibility.
Timedelta.freq
Timedelta.is_populated
Timedelta.max
Timedelta.microseconds	Number of microseconds (>= 0 and less than 1 second).
Timedelta.min
Timedelta.nanoseconds	Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
Timedelta.resolution	return a string representing the lowest resolution that we have
Timedelta.seconds	Number of seconds (>= 0 and less than 1 day).
Timedelta.value
Timedelta.view	array view compat

Methods

Timedelta.ceil	return a new Timedelta ceiled to this resolution
Timedelta.floor	return a new Timedelta floored to this resolution
Timedelta.isoformat	Format Timedelta as ISO 8601 Duration like P[n]Y[n]M[n]DT[n]H[n]M[n]S, where the [n] s are replaced by the values.
Timedelta.round	Round the Timedelta to the specified resolution
Timedelta.to_pytimedelta	return an actual datetime.timedelta object note: we lose nanosecond resolution if any
Timedelta.to_timedelta64	Returns a numpy.timedelta64 object with ‘ns’ precision
Timedelta.total_seconds	Total duration of timedelta in seconds (to ns precision)

Frequencies

to_offset(freq)

Return DateOffset object from string or tuple representation or datetime.timedelta object

Window

Rolling objects are returned by .rolling calls: pandas.DataFrame.rolling(), pandas.Series.rolling(), etc. Expanding objects are returned by .expanding calls: pandas.DataFrame.expanding(), pandas.Series.expanding(), etc. EWM objects are returned by .ewm calls: pandas.DataFrame.ewm(), pandas.Series.ewm(), etc.

Standard moving window functions

Rolling.count()	The rolling count of any non-NaN observations inside the window.
Rolling.sum(*args, **kwargs)	Calculate rolling sum of given DataFrame or Series.
Rolling.mean(*args, **kwargs)	Calculate the rolling mean of the values.
Rolling.median(**kwargs)	Calculate the rolling median.
Rolling.var([ddof])	Calculate unbiased rolling variance.
Rolling.std([ddof])	Calculate rolling standard deviation.
Rolling.min(*args, **kwargs)	Calculate the rolling minimum.
Rolling.max(*args, **kwargs)	rolling maximum
Rolling.corr([other, pairwise])	rolling sample correlation
Rolling.cov([other, pairwise, ddof])	rolling sample covariance
Rolling.skew(**kwargs)	Unbiased rolling skewness
Rolling.kurt(**kwargs)	Calculate unbiased rolling kurtosis.
Rolling.apply(func[, raw, args, kwargs])	rolling function apply
Rolling.aggregate(arg, *args, **kwargs)	Aggregate using one or more operations over the specified axis.
Rolling.quantile(quantile[, interpolation])	rolling quantile.
Window.mean(*args, **kwargs)	Calculate the window mean of the values.
Window.sum(*args, **kwargs)	Calculate window sum of given DataFrame or Series.

Standard expanding window functions

Expanding.count(**kwargs)	The expanding count of any non-NaN observations inside the window.
Expanding.sum(*args, **kwargs)	Calculate expanding sum of given DataFrame or Series.
Expanding.mean(*args, **kwargs)	Calculate the expanding mean of the values.
Expanding.median(**kwargs)	Calculate the expanding median.
Expanding.var([ddof])	Calculate unbiased expanding variance.
Expanding.std([ddof])	Calculate expanding standard deviation.
Expanding.min(*args, **kwargs)	Calculate the expanding minimum.
Expanding.max(*args, **kwargs)	expanding maximum
Expanding.corr([other, pairwise])	expanding sample correlation
Expanding.cov([other, pairwise, ddof])	expanding sample covariance
Expanding.skew(**kwargs)	Unbiased expanding skewness
Expanding.kurt(**kwargs)	Calculate unbiased expanding kurtosis.
Expanding.apply(func[, raw, args, kwargs])	expanding function apply
Expanding.aggregate(arg, *args, **kwargs)	Aggregate using one or more operations over the specified axis.
Expanding.quantile(quantile[, interpolation])	expanding quantile.

