result, but retains in result’s Index. (GH10150) • Bug in pd.eval using numexpr engine coerces 1 element numpy array to scalar (GH10546) • Bug in pd.concat with axis=0 when column is of dtype category utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t e.g. list(Series(...)) of timedelta64[ns] would prior to v0.15.0 return np.timedelta64 for each element. These will now be wrapped in Timedelta. Memory Usage Implemented methods to find memory usage

raises a ValueError instead of an Exception (GH21770) • Index subtraction will attempt to operate element-wise instead of raising TypeError (GH19369) • pandas.io.formats.style.Styler supports a number-format 'datetime64[ns]' dtype. In the future, this will return an ndarray with 'object' dtype where each element is a 'pandas.Timestamp' with the correct ˓→'tz'. To accept the future behavior, pass 'dtype=object' with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied by the value 1.882 at once. You do calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied 34 Chapter 1. Getting started pandas: calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied 34 Chapter 1. Getting started pandas: calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was

**kwargs to .agg. The values of **kwargs should be tuples where the first element is the column selection, and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular In [52]: pd.DataFrame(columns=list('ABC')).empty \\\\\\\\\\\\\\\Out[52]: True To evaluate single-element pandas objects in a boolean context, use the method bool(): In [53]: pd.Series([True]).bool() Out[53]:

**kwargs to .agg. The values of **kwargs should be tuples where the first element is the column selection, and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular In [52]: pd.DataFrame(columns=list('ABC')).empty \\\\\\\\\\\\\\\Out[52]: True To evaluate single-element pandas objects in a boolean context, use the method bool(): In [53]: pd.Series([True]).bool() Out[53]:

with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular Out[51]: False In [52]: pd.DataFrame(columns=list('ABC')).empty Out[52]: True To evaluate single-element pandas objects in a boolean context, use the method bool(): In [53]: pd.Series([True]).bool() Out[53]: equals(df2.sort_index()) Out[64]: True Comparing array-like objects You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value: In [65]: pd.Series(['foo'

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied by the value 1.882 at once. You do calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied by the value 1.882 at once. You do calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already