API Reference¶

Input/Output¶

Pickling¶

read_pickle(path) Load pickled pandas object (or any other pickled object) from the specified

Flat File¶

`read_table`(filepath_or_buffer[, sep, ...])	Read general delimited file into DataFrame
`read_csv`(filepath_or_buffer[, sep, dialect, ...])	Read CSV (comma-separated) file into DataFrame
`read_fwf`(filepath_or_buffer[, colspecs, widths])	Read a table of fixed-width formatted lines into DataFrame

Clipboard¶

read_clipboard(**kwargs) Read text from clipboard and pass to read_table.

Excel¶

`read_excel`(io[, sheetname])	Read an Excel table into a pandas DataFrame
`ExcelFile.parse`([sheetname, header, ...])	Read an Excel table into 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

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, **kwargs)	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

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.
`to_gbq`(dataframe, destination_table[, ...])	Write a DataFrame to a Google BigQuery table.

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
`StataReader.variable_labels`()	Returns variable labels as a dict, associating each variable name
`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
`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, ...])	Return indices of half-open bins to which each value of x belongs.
`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.
`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 input values as an enumerated type or categorical variable

Top-level missing data¶

`isnull`(obj)	Detect missing values (NaN in numeric arrays, None/NaN in object arrays)
`notnull`(obj)	Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays.

Top-level dealing with datetimelike¶

`to_datetime`(arg[, errors, dayfirst, utc, ...])	Convert argument to datetime.
`to_timedelta`(arg[, unit, box, coerce])	Convert argument to timedelta
`date_range`([start, end, periods, freq, tz, ...])	Return a fixed frequency datetime index, with day (calendar) as the default
`bdate_range`([start, end, periods, freq, tz, ...])	Return a fixed frequency datetime index, with business day as the default
`period_range`([start, end, periods, freq, name])	Return a fixed frequency datetime index, with day (calendar) as the default
`timedelta_range`([start, end, periods, freq, ...])	Return a fixed frequency timedelta index, with day as the default
`infer_freq`(index[, warn])	Infer the most likely frequency given the input index.

Top-level evaluation¶

eval(expr[, parser, engine, truediv, ...]) Evaluate a Python expression as a string using various backends.

Standard moving window functions¶

`rolling_count`(arg, window[, freq, center, how])	Rolling count of number of non-NaN observations inside provided window.
`rolling_sum`(arg, window[, min_periods, ...])	Moving sum.
`rolling_mean`(arg, window[, min_periods, ...])	Moving mean.
`rolling_median`(arg, window[, min_periods, ...])	O(N log(window)) implementation using skip list
`rolling_var`(arg, window[, min_periods, ...])	Numerically stable implementation using Welford’s method.
`rolling_std`(arg, window[, min_periods, ...])	Moving standard deviation.
`rolling_min`(arg, window[, min_periods, ...])	Moving min of 1d array of dtype=float64 along axis=0 ignoring NaNs.
`rolling_max`(arg, window[, min_periods, ...])	Moving max of 1d array of dtype=float64 along axis=0 ignoring NaNs.
`rolling_corr`(arg1[, arg2, window, ...])	Moving sample correlation.
`rolling_corr_pairwise`(df1[, df2, window, ...])	Deprecated.
`rolling_cov`(arg1[, arg2, window, ...])	Unbiased moving covariance.
`rolling_skew`(arg, window[, min_periods, ...])	Unbiased moving skewness.
`rolling_kurt`(arg, window[, min_periods, ...])	Unbiased moving kurtosis.
`rolling_apply`(arg, window, func[, ...])	Generic moving function application.
`rolling_quantile`(arg, window, quantile[, ...])	Moving quantile.
`rolling_window`(arg[, window, win_type, ...])	Applies a moving window of type `window_type` and size `window` on the data.

Standard expanding window functions¶

`expanding_count`(arg[, freq])	Expanding count of number of non-NaN observations.
`expanding_sum`(arg[, min_periods, freq])	Expanding sum.
`expanding_mean`(arg[, min_periods, freq])	Expanding mean.
`expanding_median`(arg[, min_periods, freq])	O(N log(window)) implementation using skip list
`expanding_var`(arg[, min_periods, freq])	Numerically stable implementation using Welford’s method.
`expanding_std`(arg[, min_periods, freq])	Expanding standard deviation.
`expanding_min`(arg[, min_periods, freq])	Moving min of 1d array of dtype=float64 along axis=0 ignoring NaNs.
`expanding_max`(arg[, min_periods, freq])	Moving max of 1d array of dtype=float64 along axis=0 ignoring NaNs.
`expanding_corr`(arg1[, arg2, min_periods, ...])	Expanding sample correlation.
`expanding_corr_pairwise`(df1[, df2, ...])	Deprecated.
`expanding_cov`(arg1[, arg2, min_periods, ...])	Unbiased expanding covariance.
`expanding_skew`(arg[, min_periods, freq])	Unbiased expanding skewness.
`expanding_kurt`(arg[, min_periods, freq])	Unbiased expanding kurtosis.
`expanding_apply`(arg, func[, min_periods, ...])	Generic expanding function application.
`expanding_quantile`(arg, quantile[, ...])	Expanding quantile.

