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pandas: powerful Python data analysis toolkit, Release 0.18.1
Whether to perform the operation in place on the data
axis : alignment axis if needed, default None
level : alignment level if needed,default None
try_cast : boolean,default False
try to cast the result back to the input type (if possible),
raise_on_error : boolean,default True
Whether to raise on invalid data types (e.g. trying to where on strings)
Returns wh : same type as caller
pandas.Series.max
Series.max(axis=None, skipna=None,level=None, numeric_only=None,**kwargs)
This method returns the maximum of the values inthe object. If you want the index ofthe maximum,
use idxmax. This is the equivalent of the numpy.ndarray method argmax.
Parameters axis : {index (0)}
skipna : boolean,default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA
level : int or level name,default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into
ascalar
numeric_only : boolean, default None
Include only float, int, boolean data. If None, will attempt to use everything, then use
only numeric data
Returns max : scalar or Series (iflevel specified)
pandas.Series.mean
Series.mean(axis=None, skipna=None,level=None, numeric_only=None,**kwargs)
Return the mean of the values for the requested axis
Parameters axis : {index (0)}
skipna : boolean,default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA
level : int or level name,default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into
ascalar
numeric_only : boolean, default None
Include only float, int, boolean data. If None, will attempt to use everything, then use
only numeric data
Returns mean : scalar orSeries (if level specified)
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Chapter 35. API Reference