Series.dt.tz_convert(*args, **kwargs) [source] Convert tz-aware DatetimeIndex from one time zone to another (using pytz/dateutil) Parameters: tz : string, pytz.timezone, dateutil.tz.tzfile or None Time zone for time. Corresponding timestamps would be converted to time zone of the TimeSeries. None will remove timezone holding UTC time. Returns: normalized : DatetimeIndex Raises: TypeError If DatetimeIndex is tz-naive.

DataFrame.corr(method='pearson', min_periods=1) [source] Compute pairwise correlation of columns, excluding NA/null values Parameters: method : {?pearson?, ?kendall?, ?spearman?} pearson : standard correlation coefficient kendall : Kendall Tau correlation coefficient spearman : Spearman rank correlation min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation Returns: y

DataFrameGroupBy.idxmin(axis=0, skipna=True) Return index of first occurrence of minimum over requested axis. NA/null values are excluded. Parameters: axis : {0 or ?index?, 1 or ?columns?}, default 0 0 or ?index? for row-wise, 1 or ?columns? for column-wise skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA Returns: idxmin : Series See also Series.idxmin Notes This method is the DataFrame version of ndarray.argmin.

Panel.copy(deep=True) [source] Make a copy of this objects data. Parameters: deep : boolean or string, default True Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices or the data are copied. Note that when deep=True data is copied, actual python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data. Returns: copy : type

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) [source] Return object with labels on given axis omitted where alternately any or all of the data are missing Parameters: axis : {0 or ?index?, 1 or ?columns?}, or tuple/list thereof Pass tuple or list to drop on multiple axes how : {?any?, ?all?} any : if any NA values are present, drop that label all : if all values are NA, drop that label thresh : int, default None int value : require that many non-NA value

Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) [source] Fill NA/NaN values using the specified method Parameters: value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list. method : {?backfi

DataFrame.blocks Internal property, property synonym for as_blocks()

DatetimeIndex.get_value_maybe_box(series, key) [source]

DataFrame.asfreq(freq, method=None, how=None, normalize=False) [source] Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Parameters: freq : DateOffset object, or string method : {?backfill?/?bfill?, ?pad?/?ffill?}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): ?pad? / ?ffill?: propagate last valid observation forward to next valid ?backfill? / ?bfill?: u

Resampler.apply(arg, *args, **kwargs) [source] Apply aggregation function or functions to resampled groups, yielding most likely Series but in some cases DataFrame depending on the output of the aggregation function Parameters: func_or_funcs : function or list / dict of functions List/dict of functions will produce DataFrame with column names determined by the function names themselves (list) or the keys in the dict Returns: Series or DataFrame See also transform Notes agg is an ali