All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query The Pivot docs. Partial sums and subtotals Frequency table like plyr in R 7.5.4 Apply Turning embedded lists into a multi-index frame Rolling apply with a DataFrame returning a Series Rolling apply

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query -1.233862 0.777575 12 0.313421 -3.520876 -0.779367 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame In [141]: df = pd.DataFrame(data={'A' : [[2,4,8,16],[100,200],[10

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query -1.233862 0.777575 12 0.313421 -3.520876 -0.779367 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame In [141]: df = pd.DataFrame(data={'A' : [[2,4,8,16],[100,200],[10

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query 233862 0.777575 12 0.313421 -3.520876 -0.779367 7.5.4 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame 446 Chapter 7. Cookbook pandas: powerful Python data analysis toolkit

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query 233862 0.777575 12 0.313421 -3.520876 -0.779367 7.5.4 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame 444 Chapter 7. Cookbook pandas: powerful Python data analysis toolkit

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query 233862 0.777575 12 0.313421 -3.520876 -0.779367 7.5.4 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame In [135]: df = pd.DataFrame(data={'A' : [[2,4,8,16],[100,200],[10

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query ExamYear 2007 74 3 3 2 2008 68 0 3 3 2009 60 2 3 2 7.5.4 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame In [136]: df = pd.DataFrame(data={’A’ : [[2,4,8,16],[100,200],[10

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query ExamYear 2007 74 3 3 2 2008 68 0 3 3 2009 60 2 3 2 7.5.4 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame In [136]: df = pd.DataFrame(data={’A’ : [[2,4,8,16],[100,200],[10

All databases supported by SQLAlchemy can be used, such as PostgreSQL, MySQL, Oracle, Microsoft SQL server (see documentation of SQLAlchemy on included dialects). The functionality of providing DBAPI connection DataFrame.dropna with duplicate indices (GH6355) • Regression in chained getitem indexing with embedded list-like from 0.12 (GH6394) • Float64Index with nans not comparing correctly (GH6401) • eval/query ExamYear 2007 74 3 3 2 2008 68 0 3 3 2009 60 2 3 2 8.5.4 Apply Rolling Apply to Organize - Turning embedded lists into a multi-index frame In [139]: df = pd.DataFrame(data={'A' : [[2,4,8,16],[100,200],[10

rest of pandas objects. Quoting and Escape Characters Quotes (and other escape characters) in embedded fields can be handled in any number of ways. One way is to use backslashes; to properly parse this column in the repr of a pandas data structure. When the column overflows, a “...” placeholder is embedded in the output. display.max_info_columns 100 max_info_columns is used in DataFrame.info method to -0.127041 0.495538 12 -2.503624 0.307157 -0.531790 Apply Rolling apply to organize - Turning embedded lists into a MultiIndex frame In [159]: df = pd.DataFrame(data={'A': [[2, 4, 8, 16], [100, 200]