'mean', 'B': 'sum'}) Out[165]: A -0.161987 B -1.695863 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will 274648 2000-01-09 1.473561 1.232948 2000-01-10 0.263927 0.846269 Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms. In [187]: tsdf.transform({'A': np.abs expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill

define their own get_window_bounds method on a pandas. api.indexers.BaseIndexer() subclass that will generate the start and end indices used for each window during the rolling aggregation. For more details previously deprecated keyword “time_rule” from (non-public) offsets.generate_range, which has been moved to core.arrays._ranges.generate_range() (GH24157) • DataFrame.loc() or Series.loc() with listlike Release 1.0.0 (continued from previous page) B 1.067739 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will

'mean', 'B': 'sum'}) Out[165]: A -0.092201 B -2.181228 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will 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': np.abs Data operations Operations on columns In Stata, arbitrary math expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data

'mean', 'B': 'sum'}) Out[165]: A 0.158164 B -1.566100 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will 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': np.abs Data operations Operations on columns In Stata, arbitrary math expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data

expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill toolkit, Release 1.1.1 If/then logic In Stata, an if clause can also be used to create new columns. generate bucket = "low" if total_bill < 10 replace bucket = "high" if total_bill >= 10 The same operation do operations on date/datetime columns. generate date1 = mdy(1, 15, 2013) generate date2 = date("Feb152015", "MDY") generate date1_year = year(date1) generate date2_month = month(date2) * shift date

expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill toolkit, Release 1.1.0 If/then logic In Stata, an if clause can also be used to create new columns. generate bucket = "low" if total_bill < 10 replace bucket = "high" if total_bill >= 10 The same operation do operations on date/datetime columns. generate date1 = mdy(1, 15, 2013) generate date2 = date("Feb152015", "MDY") generate date1_year = year(date1) generate date2_month = month(date2) * shift date

Release 1.0.5 (continued from previous page) B -1.803879 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will 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': np.abs expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill

Release 1.0.4 (continued from previous page) B -1.803879 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will 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': np.abs expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill

Release 1.0.3 (continued from previous page) B -1.803879 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will 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': np.abs expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill

(GH23614) • DataFrame.to_html() now accepts render_links as an argument, allowing the user to generate HTML with links to any URLs that appear in the DataFrame. See the section on writing HTML in the a sequence of tu- ples, now raises a TypeError rather than a ValueError (GH24024) • pd.offsets.generate_range() argument time_rule has been removed; use offset instead (GH24157) 26 Chapter 1. What’s 'mean', 'B': 'sum'}) Out[169]: A -0.019676 B 2.896420 dtype: float64 Passing a list-like will generate a DataFrame output. You will get a matrix-like output of all of the aggregators. The output will