For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 -1.236791 -0.438204 2013-01-05 -1.632181 -0.992838 By lists of integer position locations, similar to the numpy/python style: In [34]: df.iloc[[1, 2, 4], [0 0.280698 2.137642 2000-01-09 0.954301 1.909425 2000-01-10 1.614766 0.667503 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [187]: tsdf.transform({'A':

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas [33]: df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 0.066430 0.886690 2013-01-05 0.996132 0.368752 By lists of integer position locations, similar to the numpy/python style: In [34]: df.iloc[[1, 2, 4], [0 0.073235 2.556972 2000-01-09 1.076272 0.299300 2000-01-10 0.724067 -1.516840 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [187]: tsdf.transform({'A':

over a Series. Using DataFrame.itertuples() now creates itera- tors without internally allocating lists of all elements (GH20783) • Improved performance of Period constructor, additionally benefitting object would return incorrect results (GH23215) • Bug in Series that interpreted string indices as lists of characters when setting datetimelike values (GH23451) • Bug in DataFrame when creating a new column attributes in Python 2 (GH22084) • Bug in DataFrame.replace() raises RecursionError when replacing empty lists (GH22083) • Bug in Series.replace() and DataFrame.replace() when dict is used as the to_replace value

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas (continued from previous page) 2013-01-04 0.743967 -0.470009 2013-01-05 0.969829 -0.538649 By lists of integer position locations, similar to the numpy/python style: In [34]: df.iloc[[1, 2, 4], [0 0.449583 1.652947 2000-01-09 0.425545 1.550571 2000-01-10 0.413806 2.489837 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [192]: tsdf.transform({'A':

female To manually store data in a table, create a DataFrame. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as columns of the structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped in Python. R Python array list lists dictionary or list of objects data be used to replicate most other bysort processing from Stata. For example, the following example lists the first observation in the current sort order by sex/smoker group. bysort sex smoker: list if _n

female To manually store data in a table, create a DataFrame. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as columns of the structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped in Python. R Python array list lists dictionary or list of objects data be used to replicate most other bysort processing from Stata. For example, the following example lists the first observation in the current sort order by sex/smoker group. bysort sex smoker: list if _n

5 (continued from previous page) 2013-01-04 1.657498 0.980165 2013-01-05 1.175223 0.520027 By lists of integer position locations, similar to the numpy/python style: In [34]: df.iloc[[1, 2, 4], [0 Release 1.0.5 To manually store data in a table, create a DataFrame. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as rows of the DataFrame 0.254374 -0.240447 2000-01-09 0.157795 1.791197 2000-01-10 0.030876 1.371900 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [192]: tsdf.transform({'A':

(continued from previous page) 2013-01-04 0.650549 -0.900529 2013-01-05 1.832621 0.631315 By lists of integer position locations, similar to the numpy/python style: In [34]: df.iloc[[1, 2, 4], [0 Release 1.0.4 To manually store data in a table, create a DataFrame. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as rows of the DataFrame 0.254374 -0.240447 2000-01-09 0.157795 1.791197 2000-01-10 0.030876 1.371900 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [192]: tsdf.transform({'A':

(continued from previous page) 2013-01-04 -0.221818 -0.530745 2013-01-05 0.126389 -0.162619 By lists of integer position locations, similar to the numpy/python style: In [34]: df.iloc[[1, 2, 4], [0 Release 1.0.3 To manually store data in a table, create a DataFrame. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as rows of the DataFrame 0.254374 -0.240447 2000-01-09 0.157795 1.791197 2000-01-10 0.030876 1.371900 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [192]: tsdf.transform({'A':

female To manually store data in a table, create a DataFrame. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as columns of the structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped in Python. R Python array list lists dictionary or list of objects data be used to replicate most other bysort processing from Stata. For example, the following example lists the first observation in the current sort order by sex/smoker group. bysort sex smoker: list if _n