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

pandas consists of the following things A set of labeled array data structures, the primary of which are Series and DataFrame Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing An integrated group by engine for aggregating and transforming data sets Date range generation (date_range) and custom date offsets enabling the implementation of customized frequencies Input/Output tools: loading tabular data from flat files (CSV, delimited, Excel 2003), and

Series.mod(other, level=None, fill_value=None, axis=0) [source] Modulo of series and other, element-wise (binary operator mod). Equivalent to series % other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a level, matching In

Series.factorize(sort=False, na_sentinel=-1) [source] Encode the object as an enumerated type or categorical variable Parameters: sort : boolean, default False Sort by values na_sentinel: int, default -1 Value to mark ?not found? Returns: labels : the indexer to the original array uniques : the unique Index

TimedeltaIndex.factorize(sort=False, na_sentinel=-1) [source] Encode the object as an enumerated type or categorical variable Parameters: sort : boolean, default False Sort by values na_sentinel: int, default -1 Value to mark ?not found? Returns: labels : the indexer to the original array uniques : the unique Index

CategoricalIndex.remove_categories(*args, **kwargs) [source] Removes the specified categories. removals must be included in the old categories. Values which were in the removed categories will be set to NaN Parameters: removals : category or list of categories The categories which should be removed. inplace : boolean (default: False) Whether or not to remove the categories inplace or return a copy of this categorical with removed categories. Returns: cat : Categorical with removed ca

DataFrame.lookup(row_labels, col_labels) [source] Label-based ?fancy indexing? function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. Parameters: row_labels : sequence The row labels to use for lookup col_labels : sequence The column labels to use for lookup Notes Akin to: result = [] for row, col in zip(row_labels, col_labels): result.append(df.get_value(row, col)) Examples values : ndarray

Styler.bar(subset=None, axis=0, color='#d65f5f', width=100) [source] Color the background color proptional to the values in each column. Excludes non-numeric data by default. New in version 0.17.1. Parameters: subset: IndexSlice, default None a valid slice for data to limit the style application to axis: int color: str width: float A number between 0 or 100. The largest value will cover width percent of the cell?s width Returns: self : Styler

TimedeltaIndex.rename(name, inplace=False) [source] Set new names on index. Defaults to returning new index. Parameters: name : str or list name to set inplace : bool if True, mutates in place Returns: new index (of same type and class...etc) [if inplace, returns None]

DataFrame.loc Purely label-location based indexer for selection by label. .loc[] is primarily label based, but may also be used with a boolean array. Allowed inputs are: A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index). A list or array of labels, e.g. ['a', 'b', 'c']. A slice object with labels, e.g. 'a':'f' (note that contrary to usual python slices, both the start and the stop are included!). A boolean ar