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/pandas 0.19

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, ...]) 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

Clipboard

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

Excel

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

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]) 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

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.
to_gbq(dataframe, destination_table, project_id) 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.
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 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 conversions

to_numeric(arg[, errors, downcast]) Convert argument to a numeric type.

Top-level dealing with datetimelike

to_datetime(\*args, \*\*kwargs) Convert argument to datetime.
to_timedelta(\*args, \*\*kwargs) 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.

Testing

test Run tests for module using nose.

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 or ndarray-like
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,
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
Series.T return the transpose, which is by definition self
Series.memory_usage([index, deep]) Memory usage of the Series

Conversion

Series.astype(dtype[, copy, raise_on_error]) Cast object to input numpy.dtype
Series.copy([deep]) Make a copy of this objects data.
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 not null.

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__() provide iteration over the values of the Series
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]) 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).

Function application, GroupBy & Window

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 to group, return result as series) or by a series of columns.
Series.rolling(window[, min_periods, freq, ...]) Provides rolling window calculcations.
Series.expanding([min_periods, freq, ...]) Provides expanding transformations.
Series.ewm([com, span, halflife, alpha, ...]) Provides exponential weighted functions

Computations / Descriptive Stats

Series.abs() Return an object with absolute value taken–only applicable to objects that are all numeric.
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, 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, skipna]) Return cumulative max over requested axis.
Series.cummin([axis, skipna]) Return cumulative minimum over requested axis.
Series.cumprod([axis, skipna]) Return cumulative product over requested axis.
Series.cumsum([axis, skipna]) Return cumulative sum over requested axis.
Series.describe([percentiles, include, exclude]) 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 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() Returns the mode(s) of the dataset.
Series.nlargest(\*args, \*\*kwargs) Return the largest n elements.
Series.nsmallest(\*args, \*\*kwargs) 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, 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
Series.std([axis, skipna, level, ddof, ...]) Return sample 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 np.ndarray of unique values in the 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
Series.is_monotonic_increasing Return boolean if values in the object are
Series.is_monotonic_decreasing Return boolean if values in the object are
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(\*args, \*\*kwargs) Return Series with duplicate values removed
Series.duplicated(\*args, \*\*kwargs) 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 based on a date offset.
Series.head([n]) Returns first n rows
Series.idxmax([axis, skipna]) Index of first occurrence of maximum of values.
Series.idxmin([axis, 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 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 axes input function or functions.
Series.rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using 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 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.

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.reorder_levels(order) Rearrange index levels using input order.
Series.sort_values([axis, ascending, ...]) Sort by the values along either axis
Series.sort_index([axis, level, 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, fill_value]) 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[, 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.
Series.asfreq(freq[, method, how, normalize]) Convert TimeSeries to specified frequency.
Series.asof(where[, subset]) The last row without any NaN is taken (or the last row without
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 time series.
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.

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 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.weekday_name The name of day in a week (ex: Friday)
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.is_leap_year Logical indicating 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 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
Series.dt.tz_convert(\*args, \*\*kwargs) Convert tz-aware DatetimeIndex from one time zone to another (using
Series.dt.normalize(\*args, \*\*kwargs) Return DatetimeIndex with times to midnight.
Series.dt.strftime(\*args, \*\*kwargs) Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library.
Series.dt.round(\*args, \*\*kwargs) round the index to the specified freq
Series.dt.floor(\*args, \*\*kwargs) floor the index to the specified freq
Series.dt.ceil(\*args, \*\*kwargs) ceil the index to the specified freq

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()
Series.dt.total_seconds(\*args, \*\*kwargs) 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.<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 using indicated encoding.
Series.str.encode(encoding[, errors]) Encode character string in the Series/Index 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, 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 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([pat, n, expand]) 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) 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

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

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(\*\*kwds) Kernel Density Estimate plot
Series.plot.hist([bins]) Histogram
Series.plot.kde(\*\*kwds) Kernel Density Estimate plot
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.from_csv(path[, sep, parse_dates, ...]) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead).
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_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[, flavor, 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]) 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 Return a list with the row axis labels and column axis labels as the only members.
DataFrame.ndim Number of axes / array dimensions
DataFrame.size number of elements in the NDFrame
DataFrame.shape Return a tuple representing the dimensionality of the DataFrame.
DataFrame.memory_usage([index, deep]) Memory usage of DataFrame columns.