Exponentially-weighted moving window functions¶

`ewma`(arg[, com, span, halflife, ...])	Exponentially-weighted moving average
`ewmstd`(arg[, com, span, halflife, ...])	Exponentially-weighted moving std
`ewmvar`(arg[, com, span, halflife, ...])	Exponentially-weighted moving variance
`ewmcorr`(arg1[, arg2, com, span, halflife, ...])	Exponentially-weighted moving correlation
`ewmcov`(arg1[, arg2, com, span, halflife, ...])	Exponentially-weighted moving covariance

Series¶

Constructor¶

Series([data, index, dtype, name, copy, ...]) One-dimensional ndarray with axis labels (including time series).

Attributes¶

Axes

index: axis labels

`Series.values`	Return Series as ndarray
`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

Conversion¶

`Series.astype`(dtype[, copy, raise_on_error])	Cast object to input numpy.dtype
`Series.copy`([deep])	Make a copy of this object
`Series.isnull`()	Return a boolean same-sized object indicating if the values are null
`Series.notnull`()	Return a boolean same-sized object indicating if the values are

Indexing, iteration¶

`Series.get`(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
`Series.at`	Fast label-based scalar accessor
`Series.iat`	Fast integer location scalar accessor.
`Series.ix`	A primarily label-location based indexer, with integer position fallback.
`Series.loc`	Purely label-location based indexer for selection by label.
`Series.iloc`	Purely integer-location based indexing for selection by position.
`Series.__iter__`()
`Series.iteritems`()	Lazily iterate over (index, value) tuples

For more information on .at, .iat, .ix, .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
`Series.combine_first`(other)	Combine Series values, choosing the calling Series’s values first.
`Series.round`([decimals, out])	Return a with each element rounded to the given number of decimals.
`Series.lt`(other[, axis])
`Series.gt`(other[, axis])
`Series.le`(other[, axis])
`Series.ge`(other[, axis])
`Series.ne`(other[, axis])
`Series.eq`(other[, axis])

Function application, GroupBy¶

`Series.apply`(func[, convert_dtype, args])	Invoke function on values of Series.
`Series.map`(arg[, na_action])	Map values of Series using input correspondence (which can be
`Series.groupby`([by, axis, level, as_index, ...])	Group series using mapper (dict or key function, apply given function

Computations / Descriptive Stats¶

`Series.abs`()	Return an object with absolute value taken.
`Series.all`([axis, bool_only, skipna, level])	Return whether all elements are True over requested 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, out, axis])	Trim values at input threshold(s)
`Series.clip_lower`(threshold[, axis])	Return copy of the input with values below given value(s) truncated
`Series.clip_upper`(threshold[, axis])	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, dtype, out, skipna])	Return cumulative max over requested axis.
`Series.cummin`([axis, dtype, out, skipna])	Return cumulative min over requested axis.
`Series.cumprod`([axis, dtype, out, skipna])	Return cumulative prod over requested axis.
`Series.cumsum`([axis, dtype, out, skipna])	Return cumulative sum over requested axis.
`Series.describe`([percentile_width, ...])	Generate various summary statistics, excluding NaN values.
`Series.diff`([periods])	1st discrete difference of object
`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 Fishers 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`()	Returns the mode(s) of the dataset.
`Series.nlargest`([n, take_last])	Return the largest n elements.
`Series.nsmallest`([n, take_last])	Return the smallest n elements.
`Series.pct_change`([periods, fill_method, ...])	Percent change over given number of periods.
`Series.prod`([axis, skipna, level, numeric_only])	Return the product of the values for the requested axis
`Series.quantile`([q])	Return value at the given quantile, a la numpy.percentile.
`Series.rank`([method, na_option, ascending, pct])	Compute data ranks (1 through n).
`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
`Series.std`([axis, skipna, level, ddof, ...])	Return unbiased standard deviation over requested axis.
`Series.sum`([axis, skipna, level, numeric_only])	Return the sum of the values for the requested axis
`Series.var`([axis, skipna, level, ddof, ...])	Return unbiased variance over requested axis.
`Series.unique`()	Return array of unique values in the object.
`Series.nunique`([dropna])	Return number of unique elements in the object.
`Series.value_counts`([normalize, sort, ...])	Returns object containing counts of unique values.

Reindexing / Selection / Label manipulation¶

`Series.align`(other[, join, axis, level, ...])	Align two object on their axes with the
`Series.drop`(labels[, axis, level, inplace, ...])	Return new object with labels in requested axis removed
`Series.drop_duplicates`([take_last, inplace])	Return Series with duplicate values removed
`Series.duplicated`([take_last])	Return boolean Series denoting duplicate 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
`Series.head`([n])	Returns first n rows
`Series.idxmax`([axis, out, skipna])	Index of first occurrence of maximum of values.
`Series.idxmin`([axis, out, skipna])	Index of first occurrence of minimum of values.
`Series.isin`(values)	Return a boolean `Series` showing whether each element in the `Series` is exactly contained in the passed sequence of `values`.
`Series.last`(offset)	Convenience method for subsetting final periods of time series data
`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, limit])	return an object with matching indicies to myself
`Series.rename`([index])	Alter axes input function or functions.
`Series.reset_index`([level, drop, name, inplace])	Analogous to the `pandas.DataFrame.reset_index()` function, see docstring there.
`Series.sample`([n, frac, replace, weights, ...])	Returns a random sample of items from an axis of object.
`Series.select`(crit[, axis])	Return data corresponding to axis labels matching criteria
`Series.take`(indices[, axis, convert, is_copy])	return Series corresponding to requested indices
`Series.tail`([n])	Returns last n rows
`Series.truncate`([before, after, axis, copy])	Truncates a sorted NDFrame before and/or after some particular dates.
`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.