Exponentially-weighted moving window functions

EWM.mean(*args, **kwargs)	exponential weighted moving average
EWM.std([bias])	exponential weighted moving stddev
EWM.var([bias])	exponential weighted moving variance
EWM.corr([other, pairwise])	exponential weighted sample correlation
EWM.cov([other, pairwise, bias])	exponential weighted sample covariance

GroupBy

GroupBy objects are returned by groupby calls: pandas.DataFrame.groupby(), pandas.Series.groupby(), etc.

Indexing, iteration

GroupBy.__iter__()	Groupby iterator
GroupBy.groups	dict {group name -> group labels}
GroupBy.indices	dict {group name -> group indices}
GroupBy.get_group(name[, obj])	Constructs NDFrame from group with provided name

Grouper([key, level, freq, axis, sort])

A Grouper allows the user to specify a groupby instruction for a target object

Function application

GroupBy.apply(func, *args, **kwargs)	Apply function func group-wise and combine the results together.
GroupBy.aggregate(func, *args, **kwargs)
GroupBy.transform(func, *args, **kwargs)
GroupBy.pipe(func, *args, **kwargs)	Apply a function func with arguments to this GroupBy object and return the function’s result.

Computations / Descriptive Stats

GroupBy.all([skipna])	Returns True if all values in the group are truthful, else False
GroupBy.any([skipna])	Returns True if any value in the group is truthful, else False
GroupBy.bfill([limit])	Backward fill the values
GroupBy.count()	Compute count of group, excluding missing values
GroupBy.cumcount([ascending])	Number each item in each group from 0 to the length of that group - 1.
GroupBy.ffill([limit])	Forward fill the values
GroupBy.first(**kwargs)	Compute first of group values
GroupBy.head([n])	Returns first n rows of each group.
GroupBy.last(**kwargs)	Compute last of group values
GroupBy.max(**kwargs)	Compute max of group values
GroupBy.mean(*args, **kwargs)	Compute mean of groups, excluding missing values
GroupBy.median(**kwargs)	Compute median of groups, excluding missing values
GroupBy.min(**kwargs)	Compute min of group values
GroupBy.ngroup([ascending])	Number each group from 0 to the number of groups - 1.
GroupBy.nth(n[, dropna])	Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints.
GroupBy.ohlc()	Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex
GroupBy.prod(**kwargs)	Compute prod of group values
GroupBy.rank([method, ascending, na_option, …])	Provides the rank of values within each group.
GroupBy.pct_change([periods, fill_method, …])	Calcuate pct_change of each value to previous entry in group
GroupBy.size()	Compute group sizes
GroupBy.sem([ddof])	Compute standard error of the mean of groups, excluding missing values
GroupBy.std([ddof])	Compute standard deviation of groups, excluding missing values
GroupBy.sum(**kwargs)	Compute sum of group values
GroupBy.var([ddof])	Compute variance of groups, excluding missing values
GroupBy.tail([n])	Returns last n rows of each group

The following methods are available in both SeriesGroupBy and DataFrameGroupBy objects, but may differ slightly, usually in that the DataFrameGroupBy version usually permits the specification of an axis argument, and often an argument indicating whether to restrict application to columns of a specific data type.

