Improved performance of where() for Categorical data (GH24077) • Improved performance of iterating over a Series. Using DataFrame.itertuples() now creates itera- tors without internally allocating lists installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size may involve copying data and coercing values. See dtypes for more. to_numpy() gives some control over the dtype of the resulting numpy.ndarray. For example, consider date- times with timezones. NumPy

Locations and Names . . . . . . . . . . . . . . . . . . . . . . . 1028 24.1.1.3 General Parsing Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029 24.1.1.4 NA and Missing Data Handling (GH18413) • Bug in DataFrame.to_latex() with longtable=True where a latex multicolumn always spanned over three columns (GH17959) 1.1.5.4 Plotting • Bug in DataFrame.plot() and Series.plot() with DatetimeIndex resampling frequecy is 12h or higher (GH15549) • Bug in pd.DataFrameGroupBy.count() when counting over a datetimelike column (GH13393) • Bug in rolling.var where calculation is inaccurate with a zero-valued

The returned dtype of unique() now matches the input dtype. (GH27874) • Changed the default configuration value for options.matplotlib.register_converters from True to "auto" (GH18720). Now, pandas custom • Performance improvement in DataFrame.select_dtypes() by using vectorization instead of iterating over a loop (GH28317) • Performance improvement in Categorical.searchsorted() and CategoricalIndex. searchsorted() • Bug in DataFrame.rolling() not allowing for rolling over datetimes when axis=1 (GH28192) • Bug in DataFrame.rolling() not allowing rolling over multi-index levels (GH15584). 30 Chapter 1. What’s new

data. To introduction tutorial To user guide Straight to tutorial... There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size DataFrame with a new column name in between the []. • Operations are element-wise, no need to loop over rows. • Use rename with a dictionary or function to rename row labels or column names. The user

data. To introduction tutorial To user guide Straight to tutorial... There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size DataFrame with a new column name in between the []. • Operations are element-wise, no need to loop over rows. • Use rename with a dictionary or function to rename row labels or column names. The user

normalizes the provided input dict to all nested levels. The new max_level parameter provides more control over which level to end normalization (GH23843): The repr now looks like this: In [9]: from pandas.io installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size may involve copying data and coercing values. See dtypes for more. to_numpy() gives some control over the dtype of the resulting numpy.ndarray. For example, consider date- times with timezones. NumPy

normalizes the provided input dict to all nested levels. The new max_level parameter provides more control over which level to end normalization (GH23843): The repr now looks like this: In [9]: from pandas.io installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size may involve copying data and coercing values. See dtypes for more. to_numpy() gives some control over the dtype of the resulting numpy.ndarray. For example, consider date- times with timezones. NumPy

data. To introduction tutorial To user guide Straight to tutorial... There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size combining multiple conditional statements, each condition must be surrounded by parentheses (). More- over, you can not use or/and but need to use the or operator | and the and operator &. See the dedicated

data. To introduction tutorial To user guide Straight to tutorial... There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size combining multiple conditional statements, each condition must be surrounded by parentheses (). More- over, you can not use or/and but need to use the or operator | and the and operator &. See the dedicated

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 17.3 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 17.4 Miscellaneous release. 1.1 v.0.7.1 (February 29, 2012) This release includes a few new features and addresses over a dozen bugs in 0.7.0. 1.1.1 New features • Add to_clipboard function to pandas namespace for writing toolkit, Release 0.7.1 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important to note that these contributions are typically