Missing data handling¶

`Series.dropna`([axis, inplace])	Return Series without null values
`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.order`([na_last, ascending, kind, ...])	Sorts Series object, by value, maintaining index-value link.
`Series.reorder_levels`(order)	Rearrange index levels using input order.
`Series.sort`([axis, ascending, kind, ...])	Sort values and index labels by value.
`Series.sort_index`([ascending])	Sort object by labels (along an axis)
`Series.sortlevel`([level, ascending, ...])	Sort Series with MultiIndex by chosen level.
`Series.swaplevel`(i, j[, copy])	Swap levels i and j in a MultiIndex
`Series.unstack`([level])	Unstack, a.k.a.
`Series.searchsorted`(v[, side, sorter])	Find indices where elements should be inserted to maintain order.

Combining / joining / merging¶

`Series.append`(to_append[, verify_integrity])	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.

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.<property>.

Datetime Properties

`Series.dt.date`	Returns numpy array of datetime.date.
`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.second`	The seconds 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`	Logical indicating if last day of month (defined by frequency)
`Series.dt.is_quarter_start`	Logical indicating if first day of quarter (defined by frequency)
`Series.dt.is_quarter_end`	Logical indicating if last day of quarter (defined by frequency)
`Series.dt.is_year_start`	Logical indicating if first day of year (defined by frequency)
`Series.dt.is_year_end`	Logical indicating if last day of year (defined by frequency)
`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`	get/set the frequncy of the Index

Datetime Methods

`Series.dt.to_period`(args, *kwargs)	Cast to PeriodIndex at a particular frequency
`Series.dt.to_pydatetime`()
`Series.dt.tz_localize`(args, *kwargs)	Localize tz-naive DatetimeIndex to given time zone (using pytz/dateutil),
`Series.dt.tz_convert`(args, *kwargs)	Convert tz-aware DatetimeIndex from one time zone to another (using pytz/dateutil)
`Series.dt.normalize`(args, *kwargs)	Return DatetimeIndex with times to midnight.

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()

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 acccessed like Series.str.<function/property>.

`Series.str.capitalize`()	Convert strings in the Series/Index to be capitalized.
`Series.str.cat`([others, sep, na_rep])	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, ...])	Return boolean Series/`array` whether given pattern/regex is contained in each string in the Series/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 to unicode using indicated encoding.
`Series.str.encode`(encoding[, errors])	Encode character string in the Series/Index to some other encoding using indicated encoding.
`Series.str.endswith`(pat[, na])	Return boolean Series indicating whether each string in the Series/Index ends with passed pattern.
`Series.str.extract`(pat[, flags])	Find groups in each string in the Series using passed regular expression.
`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 lists, tuples, or strings in each element in the Series/Index.
`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, ...])	Deprecated: Find groups in each string in the Series/Index using passed 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, flags])	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 slice of each string in the Series/Index with another string.
`Series.str.split`(args, *kwargs)	Split each string (a la re.split) in the Series/Index by given pattern, propagating NA values.
`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])	Return boolean Series/`array` indicating whether each string in the Series/Index starts with passed 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)	“
`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¶

If the Series is of dtype category, Series.cat can be used to change the 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`	Gets the ordered attribute
`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

To create a Series of dtype category, use cat = s.astype("category").

The following two Categorical constructors are considered API but should only be used when adding ordering information or special categories is need at creation time of the categorical data:

Categorical(values[, categories, ordered, ...]) Represents a categorical variable in classic R / S-plus fashion

Categorical.from_codes(codes, categories[, ...]) Make a Categorical type from codes and categories arrays.

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

Categorical.__array__([dtype]) The numpy array interface.

Plotting¶

`Series.hist`([by, ax, grid, xlabelsize, ...])	Draw histogram of the input series using matplotlib
`Series.plot`(data[, kind, ax, figsize, ...])	Make plots of Series using matplotlib / pylab.

Serialization / IO / Conversion¶

`Series.from_csv`(path[, sep, parse_dates, ...])	Read delimited file into Series
`Series.to_pickle`(path)	Pickle (serialize) object to input file path
`Series.to_csv`(path[, index, sep, na_rep, ...])	Write Series to a comma-separated values (csv) file
`Series.to_dict`()	Convert Series to {label -> value} dict
`Series.to_frame`([name])	Convert Series to DataFrame
`Series.to_hdf`(path_or_buf, key, **kwargs)	activate the HDFStore
`Series.to_sql`(name, con[, flavor, schema, ...])	Write records stored in a DataFrame to a SQL database.
`Series.to_msgpack`([path_or_buf])	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])	Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.

Sparse methods¶

`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

index: row labels

columns: column labels

`DataFrame.as_matrix`([columns])	Convert the frame to its Numpy-array representation.
`DataFrame.dtypes`	Return the dtypes in this object
`DataFrame.ftypes`	Return the ftypes (indication of sparse/dense and dtype) in this object.
`DataFrame.get_dtype_counts`()	Return the counts of dtypes in this object
`DataFrame.get_ftype_counts`()	Return the counts of ftypes in this object
`DataFrame.select_dtypes`([include, exclude])	Return a subset of a DataFrame including/excluding columns based on their `dtype`.
`DataFrame.values`	Numpy representation of NDFrame
`DataFrame.axes`
`DataFrame.ndim`	Number of axes / array dimensions
`DataFrame.size`	number of elements in the NDFrame
`DataFrame.shape`

Conversion¶

`DataFrame.astype`(dtype[, copy, raise_on_error])	Cast object to input numpy.dtype
`DataFrame.convert_objects`([convert_dates, ...])	Attempt to infer better dtype for object columns
`DataFrame.copy`([deep])	Make a copy of this object
`DataFrame.isnull`()	Return a boolean same-sized object indicating if the values are null
`DataFrame.notnull`()	Return a boolean same-sized object indicating if the values are