Conversion

DataFrame.astype(dtype[, copy, raise_on_error]) Cast object to input numpy.dtype
DataFrame.convert_objects([convert_dates, ...]) Deprecated.
DataFrame.copy([deep]) Make a copy of this objects data.
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 not null.

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 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]) Returns last n rows
DataFrame.xs(key[, axis, level, drop_level]) 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[, inplace]) 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.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, ...]) 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 to group, return result as series) or by a series of columns.
DataFrame.rolling(window[, min_periods, ...]) Provides rolling window calculcations.
DataFrame.expanding([min_periods, freq, ...]) Provides expanding transformations.
DataFrame.ewm([com, span, halflife, alpha, ...]) Provides exponential weighted functions

Computations / Descriptive Stats

DataFrame.abs() Return an object with absolute value taken–only applicable to objects that are all numeric.
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, 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, skipna]) Return cumulative max over requested axis.
DataFrame.cummin([axis, skipna]) Return cumulative minimum over requested axis.
DataFrame.cumprod([axis, skipna]) Return cumulative product over requested axis.
DataFrame.cumsum([axis, skipna]) Return cumulative sum over requested axis.
DataFrame.describe([percentiles, include, ...]) Generate various summary statistics, excluding NaN values.
DataFrame.diff([periods, axis]) 1st discrete difference of object
DataFrame.eval(expr[, inplace]) Evaluate an expression in the context of the calling DataFrame instance.
DataFrame.kurt([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, ...]) 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, 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
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.

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]) 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]) 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 based on a date offset.
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 indices to myself.
DataFrame.rename([index, columns]) Alter axes input function or functions.
DataFrame.rename_axis(mapper[, axis, copy, ...]) Alter index and / or columns using 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 index value.

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_values(by[, axis, ascending, ...]) Sort by the values along either axis
DataFrame.sort_index([axis, level, ...]) Sort object by labels (along an axis)
DataFrame.sortlevel([level, axis, ...]) Sort multilevel index by chosen axis and primary level.
DataFrame.nlargest(n, columns[, keep]) Get the rows of a DataFrame sorted by the n largest values of columns.
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]) 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, 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.T Transpose index and columns
DataFrame.to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
DataFrame.to_xarray() Return an xarray object from the pandas object.
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 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 TimeSeries to specified frequency.
DataFrame.asof(where[, subset]) The last row without any NaN is taken (or the last row without
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 time series.
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.plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame.plot.<kind>.

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]) Horizontal bar plot
DataFrame.plot.box([by]) Boxplot
DataFrame.plot.density(\*\*kwds) Kernel Density Estimate plot
DataFrame.plot.hexbin(x, y[, C, ...]) Hexbin plot
DataFrame.plot.hist([by, bins]) Histogram
DataFrame.plot.kde(\*\*kwds) Kernel Density Estimate plot
DataFrame.plot.line([x, y]) Line plot
DataFrame.plot.pie([y]) Pie chart
DataFrame.plot.scatter(x, y[, s, c]) Scatter plot
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.

Serialization / IO / Conversion

DataFrame.from_csv(path[, header, sep, ...]) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead).
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) Write the contained data to an HDF5 file using HDFStore.
DataFrame.to_sql(name, con[, flavor, ...]) Write records stored in a DataFrame to a SQL database.
DataFrame.to_dict([orient]) 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, 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 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 Return index label(s) of the internal NDFrame
Panel.ndim Number of axes / array dimensions
Panel.size number of elements in the NDFrame
Panel.shape Return a 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 objects data.
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 not null.