DataFrameGroupBy.agg(arg, *args, **kwargs)	Aggregate using one or more operations over the specified axis.
DataFrameGroupBy.all([skipna])	Returns True if all values in the group are truthful, else False
DataFrameGroupBy.any([skipna])	Returns True if any value in the group is truthful, else False
DataFrameGroupBy.bfill([limit])	Backward fill the values
DataFrameGroupBy.corr	Compute pairwise correlation of columns, excluding NA/null values
DataFrameGroupBy.count()	Compute count of group, excluding missing values
DataFrameGroupBy.cov	Compute pairwise covariance of columns, excluding NA/null values.
DataFrameGroupBy.cummax([axis])	Cumulative max for each group
DataFrameGroupBy.cummin([axis])	Cumulative min for each group
DataFrameGroupBy.cumprod([axis])	Cumulative product for each group
DataFrameGroupBy.cumsum([axis])	Cumulative sum for each group
DataFrameGroupBy.describe(**kwargs)	Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
DataFrameGroupBy.diff	First discrete difference of element.
DataFrameGroupBy.ffill([limit])	Forward fill the values
DataFrameGroupBy.fillna	Fill NA/NaN values using the specified method
DataFrameGroupBy.filter(func[, dropna])	Return a copy of a DataFrame excluding elements from groups that do not satisfy the boolean criterion specified by func.
DataFrameGroupBy.hist	Make a histogram of the DataFrame’s.
DataFrameGroupBy.idxmax	Return index of first occurrence of maximum over requested axis.
DataFrameGroupBy.idxmin	Return index of first occurrence of minimum over requested axis.
DataFrameGroupBy.mad	Return the mean absolute deviation of the values for the requested axis
DataFrameGroupBy.pct_change([periods, …])	Calcuate pct_change of each value to previous entry in group
DataFrameGroupBy.plot	Class implementing the .plot attribute for groupby objects
DataFrameGroupBy.quantile	Return values at the given quantile over requested axis, a la numpy.percentile.
DataFrameGroupBy.rank([method, ascending, …])	Provides the rank of values within each group.
DataFrameGroupBy.resample(rule, *args, **kwargs)	Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended
DataFrameGroupBy.shift([periods, freq, axis])	Shift each group by periods observations
DataFrameGroupBy.size()	Compute group sizes
DataFrameGroupBy.skew	Return unbiased skew over requested axis Normalized by N-1
DataFrameGroupBy.take	Return the elements in the given positional indices along an axis.
DataFrameGroupBy.tshift	Shift the time index, using the index’s frequency if available.

The following methods are available only for SeriesGroupBy objects.

SeriesGroupBy.nlargest	Return the largest n elements.
SeriesGroupBy.nsmallest	Return the smallest n elements.
SeriesGroupBy.nunique([dropna])	Returns number of unique elements in the group
SeriesGroupBy.unique	Return unique values of Series object.
SeriesGroupBy.value_counts([normalize, …])
SeriesGroupBy.is_monotonic_increasing	Return boolean if values in the object are monotonic_increasing
SeriesGroupBy.is_monotonic_decreasing	Return boolean if values in the object are monotonic_decreasing

The following methods are available only for DataFrameGroupBy objects.

DataFrameGroupBy.corrwith	Compute pairwise correlation between rows or columns of two DataFrame objects.
DataFrameGroupBy.boxplot([subplots, column, …])	Make box plots from DataFrameGroupBy data.

Resampling

Resampler objects are returned by resample calls: pandas.DataFrame.resample(), pandas.Series.resample().

Indexing, iteration

Resampler.__iter__()	Groupby iterator
Resampler.groups	dict {group name -> group labels}
Resampler.indices	dict {group name -> group indices}
Resampler.get_group(name[, obj])	Constructs NDFrame from group with provided name

Function application

Resampler.apply(arg, *args, **kwargs)	Aggregate using one or more operations over the specified axis.
Resampler.aggregate(arg, *args, **kwargs)	Aggregate using one or more operations over the specified axis.
Resampler.transform(arg, *args, **kwargs)	Call function producing a like-indexed Series on each group and return a Series with the transformed values
Resampler.pipe(func, *args, **kwargs)	Apply a function func with arguments to this Resampler object and return the function’s result.

Upsampling

Resampler.ffill([limit])	Forward fill the values
Resampler.backfill([limit])	Backward fill the new missing values in the resampled data.
Resampler.bfill([limit])	Backward fill the new missing values in the resampled data.
Resampler.pad([limit])	Forward fill the values
Resampler.nearest([limit])	Fill values with nearest neighbor starting from center
Resampler.fillna(method[, limit])	Fill missing values introduced by upsampling.
Resampler.asfreq([fill_value])	return the values at the new freq, essentially a reindex
Resampler.interpolate([method, axis, limit, …])	Interpolate values according to different methods.