Indexing, iteration¶

`DataFrame.head`([n])	Returns first n rows
`DataFrame.at`	Fast label-based scalar accessor
`DataFrame.iat`	Fast integer location scalar accessor.
`DataFrame.ix`	A primarily label-location based indexer, with integer position fallback.
`DataFrame.loc`	Purely label-location based indexer for selection by label.
`DataFrame.iloc`	Purely integer-location based indexing for selection by position.
`DataFrame.insert`(loc, column, value[, ...])	Insert column into DataFrame at specified location.
`DataFrame.__iter__`()	Iterate over infor axis
`DataFrame.iteritems`()	Iterator over (column, series) pairs
`DataFrame.iterrows`()	Iterate over rows of DataFrame as (index, Series) pairs.
`DataFrame.itertuples`([index])	Iterate over rows of DataFrame as tuples, with index value
`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])	Returns last n rows
`DataFrame.xs`(key[, axis, level, copy, ...])	Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
`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, **kwargs)	Query the columns of a frame with a boolean expression.

For more information on .at, .iat, .ix, .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.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
`DataFrame.combineAdd`(other)	Add two DataFrame objects and do not propagate
`DataFrame.combine_first`(other)	Combine two DataFrame objects and default to non-null values in frame calling the method.
`DataFrame.combineMult`(other)	Multiply two DataFrame objects and do not propagate NaN values, so if

Function application, GroupBy¶

`DataFrame.apply`(func[, axis, broadcast, ...])	Applies function along input axis of DataFrame.
`DataFrame.applymap`(func)	Apply a function to a DataFrame that is intended to operate elementwise, i.e.
`DataFrame.groupby`([by, axis, level, ...])	Group series using mapper (dict or key function, apply given function

Computations / Descriptive Stats¶

`DataFrame.abs`()	Return an object with absolute value taken.
`DataFrame.all`([axis, bool_only, skipna, level])	Return whether all elements are True over requested axis
`DataFrame.any`([axis, bool_only, skipna, level])	Return whether any element is True over requested axis
`DataFrame.clip`([lower, upper, out, axis])	Trim values at input threshold(s)
`DataFrame.clip_lower`(threshold[, axis])	Return copy of the input with values below given value(s) truncated
`DataFrame.clip_upper`(threshold[, axis])	Return copy of input with values above given value(s) truncated
`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])	Return Series with number of non-NA/null observations over requested axis.
`DataFrame.cov`([min_periods])	Compute pairwise covariance of columns, excluding NA/null values
`DataFrame.cummax`([axis, dtype, out, skipna])	Return cumulative max over requested axis.
`DataFrame.cummin`([axis, dtype, out, skipna])	Return cumulative min over requested axis.
`DataFrame.cumprod`([axis, dtype, out, skipna])	Return cumulative prod over requested axis.
`DataFrame.cumsum`([axis, dtype, out, skipna])	Return cumulative sum over requested axis.
`DataFrame.describe`([percentile_width, ...])	Generate various summary statistics, excluding NaN values.
`DataFrame.diff`([periods, axis])	1st discrete difference of object
`DataFrame.eval`(expr, **kwargs)	Evaluate an expression in the context of the calling DataFrame instance.
`DataFrame.kurt`([axis, skipna, level, ...])	Return unbiased kurtosis over requested axis using Fishers 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, ...])	Percent change over given number of periods.
`DataFrame.prod`([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, numeric_only, method, ...])	Compute numerical data ranks (1 through n) along axis.
`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
`DataFrame.sum`([axis, skipna, level, ...])	Return the sum of the values for the requested axis
`DataFrame.std`([axis, skipna, level, ddof, ...])	Return unbiased standard deviation over requested axis.
`DataFrame.var`([axis, skipna, level, ddof, ...])	Return unbiased variance over requested axis.

Reindexing / Selection / Label manipulation¶

`DataFrame.add_prefix`(prefix)	Concatenate prefix string with panel items names.
`DataFrame.add_suffix`(suffix)	Concatenate suffix string with panel items names
`DataFrame.align`(other[, join, axis, level, ...])	Align two object on their axes with the
`DataFrame.drop`(labels[, axis, level, ...])	Return new object with labels in requested axis removed
`DataFrame.drop_duplicates`(args, *kwargs)	Return DataFrame with duplicate rows removed, optionally only
`DataFrame.duplicated`(args, *kwargs)	Return boolean Series denoting duplicate rows, optionally only
`DataFrame.equals`(other)	Determines if two NDFrame objects contain the same elements.
`DataFrame.filter`([items, like, regex, axis])	Restrict the info axis to set of items or wildcard
`DataFrame.first`(offset)	Convenience method for subsetting initial periods of time series data
`DataFrame.head`([n])	Returns 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
`DataFrame.reindex`([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 indicies to myself
`DataFrame.rename`([index, columns])	Alter axes input function or functions.
`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, ...])	Returns a random sample of items from an axis of object.
`DataFrame.select`(crit[, axis])	Return data corresponding to axis labels matching criteria
`DataFrame.set_index`(keys[, drop, append, ...])	Set the DataFrame index (row labels) using one or more existing columns.
`DataFrame.tail`([n])	Returns last n rows
`DataFrame.take`(indices[, axis, convert, is_copy])	Analogous to ndarray.take
`DataFrame.truncate`([before, after, axis, copy])	Truncates a sorted NDFrame before and/or after some particular dates.

