pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, dropna=True, normalize=False) [source] Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed Parameters: index : array-like, Series, or list of arrays/Series Values to group by in the rows columns : array-like, Series, or list of arrays/Series Values to group by

pandas.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True) [source] Concatenate pandas objects along a particular axis with optional set logic along the other axes. Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number Parameters: objs : a sequence or mapping of Series, DataFrame, or Panel obje

pandas.bdate_range(start=None, end=None, periods=None, freq='B', tz=None, normalize=True, name=None, closed=None, **kwargs) [source] Return a fixed frequency datetime index, with business day as the default frequency Parameters: start : string or datetime-like, default None Left bound for generating dates end : string or datetime-like, default None Right bound for generating dates periods : integer or None, default None If None, must specify start and end freq : string or DateOffset,

Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. This is encouraging because it means pandas is not only helping users to handle their data tasks but also that it provides a better starting point for developers to build powerful and more focused data tools. The creation of libraries that complement pandas? functionality also allows pandas development to remain focused around it?s original requirements. This is an

pandas consists of the following things A set of labeled array data structures, the primary of which are Series and DataFrame Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing An integrated group by engine for aggregating and transforming data sets Date range generation (date_range) and custom date offsets enabling the implementation of customized frequencies Input/Output tools: loading tabular data from flat files (CSV, delimited, Excel 2003), and

class pandas.option_context(*args) [source] Context manager to temporarily set options in the with statement context. You need to invoke as option_context(pat, val, [(pat, val), ...]). Examples >>> with option_context('display.max_rows', 10, 'display.max_columns', 5): ...

Overview pandas has an options system that lets you customize some aspects of its behaviour, display-related options being those the user is most likely to adjust. Options have a full ?dotted-style?, case-insensitive name (e.g. display.max_rows). You can get/set options directly as attributes of the top-level options attribute: In [1]: import pandas as pd In [2]: pd.options.display.max_rows Out[2]: 15 In [3]: pd.options.display.max_rows = 999 In [4]: pd.options.display.max_rows Out[4]: 999

class pandas.MultiIndex [source] A multi-level, or hierarchical, index object for pandas objects Parameters: levels : sequence of arrays The unique labels for each level labels : sequence of arrays Integers for each level designating which label at each location sortorder : optional int Level of sortedness (must be lexicographically sorted by that level) names : optional sequence of objects Names for each of the index levels. (name is accepted for compat) copy : boolean, default Fa

MultiIndex.where(cond, other=None) [source]

MultiIndex.view(cls=None) [source] this is defined as a copy with the same identity