MultiIndex.argmin(axis=None) [source] return a ndarray of the minimum argument indexer See also numpy.ndarray.argmin

MultiIndex.argmax(axis=None) [source] return a ndarray of the maximum argument indexer See also numpy.ndarray.argmax

MultiIndex.append(other) [source] Append a collection of Index options together Parameters: other : Index or list/tuple of indices Returns: appended : Index

MultiIndex.any(other=None) [source]

MultiIndex.all(other=None) [source]

This section covers indexing with a MultiIndex and more advanced indexing features. See the Indexing and Selecting Data for general indexing documentation. Warning Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called chained assignment and should be avoided. See Returning a View versus Copy Warning In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the

pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations. Concatenating objects The concat function (in the main pandas namespace) does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes. Note t

The pandas I/O API is a set of top level reader functions accessed like pd.read_csv() that generally return a pandas object. read_csv read_excel read_hdf read_sql read_json read_msgpack (experimental) read_html read_gbq (experimental) read_stata read_sas read_clipboard read_pickle The corresponding writer functions are object methods that are accessed like df.to_csv() to_csv to_excel to_hdf to_sql to_json to_msgpack (experimental) to_html to_gbq (experimental) to_stata to_clipboard to_

We?ll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, and axis labeling / alignment apply across all of the objects. To get started, import numpy and load pandas into your namespace: In [1]: import numpy as np In [2]: import pandas as pd Here is a basic tenet to keep in mind: data alignment is intrinsic. The link between labels and data will not be broken unless done so explic

The easiest way for the majority of users to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Instructions for installing from source, PyPI, various Linux distributions, or a development version are also provided. Python version support Officially Python 2.7, 3.4, 3.5, and 3.6 Installing pandas Trying out pandas, no installation required