Missing data handling¶

`DataFrame.dropna`([axis, how, thresh, ...])	Return object with labels on given axis omitted where alternately any
`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’.

Reshaping, sorting, transposing¶

`DataFrame.pivot`([index, columns, values])	Reshape data (produce a “pivot” table) based on column values.
`DataFrame.reorder_levels`(order[, axis])	Rearrange index levels using input order.
`DataFrame.sort`([columns, axis, ascending, ...])	Sort DataFrame either by labels (along either axis) or by the values in
`DataFrame.sort_index`([axis, by, ascending, ...])	Sort DataFrame either by labels (along either axis) or by the values in
`DataFrame.sortlevel`([level, axis, ...])	Sort multilevel index by chosen axis and primary level.
`DataFrame.swaplevel`(i, j[, axis])	Swap levels i and j in a MultiIndex on a particular axis
`DataFrame.stack`([level, dropna])	Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels.
`DataFrame.unstack`([level])	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.T`	Transpose index and columns
`DataFrame.to_panel`()	Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
`DataFrame.transpose`()	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 all the original columns in addition to the new 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 DataFrame in place using non-NA values from passed DataFrame.

Time series-related¶

`DataFrame.asfreq`(freq[, method, how, normalize])	Convert all TimeSeries inside to specified frequency using DateOffset objects.
`DataFrame.shift`([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
`DataFrame.first_valid_index`()	Return label for first non-NA/null value
`DataFrame.last_valid_index`()	Return label for last non-NA/null value
`DataFrame.resample`(rule[, how, axis, ...])	Convenience method for frequency conversion and resampling of regular time-series data.
`DataFrame.to_period`([freq, axis, copy])	Convert DataFrame from DatetimeIndex to PeriodIndex with desired
`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`(args, *kwargs)	Localize tz-naive TimeSeries to target time zone

Plotting¶

`DataFrame.boxplot`([column, by, ax, ...])	Make a box plot from DataFrame column optionally grouped by some columns or
`DataFrame.hist`(data[, column, by, grid, ...])	Draw histogram of the DataFrame’s series using matplotlib / pylab.
`DataFrame.plot`(data[, x, y, kind, ax, ...])	Make plots of DataFrame using matplotlib / pylab.

Serialization / IO / Conversion¶

`DataFrame.from_csv`(path[, header, sep, ...])	Read delimited file into DataFrame
`DataFrame.from_dict`(data[, orient, dtype])	Construct DataFrame from dict of array-like or dicts
`DataFrame.from_items`(items[, columns, orient])	Convert (key, value) pairs to DataFrame.
`DataFrame.from_records`(data[, index, ...])	Convert structured or record ndarray to DataFrame
`DataFrame.info`([verbose, buf, max_cols, ...])	Concise summary of a DataFrame.
`DataFrame.to_pickle`(path)	Pickle (serialize) object to input file path
`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)	activate the HDFStore
`DataFrame.to_sql`(name, con[, flavor, ...])	Write records stored in a DataFrame to a SQL database.
`DataFrame.to_dict`(args, *kwargs)	Convert DataFrame to dictionary.
`DataFrame.to_excel`(excel_writer[, ...])	Write DataFrame to a 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_latex`([buf, columns, ...])	Render a DataFrame to a tabular environment table.
`DataFrame.to_stata`(fname[, convert_dates, ...])	A class for writing Stata binary dta files from array-like objects
`DataFrame.to_msgpack`([path_or_buf])	msgpack (serialize) object to input file path
`DataFrame.to_gbq`(destination_table[, ...])	Write a DataFrame to a Google BigQuery table.
`DataFrame.to_records`([index, convert_datetime64])	Convert DataFrame to 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])	Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.

Panel¶

Constructor¶

Panel([data, items, major_axis, minor_axis, ...]) 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`	Numpy representation of NDFrame
`Panel.axes`	index(es) of the NDFrame
`Panel.ndim`	Number of axes / array dimensions
`Panel.size`	number of elements in the NDFrame
`Panel.shape`	tuple of axis dimensions
`Panel.dtypes`	Return the dtypes in this object
`Panel.ftypes`	Return the ftypes (indication of sparse/dense and dtype) in this object.
`Panel.get_dtype_counts`()	Return the counts of dtypes in this object
`Panel.get_ftype_counts`()	Return the counts of ftypes in this object

Conversion¶

`Panel.astype`(dtype[, copy, raise_on_error])	Cast object to input numpy.dtype
`Panel.copy`([deep])	Make a copy of this object
`Panel.isnull`()	Return a boolean same-sized object indicating if the values are null
`Panel.notnull`()	Return a boolean same-sized object indicating if the values are

Getting and setting¶

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

Indexing, iteration, slicing¶

`Panel.at`	Fast label-based scalar accessor
`Panel.iat`	Fast integer location scalar accessor.
`Panel.ix`	A primarily label-location based indexer, with integer position fallback.
`Panel.loc`	Purely label-location based indexer for selection by label.
`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, copy])	Return slice of panel along selected axis
`Panel.major_xs`(key[, copy])	Return slice of panel along major axis
`Panel.minor_xs`(key[, copy])	Return slice of panel along minor axis

For more information on .at, .iat, .ix, .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)	Wrapper for comparison method lt
`Panel.gt`(other)	Wrapper for comparison method gt
`Panel.le`(other)	Wrapper for comparison method le
`Panel.ge`(other)	Wrapper for comparison method ge
`Panel.ne`(other)	Wrapper for comparison method ne
`Panel.eq`(other)	Wrapper for comparison method eq

