DataFrame.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return unbiased variance over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument Parameters: axis : {index (0), columns (1)} 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, collaps

DataFrame.where(cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True) [source] Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other. Parameters: cond : boolean NDFrame, array or callable If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn?t check it). New in version

DataFrame.values Numpy representation of NDFrame Notes The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type convention, mixing int64 and uint

DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False) [source] Modify DataFrame in place using non-NA values from passed DataFrame. Aligns on indices Parameters: other : DataFrame, or object coercible into a DataFrame join : {?left?}, default ?left? overwrite : boolean, default True If True then overwrite values for common keys in the calling frame filter_func : callable(1d-array) -> 1d-array<boolean>, default None Can choose to replace va

DataFrame.tz_convert(tz, axis=0, level=None, copy=True) [source] Convert tz-aware axis to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None If axis ia a MultiIndex, convert a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data Raises: TypeError If the axis is tz-naive.

DataFrame.tz_localize(*args, **kwargs) [source] Localize tz-naive TimeSeries to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to localize level : int, str, default None If axis ia a MultiIndex, localize a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data ambiguous : ?infer?, bool-ndarray, ?NaT?, default ?raise? ?infer? will attempt to infer fall dst-transition hours based on order bool-ndarra

DataFrame.unstack(level=-1, fill_value=None) [source] Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex). The level involved will automatically get sorted. Parameters: level : int, string, or list of these, default -1 (last level) Level(s)

DataFrame.truediv(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator truediv). Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with thi

DataFrame.truncate(before=None, after=None, axis=None, copy=True) [source] Truncates a sorted NDFrame before and/or after some particular index value. If the axis contains only datetime values, before/after parameters are converted to datetime values. Parameters: before : date Truncate before index value after : date Truncate after index value axis : the truncation axis, defaults to the stat axis copy : boolean, default is True, return a copy of the truncated section Returns: trunc

DataFrame.tshift(periods=1, freq=None, axis=0) [source] Shift the time index, using the index?s frequency if available. Parameters: periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, default None Increment to use from the tseries module or time rule (e.g. ?EOM?) axis : int or basestring Corresponds to the axis that contains the Index Returns: shifted : NDFrame Notes If freq is not specified then tries to use the