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]) 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, .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[, 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 an object with absolute value taken–only applicable to objects that are all numeric.
Panel.clip([lower, upper, 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, skipna]) Return cumulative max over requested axis.
Panel.cummin([axis, skipna]) Return cumulative minimum over requested axis.
Panel.cumprod([axis, skipna]) Return cumulative product over requested axis.
Panel.cumsum([axis, 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 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) 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]) 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([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, ...]) 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, ...]) 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 index value.

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, 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(\*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) 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_xarray() Return an xarray object from the pandas object.
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, ...]) Panel4D is a 4-Dimensional named container very much like a Panel, but having 4 named dimensions.

Serialization / IO / Conversion

Panel4D.to_xarray() Return an xarray object from the pandas object.

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 Return index label(s) of the internal NDFrame
Panel4D.ndim Number of axes / array dimensions
Panel4D.size number of elements in the NDFrame
Panel4D.shape Return a 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 objects data.
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 not null.

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,
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
Index.T return the transpose, which is by definition self
Index.memory_usage([deep]) Memory usage of my 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(\*args, \*\*kwargs) Return Index with duplicate values removed
Index.duplicated(\*args, \*\*kwargs) Return boolean np.ndarray 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.reindex(target[, method, level, ...]) Create index with target’s values (move/add/delete values as necessary)
Index.repeat(n, \*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 %(klass)s 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() Return Index 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.
Index.fillna([value, downcast]) Fill NA/NaN values with the specified value
Index.dropna([how]) Return Index without NA/NaN values

Conversion

Index.astype(dtype[, copy]) Create an Index with values cast to dtypes.
Index.tolist() return a list of the Index values
Index.to_datetime([dayfirst]) DEPRECATED: use pandas.to_datetime() instead.
Index.to_series(\*\*kwargs) Create a Series with both index and values equal to the index keys

Sorting

Index.argsort(\*args, \*\*kwargs) Returns the indices that would sort the index and its underlying data.
Index.sort_values([return_indexer, ascending]) Return sorted copy of Index

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) 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.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, tolerance]) 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

MultiIndex

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

MultiIndex Components

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.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.is_lexsorted() Return True if the labels are lexicographically sorted
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.

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.weekday_name The name of day in a week (ex: Friday)
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.is_leap_year Logical indicating if the date belongs to a leap year
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.strftime(date_format) Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library.
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
DatetimeIndex.tz_localize(\*args, \*\*kwargs) Localize tz-naive DatetimeIndex to given time zone (using
DatetimeIndex.round(freq, \*args, \*\*kwargs) round the index to the specified freq
DatetimeIndex.floor(freq) floor the index to the specified freq
DatetimeIndex.ceil(freq) ceil the index to the specified freq

Conversion

DatetimeIndex.to_datetime([dayfirst])
DatetimeIndex.to_period([freq]) Cast to PeriodIndex at a particular frequency
DatetimeIndex.to_perioddelta(freq) Calcuates 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]) 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
TimedeltaIndex.round(freq, \*args, \*\*kwargs) round the index to the specified freq
TimedeltaIndex.floor(freq) floor the index to the specified freq
TimedeltaIndex.ceil(freq) ceil the index to the specified freq

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() rolling count of number of non-NaN
Rolling.sum(\*args, \*\*kwargs) rolling sum
Rolling.mean(\*args, \*\*kwargs) rolling mean
Rolling.median(\*\*kwargs) rolling median
Rolling.var([ddof]) rolling variance
Rolling.std([ddof]) rolling standard deviation
Rolling.min(\*args, \*\*kwargs) 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) Unbiased rolling kurtosis
Rolling.apply(func[, args, kwargs]) rolling function apply
Rolling.quantile(quantile, \*\*kwargs) rolling quantile
Window.mean(\*args, \*\*kwargs) window mean
Window.sum(\*args, \*\*kwargs) window sum