Function application, GroupBy¶

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

Computations / Descriptive Stats¶

`Panel.abs`()	Return an object with absolute value taken.
`Panel.clip`([lower, upper, out, axis])	Trim values at input threshold(s)
`Panel.clip_lower`(threshold[, axis])	Return copy of the input with values below given value(s) truncated
`Panel.clip_upper`(threshold[, axis])	Return copy of input with values above given value(s) truncated
`Panel.count`([axis])	Return number of observations over requested axis.
`Panel.cummax`([axis, dtype, out, skipna])	Return cumulative max over requested axis.
`Panel.cummin`([axis, dtype, out, skipna])	Return cumulative min over requested axis.
`Panel.cumprod`([axis, dtype, out, skipna])	Return cumulative prod over requested axis.
`Panel.cumsum`([axis, dtype, out, skipna])	Return cumulative sum over requested 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, ...])	Percent change over given number of periods.
`Panel.prod`([axis, skipna, level, numeric_only])	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
`Panel.sum`([axis, skipna, level, numeric_only])	Return the sum of the values for the requested axis
`Panel.std`([axis, skipna, level, ddof, ...])	Return unbiased 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)	Concatenate prefix string with panel items names.
`Panel.add_suffix`(suffix)	Concatenate suffix string with panel items names
`Panel.drop`(labels[, axis, level, inplace, ...])	Return new object with labels in requested axis removed
`Panel.equals`(other)	Determines if two NDFrame objects contain the same elements.
`Panel.filter`([items, like, regex, axis])	Restrict the info axis to set of items or wildcard
`Panel.first`(offset)	Convenience method for subsetting initial periods of time series data
`Panel.last`(offset)	Convenience method for subsetting final periods of time series data
`Panel.reindex`([items, major_axis, minor_axis])	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, limit])	return an object with matching indicies to myself
`Panel.rename`([items, major_axis, minor_axis])	Alter axes input function or functions.
`Panel.sample`([n, frac, replace, weights, ...])	Returns a random sample of items from an axis of object.
`Panel.select`(crit[, axis])	Return data corresponding to axis labels matching criteria
`Panel.take`(indices[, axis, convert, is_copy])	Analogous to ndarray.take
`Panel.truncate`([before, after, axis, copy])	Truncates a sorted NDFrame before and/or after some particular dates.

Missing data handling¶

`Panel.dropna`([axis, how, inplace])	Drop 2D from panel, holding passed axis constant
`Panel.fillna`([value, method, axis, inplace, ...])	Fill NA/NaN values using the specified method

Reshaping, sorting, transposing¶

`Panel.sort_index`([axis, 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 all TimeSeries inside to specified frequency using DateOffset objects.
`Panel.shift`(args, *kwargs)	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 regular time-series data.
`Panel.tz_convert`(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
`Panel.tz_localize`(args, *kwargs)	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)	Pickle (serialize) object to input file path
`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)	activate the HDFStore
`Panel.to_json`([path_or_buf, orient, ...])	Convert the object to a JSON string.
`Panel.to_sparse`([fill_value, kind])	Convert to SparsePanel
`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])	Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.

Panel4D¶

Constructor¶

Panel4D([data, labels, items, major_axis, ...]) Represents a 4 dimensional structured

Attributes and underlying data¶

Axes

labels: axis 1; each label corresponds to a Panel contained inside

items: axis 2; each item corresponds to a DataFrame contained inside

major_axis: axis 3; the index (rows) of each of the DataFrames

minor_axis: axis 4; the columns of each of the DataFrames

`Panel4D.values`	Numpy representation of NDFrame
`Panel4D.axes`	index(es) of the NDFrame
`Panel4D.ndim`	Number of axes / array dimensions
`Panel4D.size`	number of elements in the NDFrame
`Panel4D.shape`	tuple of axis dimensions
`Panel4D.dtypes`	Return the dtypes in this object
`Panel4D.ftypes`	Return the ftypes (indication of sparse/dense and dtype) in this object.
`Panel4D.get_dtype_counts`()	Return the counts of dtypes in this object
`Panel4D.get_ftype_counts`()	Return the counts of ftypes in this object

Conversion¶

`Panel4D.astype`(dtype[, copy, raise_on_error])	Cast object to input numpy.dtype
`Panel4D.copy`([deep])	Make a copy of this object
`Panel4D.isnull`()	Return a boolean same-sized object indicating if the values are null
`Panel4D.notnull`()	Return a boolean same-sized object indicating if the values are

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
`Index.is_monotonic_decreasing`	return if the index is monotonic decreasing (only equal or
`Index.is_unique`
`Index.has_duplicates`
`Index.dtype`
`Index.inferred_type`
`Index.is_all_dates`
`Index.shape`	return a tuple of the shape of the underlying data
`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.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

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`([names, name, dtype, deep])	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`([take_last])	Return Index with duplicate values removed
`Index.duplicated`([take_last])	Return boolean np.array denoting duplicate 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
`Index.insert`(loc, item)	Make new Index inserting new item at location.
`Index.min`()	The minimum value of the object
`Index.max`()	The maximum value of the object
`Index.order`([return_indexer, ascending])	Return sorted copy of Index
`Index.reindex`(target[, method, level, limit])	Create index with target’s values (move/add/delete values as necessary)
`Index.repeat`(n)	return a new Index of the values repeated n times
`Index.take`(indexer[, axis])	return a new Index of the values selected by the indexer
`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`()	Return array of unique values in the object.
`Index.nunique`([dropna])	Return number of unique elements in the object.
`Index.value_counts`([normalize, sort, ...])	Returns object containing counts of unique values.