Computations / Descriptive Stats

Resampler.count([_method])	Compute count of group, excluding missing values
Resampler.nunique([_method])	Returns number of unique elements in the group
Resampler.first([_method])	Compute first of group values
Resampler.last([_method])	Compute last of group values
Resampler.max([_method])	Compute max of group values
Resampler.mean([_method])	Compute mean of groups, excluding missing values
Resampler.median([_method])	Compute median of groups, excluding missing values
Resampler.min([_method])	Compute min of group values
Resampler.ohlc([_method])	Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex
Resampler.prod([_method, min_count])	Compute prod of group values
Resampler.size()	Compute group sizes
Resampler.sem([_method])	Compute standard error of the mean of groups, excluding missing values
Resampler.std([ddof])	Compute standard deviation of groups, excluding missing values
Resampler.sum([_method, min_count])	Compute sum of group values
Resampler.var([ddof])	Compute variance of groups, excluding missing values

Style

Styler objects are returned by pandas.DataFrame.style.

Styler Constructor

Styler(data[, precision, table_styles, …])	Helps style a DataFrame or Series according to the data with HTML and CSS.
Styler.from_custom_template(searchpath, name)	Factory function for creating a subclass of Styler with a custom template and Jinja environment.

Styler Attributes

Styler.env
Styler.template
Styler.loader

Style Application

Styler.apply(func[, axis, subset])	Apply a function column-wise, row-wise, or table-wase, updating the HTML representation with the result.
Styler.applymap(func[, subset])	Apply a function elementwise, updating the HTML representation with the result.
Styler.where(cond, value[, other, subset])	Apply a function elementwise, updating the HTML representation with a style which is selected in accordance with the return value of a function.
Styler.format(formatter[, subset])	Format the text display value of cells.
Styler.set_precision(precision)	Set the precision used to render.
Styler.set_table_styles(table_styles)	Set the table styles on a Styler.
Styler.set_table_attributes(attributes)	Set the table attributes.
Styler.set_caption(caption)	Set the caption on a Styler
Styler.set_properties([subset])	Convenience method for setting one or more non-data dependent properties or each cell.
Styler.set_uuid(uuid)	Set the uuid for a Styler.
Styler.clear()	“Reset” the styler, removing any previously applied styles.

Builtin Styles

Styler.highlight_max([subset, color, axis])	Highlight the maximum by shading the background
Styler.highlight_min([subset, color, axis])	Highlight the minimum by shading the background
Styler.highlight_null([null_color])	Shade the background null_color for missing values.
Styler.background_gradient([cmap, low, …])	Color the background in a gradient according to the data in each column (optionally row).
Styler.bar([subset, axis, color, width, align])	Color the background color proptional to the values in each column.

Style Export and Import

Styler.render(**kwargs)	Render the built up styles to HTML
Styler.export()	Export the styles to applied to the current Styler.
Styler.use(styles)	Set the styles on the current Styler, possibly using styles from Styler.export.
Styler.to_excel(excel_writer[, sheet_name, …])	Write Styler to an excel sheet

Plotting

The following functions are contained in the pandas.plotting module.

andrews_curves(frame, class_column[, ax, …])	Generates a matplotlib plot of Andrews curves, for visualising clusters of multivariate data.
bootstrap_plot(series[, fig, size, samples])	Bootstrap plot on mean, median and mid-range statistics.
deregister_matplotlib_converters()	Remove pandas’ formatters and converters
lag_plot(series[, lag, ax])	Lag plot for time series.
parallel_coordinates(frame, class_column[, …])	Parallel coordinates plotting.
radviz(frame, class_column[, ax, color, …])	Plot a multidimensional dataset in 2D.
register_matplotlib_converters([explicit])	Register Pandas Formatters and Converters with matplotlib
scatter_matrix(frame[, alpha, figsize, ax, …])	Draw a matrix of scatter plots.

General utility functions

Working with options

describe_option(pat[, _print_desc])	Prints the description for one or more registered options.
reset_option(pat)	Reset one or more options to their default value.
get_option(pat)	Retrieves the value of the specified option.
set_option(pat, value)	Sets the value of the specified option.
option_context(*args)	Context manager to temporarily set options in the with statement context.

Testing functions

testing.assert_frame_equal(left, right[, …])	Check that left and right DataFrame are equal.
testing.assert_series_equal(left, right[, …])	Check that left and right Series are equal.
testing.assert_index_equal(left, right[, …])	Check that left and right Index are equal.

