Series.dt.to_pydatetime() [source]

Series.empty True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0. See also pandas.Series.dropna, pandas.DataFrame.dropna Notes If NDFrame contains only NaNs, it is still not considered empty. See the example below. Examples An example of an actual empty DataFrame. Notice the index is empty: >>> df_empty = pd.DataFrame({'A' : []}) >>> df_empty Empty DataFrame Columns: [A] Index: [] >>> df_empty.empty True If we only have NaNs in

DatetimeIndex.get_values() [source] return the underlying data as an ndarray

classmethod Series.from_csv(path, sep=', ', parse_dates=True, header=None, index_col=0, encoding=None, infer_datetime_format=False) [source] Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a time Series. This method only differs from pandas.read_csv() in some defaults: index_c

TimedeltaIndex.searchsorted(key, side='left', sorter=None) [source] Find indices where elements should be inserted to maintain order. Find the indices into a sorted TimedeltaIndex self such that, if the corresponding elements in v were inserted before the indices, the order of self would be preserved. Parameters: key : array_like Values to insert into self. side : {?left?, ?right?}, optional If ?left?, the index of the first suitable location found is given. If ?right?, return the last

DatetimeIndex.nanosecond The nanoseconds of the datetime

Panel4D.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False) [source] Modify Panel in place using non-NA values from passed Panel, or object coercible to Panel. Aligns on items Parameters: other : Panel, or object coercible to Panel join : How to join individual DataFrames {?left?, ?right?, ?outer?, ?inner?}, default ?left? overwrite : boolean, default True If True then overwrite values for common keys in the calling panel filter_func : callable(1d-array)

DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] This method returns the maximum of the values in the object. If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax. 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

Styler.highlight_null(null_color='red') [source] Shade the background null_color for missing values. New in version 0.17.1. Parameters: null_color: str Returns: self : Styler

pandas.io.gbq.to_gbq(dataframe, destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None) [source] Write a DataFrame to a Google BigQuery table. THIS IS AN EXPERIMENTAL LIBRARY The main method a user calls to export pandas DataFrame contents to Google BigQuery table. Google BigQuery API Client Library v2 for Python is used. Documentation is available at https://developers.google.com/api-client-library/python/apis/bigquery/v2 Authenticati