2.2 Computing rolling pairwise covariances and correlations In financial data analysis and other fields it’s common to compute covariance and correlation matrices for a collection of time series. Often with the same frequency is very fast (important for fast data alignment) • Quick access to date fields via properties such as year, month, etc. • Regularization functions like snap and very fast asof particularly useful for working with various quarterly data common to economics, business, and other fields. Many organizations define quarters relative to the month in which their fiscal year start and ends

The function signature for assign is simply **kwargs. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a Series or NumPy array), or a function documentation for more details. Error handling error_bad_lines [boolean, default True] Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame is often the case that we may want to store date and time data separately, or store various date fields separately. the parse_dates keyword can be used to specify a combination of columns to parse the

The function signature for assign is simply **kwargs. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a Series or NumPy array), or a function MultiIndex on the columns). Error Handling error_bad_lines [boolean, default True] Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame is often the case that we may want to store date and time data separately, or store various date fields separately. the parse_dates keyword can be used to specify a combination of columns to parse the

Release 0.12.0 10.2.2 Computing rolling pairwise correlations In financial data analysis and other fields it’s common to compute correlation matrices for a collection of time series. More difficult is to with the same frequency is very fast (important for fast data alignment) • Quick access to date fields via properties such as year, month, etc. • Regularization functions like snap and very fast asof particularly useful for working with various quarterly data common to economics, business, and other fields. Many organizations define quarters relative to the month in which their fiscal year start and ends

types now return other Index types . . . . . . . . . . . . . . . . . . 23 1.3.2.3 Accessing datetime fields of Index now return Index . . . . . . . . . . . . . . . . . 24 1.3.2.4 pd.unique will now be consistent What’s New pandas: powerful Python data analysis toolkit, Release 0.20.3 – Accessing datetime fields of Index now return Index – pd.unique will now be consistent with extension types – S3 File Handling \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[44]: ˓→'{"schema": {"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"} ˓→,{"name":"B","type":"string"}

4 columns] 11.2.2 Computing rolling pairwise correlations In financial data analysis and other fields it’s common to compute correlation matrices for a collection of time series. More difficult is to with the same frequency is very fast (important for fast data alignment) • Quick access to date fields via properties such as year, month, etc. • Regularization functions like snap and very fast asof particularly useful for working with various quarterly data common to economics, business, and other fields. Many organizations define quarters relative to the month in which their fiscal year start and ends

types now return other Index types . . . . . . . . . . . . . . . . . . 52 1.5.2.3 Accessing datetime fields of Index now return Index . . . . . . . . . . . . . . . . . 53 1.5.2.4 pd.unique will now be consistent columns when specifying na_filter=False (GH5239) • Bug in read_csv() when reading numeric category fields with high cardinality (GH18186) • Bug in DataFrame.to_csv() when the table had MultiIndex columns created with pandas < 0.13.0 – Map on Index types now return other Index types – Accessing datetime fields of Index now return Index – pd.unique will now be consistent with extension types – S3 File Handling

types now return other Index types . . . . . . . . . . . . . . . . . . 21 1.2.2.3 Accessing datetime fields of Index now return Index . . . . . . . . . . . . . . . . . 23 1.2.2.4 pd.unique will now be consistent created with pandas < 0.13.0 – Map on Index types now return other Index types – Accessing datetime fields of Index now return Index – pd.unique will now be consistent with extension types – S3 File Handling \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[44]: ˓→'{"schema": {"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"} ˓→,{"name":"B","type":"string"}

GH8345, GH8471) Warning: Timedelta scalars (and TimedeltaIndex) component fields are not the same as the component fields on a datetime.timedelta object. For example, .seconds on a datetime.timedelta Out[16]: Timedelta(’-1 days +23:59:59.999999’) # a NaT In [17]: Timedelta(’nan’) Out[17]: NaT Access fields for a Timedelta In [18]: td = Timedelta(’1 hour 3m 15.5us’) 1.3. v0.15.0 (October 18, 2014) 17 2.2 Computing rolling pairwise covariances and correlations In financial data analysis and other fields it’s common to compute covariance and correlation matrices for a collection of time series. Often

GH8345, GH8471) Warning: Timedelta scalars (and TimedeltaIndex) component fields are not the same as the component fields on a datetime.timedelta object. For example, .seconds on a datetime.timedelta Out[16]: Timedelta(’-1 days +23:59:59.999999’) # a NaT In [17]: Timedelta(’nan’) Out[17]: NaT Access fields for a Timedelta In [18]: td = Timedelta(’1 hour 3m 15.5us’) In [19]: td.hours Out[19]: 1L In [20]: 2.2 Computing rolling pairwise covariances and correlations In financial data analysis and other fields it’s common to compute covariance and correlation matrices for a collection of time series. Often