Exceptions and warnings

errors.DtypeWarning	Warning raised when reading different dtypes in a column from a file.
errors.EmptyDataError	Exception that is thrown in pd.read_csv (by both the C and Python engines) when empty data or header is encountered.
errors.OutOfBoundsDatetime
errors.ParserError	Exception that is raised by an error encountered in pd.read_csv.
errors.ParserWarning	Warning raised when reading a file that doesn’t use the default ‘c’ parser.
errors.PerformanceWarning	Warning raised when there is a possible performance impact.
errors.UnsortedIndexError	Error raised when attempting to get a slice of a MultiIndex, and the index has not been lexsorted.
errors.UnsupportedFunctionCall	Exception raised when attempting to call a numpy function on a pandas object, but that function is not supported by the object e.g.

Data types related functionality

api.types.union_categoricals(to_union[, …])	Combine list-like of Categorical-like, unioning categories.
api.types.infer_dtype	Efficiently infer the type of a passed val, or list-like array of values.
api.types.pandas_dtype(dtype)	Converts input into a pandas only dtype object or a numpy dtype object.

Dtype introspection

api.types.is_bool_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a boolean dtype.
api.types.is_categorical_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the Categorical dtype.
api.types.is_complex_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a complex dtype.
api.types.is_datetime64_any_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the datetime64 dtype.
api.types.is_datetime64_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the datetime64 dtype.
api.types.is_datetime64_ns_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the datetime64[ns] dtype.
api.types.is_datetime64tz_dtype(arr_or_dtype)	Check whether an array-like or dtype is of a DatetimeTZDtype dtype.
api.types.is_extension_type(arr)	Check whether an array-like is of a pandas extension class instance.
api.types.is_float_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a float dtype.
api.types.is_int64_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the int64 dtype.
api.types.is_integer_dtype(arr_or_dtype)	Check whether the provided array or dtype is of an integer dtype.
api.types.is_interval_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the Interval dtype.
api.types.is_numeric_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a numeric dtype.
api.types.is_object_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the object dtype.
api.types.is_period_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the Period dtype.
api.types.is_signed_integer_dtype(arr_or_dtype)	Check whether the provided array or dtype is of a signed integer dtype.
api.types.is_string_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the string dtype.
api.types.is_timedelta64_dtype(arr_or_dtype)	Check whether an array-like or dtype is of the timedelta64 dtype.
api.types.is_timedelta64_ns_dtype(arr_or_dtype)	Check whether the provided array or dtype is of the timedelta64[ns] dtype.
api.types.is_unsigned_integer_dtype(arr_or_dtype)	Check whether the provided array or dtype is of an unsigned integer dtype.
api.types.is_sparse(arr)	Check whether an array-like is a pandas sparse array.

Iterable introspection

api.types.is_dict_like(obj)	Check if the object is dict-like.
api.types.is_file_like(obj)	Check if the object is a file-like object.
api.types.is_list_like(obj)	Check if the object is list-like.
api.types.is_named_tuple(obj)	Check if the object is a named tuple.
api.types.is_iterator(obj)	Check if the object is an iterator.

Scalar introspection

api.types.is_bool
api.types.is_categorical(arr)	Check whether an array-like is a Categorical instance.
api.types.is_complex
api.types.is_datetimetz(arr)	Check whether an array-like is a datetime array-like with a timezone component in its dtype.
api.types.is_float
api.types.is_hashable(obj)	Return True if hash(obj) will succeed, False otherwise.
api.types.is_integer
api.types.is_interval
api.types.is_number(obj)	Check if the object is a number.
api.types.is_period(arr)	Check whether an array-like is a periodical index.
api.types.is_re(obj)	Check if the object is a regex pattern instance.
api.types.is_re_compilable(obj)	Check if the object can be compiled into a regex pattern instance.
api.types.is_scalar	Return True if given value is scalar.

Extensions

These are primarily intended for library authors looking to extend pandas objects.

api.extensions.register_dataframe_accessor(name)	Register a custom accessor on DataFrame objects.
api.extensions.register_series_accessor(name)	Register a custom accessor on Series objects.
api.extensions.register_index_accessor(name)	Register a custom accessor on Index objects.
api.extensions.ExtensionDtype	A custom data type, to be paired with an ExtensionArray.
api.extensions.ExtensionArray	Abstract base class for custom 1-D array types.