Standard expanding window functions

Expanding.count(\*\*kwargs) expanding count of number of non-NaN
Expanding.sum(\*args, \*\*kwargs) expanding sum
Expanding.mean(\*args, \*\*kwargs) expanding mean
Expanding.median(\*\*kwargs) expanding median
Expanding.var([ddof]) expanding variance
Expanding.std([ddof]) expanding standard deviation
Expanding.min(\*args, \*\*kwargs) 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) Unbiased expanding kurtosis
Expanding.apply(func[, args, kwargs]) expanding function apply
Expanding.quantile(quantile, \*\*kwargs) 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

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() 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.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(\*args, \*\*kwargs) 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.agg(arg, \*args, \*\*kwargs) Aggregate using input function or dict of {column ->
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.bfill([limit]) Backward fill the values
DataFrameGroupBy.corr([method, min_periods]) Compute pairwise correlation of columns, excluding NA/null values
DataFrameGroupBy.count() Compute count of group, excluding missing values
DataFrameGroupBy.cov([min_periods]) Compute pairwise covariance of columns, excluding NA/null values
DataFrameGroupBy.cummax([axis, skipna]) Return cumulative max over requested axis.
DataFrameGroupBy.cummin([axis, skipna]) Return cumulative minimum over requested axis.
DataFrameGroupBy.cumprod([axis]) Cumulative product for each group
DataFrameGroupBy.cumsum([axis]) Cumulative sum for each group
DataFrameGroupBy.describe([percentiles, ...]) Generate various summary statistics, excluding NaN values.
DataFrameGroupBy.diff([periods, axis]) 1st discrete difference of object
DataFrameGroupBy.ffill([limit]) Forward fill the values
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.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 Class implementing the .plot attribute for groupby objects
DataFrameGroupBy.quantile([q, axis, ...]) Return values at the given quantile over requested axis, a la numpy.percentile.
DataFrameGroupBy.rank([axis, method, ...]) Compute numerical data ranks (1 through n) along axis.
DataFrameGroupBy.resample(rule, \*args, \*\*kwargs) Provide resampling when using a TimeGrouper
DataFrameGroupBy.shift([periods, freq, axis]) Shift each group by periods observations
DataFrameGroupBy.size() Compute group sizes
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(\*args, \*\*kwargs) Return the largest n elements.
SeriesGroupBy.nsmallest(\*args, \*\*kwargs) Return the smallest n elements.
SeriesGroupBy.nunique([dropna]) Returns number of unique elements in the group
SeriesGroupBy.unique() Return np.ndarray of unique values in the object.
SeriesGroupBy.value_counts([normalize, ...])

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.

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) Apply aggregation function or functions to resampled groups, yielding
Resampler.aggregate(arg, \*args, \*\*kwargs) Apply aggregation function or functions to resampled groups, yielding
Resampler.transform(arg, \*args, \*\*kwargs) Call function producing a like-indexed Series on each group and return

Upsampling

Resampler.ffill([limit]) Forward fill the values
Resampler.backfill([limit]) Backward fill the values
Resampler.bfill([limit]) Backward fill the values
Resampler.pad([limit]) Forward fill the values
Resampler.fillna(method[, limit]) Fill missing values
Resampler.asfreq() return the values at the new freq,
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
Resampler.prod([_method]) Compute prod of group values
Resampler.size([_method]) 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]) Compute sum of group values
Resampler.var([ddof]) Compute variance of groups, excluding missing values

Style

Styler objects are returned by pandas.DataFrame.style.

Constructor

Styler(data[, precision, table_styles, ...]) Helps style a DataFrame or Series according to the data with HTML and CSS.

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.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_caption(caption) Se the caption on a Styler
Styler.set_properties([subset]) Convience 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]) Color the background color proptional to the values in each column.

Style Export and Import

Styler.render() 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.

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.

© 2011–2012 Lambda Foundry, Inc. and PyData Development Team
© 2008–2011 AQR Capital Management, LLC
© 2008–2014 the pandas development team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.19.2/api.html