Conversion¶

`Index.astype`(dtype)
`Index.tolist`()	return a list of the Index values
`Index.to_datetime`([dayfirst])	For an Index containing strings or datetime.datetime objects, attempt
`Index.to_series`(**kwargs)	Create a Series with both index and values equal to the index keys

Sorting¶

`Index.argsort`(args, *kwargs)	return an ndarray indexer of the underlying data
`Index.order`([return_indexer, ascending])	Return sorted copy of Index
`Index.sort`(args, *kwargs)

Time-specific operations¶

Index.shift([periods, freq]) Shift Index containing datetime objects by input number of periods and

Combining / joining / set operations¶

`Index.append`(other)	Append a collection of Index options together
`Index.join`(other[, how, level, return_indexers])	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)	Compute sorted set difference of two Index objects
`Index.sym_diff`(other[, result_name])	Compute the sorted symmetric difference of two Index objects.

Selecting¶

`Index.get_indexer`(target[, method, limit])	Compute indexer and mask for new index given the current index.
`Index.get_indexer_non_unique`(target)	return an indexer suitable for taking from a non unique index
`Index.get_level_values`(level)	Return vector of label values for requested level, equal to the length
`Index.get_loc`(key[, method])	Get integer location for requested label
`Index.get_value`(series, key)	Fast lookup of value from 1-dimensional ndarray.
`Index.isin`(values[, level])	Compute boolean array of whether each index value is found in the passed set of values.
`Index.slice_indexer`([start, end, step, kind])	For an ordered Index, compute the slice indexer for input labels and
`Index.slice_locs`([start, end, step, kind])	Compute slice locations for input labels.

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

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 datetime.date.
`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`	get/set the frequncy of the Index
`DatetimeIndex.freqstr`	return the frequency object as a string if its set, otherwise None
`DatetimeIndex.is_month_start`	Logical indicating if first day of month (defined by frequency)
`DatetimeIndex.is_month_end`	Logical indicating if last day of month (defined by frequency)
`DatetimeIndex.is_quarter_start`	Logical indicating if first day of quarter (defined by frequency)
`DatetimeIndex.is_quarter_end`	Logical indicating if last day of quarter (defined by frequency)
`DatetimeIndex.is_year_start`	Logical indicating if first day of year (defined by frequency)
`DatetimeIndex.is_year_end`	Logical indicating if last day of year (defined by frequency)
`DatetimeIndex.inferred_freq`

Selecting¶

`DatetimeIndex.indexer_at_time`(time[, asof])	Select values at particular time of day (e.g.
`DatetimeIndex.indexer_between_time`(...[, ...])	Select values between particular times of day (e.g., 9:00-9:30AM)

Time-specific operations¶

`DatetimeIndex.normalize`()	Return DatetimeIndex with times to midnight.
`DatetimeIndex.snap`([freq])	Snap time stamps to nearest occurring frequency
`DatetimeIndex.tz_convert`(tz)	Convert tz-aware DatetimeIndex from one time zone to another (using pytz/dateutil)
`DatetimeIndex.tz_localize`(args, *kwargs)	Localize tz-naive DatetimeIndex to given time zone (using pytz/dateutil),

Conversion¶

`DatetimeIndex.to_datetime`([dayfirst])
`DatetimeIndex.to_period`([freq])	Cast to PeriodIndex at a particular frequency
`DatetimeIndex.to_pydatetime`()	Return DatetimeIndex as object ndarray of datetime.datetime objects
`DatetimeIndex.to_series`([keep_tz])	Create a Series with both index and values equal to the index keys

TimedeltaIndex¶

TimedeltaIndex Immutable ndarray of timedelta64 data, represented internally as int64, and

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`

Conversion¶

`TimedeltaIndex.to_pytimedelta`()	Return TimedeltaIndex as object ndarray of datetime.timedelta objects
`TimedeltaIndex.to_series`(**kwargs)	Create a Series with both index and values equal to the index keys

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 and combine results together in an intelligent way.
`GroupBy.aggregate`(func, args, *kwargs)
`GroupBy.transform`(func, args, *kwargs)

Computations / Descriptive Stats¶

`GroupBy.count`([axis])
`GroupBy.cumcount`([ascending])	Number each item in each group from 0 to the length of that group - 1.
`GroupBy.first`()	Compute first of group values
`GroupBy.head`([n])	Returns first n rows of each group.
`GroupBy.last`()	Compute last of group values
`GroupBy.max`()	Compute max of group values
`GroupBy.mean`()	Compute mean of groups, excluding missing values
`GroupBy.median`()	Compute median of groups, excluding missing values
`GroupBy.min`()	Compute min of group values
`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
`GroupBy.prod`()	Compute prod of group values
`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`()	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.bfill`([axis, inplace, ...])	Synonym for NDFrame.fillna(method=’bfill’)
`DataFrameGroupBy.cummax`([axis, dtype, out, ...])	Return cumulative max over requested axis.
`DataFrameGroupBy.cummin`([axis, dtype, out, ...])	Return cumulative min over requested axis.
`DataFrameGroupBy.cumprod`([axis, dtype, out, ...])	Return cumulative prod over requested axis.
`DataFrameGroupBy.cumsum`([axis, dtype, out, ...])	Return cumulative sum over requested axis.
`DataFrameGroupBy.describe`([...])	Generate various summary statistics, excluding NaN values.
`DataFrameGroupBy.all`([axis, bool_only, ...])	Return whether all elements are True over requested axis
`DataFrameGroupBy.any`([axis, bool_only, ...])	Return whether any element is True over requested axis
`DataFrameGroupBy.corr`([method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values
`DataFrameGroupBy.cov`([min_periods])	Compute pairwise covariance of columns, excluding NA/null values
`DataFrameGroupBy.diff`([periods, axis])	1st discrete difference of object
`DataFrameGroupBy.ffill`([axis, inplace, ...])	Synonym for NDFrame.fillna(method=’ffill’)
`DataFrameGroupBy.fillna`([value, method, ...])	Fill NA/NaN values using the specified method
`DataFrameGroupBy.hist`(data[, column, by, ...])	Draw histogram of the DataFrame’s series using matplotlib / pylab.
`DataFrameGroupBy.idxmax`([axis, skipna])	Return index of first occurrence of maximum over requested axis.
`DataFrameGroupBy.idxmin`([axis, skipna])	Return index of first occurrence of minimum over requested axis.
`DataFrameGroupBy.irow`(i[, copy])
`DataFrameGroupBy.mad`([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis
`DataFrameGroupBy.pct_change`([periods, ...])	Percent change over given number of periods.
`DataFrameGroupBy.plot`(data[, x, y, kind, ...])	Make plots of DataFrame using matplotlib / pylab.
`DataFrameGroupBy.quantile`([q, axis, ...])	Return values at the given quantile over requested axis, a la numpy.percentile.
`DataFrameGroupBy.rank`([axis, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
`DataFrameGroupBy.resample`(rule[, how, axis, ...])	Convenience method for frequency conversion and resampling of regular time-series data.
`DataFrameGroupBy.shift`([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
`DataFrameGroupBy.skew`([axis, skipna, level, ...])	Return unbiased skew over requested axis
`DataFrameGroupBy.take`(indices[, axis, ...])	Analogous to ndarray.take
`DataFrameGroupBy.tshift`([periods, freq, axis])	Shift the time index, using the index’s frequency if available

The following methods are available only for SeriesGroupBy objects.

`SeriesGroupBy.nlargest`([n, take_last])	Return the largest n elements.
`SeriesGroupBy.nsmallest`([n, take_last])	Return the smallest n elements.
`SeriesGroupBy.nunique`([dropna])	Return number of unique elements in the object.
`SeriesGroupBy.unique`()	Return array of unique values in the object.
`SeriesGroupBy.value_counts`([normalize, ...])	Returns object containing counts of unique values.

The following methods are available only for DataFrameGroupBy objects.

`DataFrameGroupBy.corrwith`(other[, axis, drop])	Compute pairwise correlation between rows or columns of two DataFrame objects.
`DataFrameGroupBy.boxplot`(grouped[, ...])	Make box plots from DataFrameGroupBy data.

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.

`Series.asfreq`(freq[, method, how, normalize])	Convert all TimeSeries inside to specified frequency using DateOffset objects.
`Series.asof`(where)	Return last good (non-NaN) value in TimeSeries if value is NaN for requested date.
`Series.shift`([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
`Series.first_valid_index`()	Return label for first non-NA/null value
`Series.last_valid_index`()	Return label for last non-NA/null value
`Series.resample`(rule[, how, axis, ...])	Convenience method for frequency conversion and resampling of regular time-series data.
`Series.tz_convert`(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
`Series.tz_localize`(args, *kwargs)	Localize tz-naive TimeSeries to target time zone

Navigation

Table Of Contents

Search

API Reference¶

Input/Output¶

Pickling¶

Flat File¶

Clipboard¶

Excel¶

JSON¶

HTML¶

HDFStore: PyTables (HDF5)¶

SQL¶

Google BigQuery¶

STATA¶

General functions¶

Data manipulations¶

Top-level missing data¶

Top-level dealing with datetimelike¶

Top-level evaluation¶

Standard moving window functions¶

Standard expanding window functions¶

Exponentially-weighted moving window functions¶

Series¶

Constructor¶

Attributes¶

Conversion¶

Indexing, iteration¶

Binary operator functions¶

Function application, GroupBy¶

Computations / Descriptive Stats¶

Reindexing / Selection / Label manipulation¶

Missing data handling¶

Reshaping, sorting¶

Combining / joining / merging¶

Time series-related¶

Datetimelike Properties¶

String handling¶

Categorical¶

Plotting¶

Serialization / IO / Conversion¶

Sparse methods¶

DataFrame¶

Constructor¶

Attributes and underlying data¶

Conversion¶

Indexing, iteration¶

Binary operator functions¶

Function application, GroupBy¶

Computations / Descriptive Stats¶

Reindexing / Selection / Label manipulation¶

Missing data handling¶

Reshaping, sorting, transposing¶

Combining / joining / merging¶

Time series-related¶

Plotting¶

Serialization / IO / Conversion¶

Panel¶

Constructor¶

Attributes and underlying data¶

Conversion¶

Getting and setting¶

Indexing, iteration, slicing¶

Binary operator functions¶

Function application, GroupBy¶

Computations / Descriptive Stats¶

Reindexing / Selection / Label manipulation¶

Missing data handling¶

Reshaping, sorting, transposing¶

Combining / joining / merging¶

Time series-related¶

Serialization / IO / Conversion¶

Panel4D¶

Constructor¶

Attributes and underlying data¶

Conversion¶

Index¶

Attributes¶

Modifying and Computations¶

Conversion¶