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.io.json.json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None) [source] ?Normalize? semi-structured JSON data into a flat table Parameters: data : dict or list of dicts Unserialized JSON objects record_path : string or list of strings, default None Path in each object to list of records. If not passed, data will be assumed to be an array of records meta : list of paths (string or list of strings), default None Fields to use as metadata for each

pandas.infer_freq(index, warn=True) [source] Infer the most likely frequency given the input index. If the frequency is uncertain, a warning will be printed. Parameters: index : DatetimeIndex or TimedeltaIndex if passed a Series will use the values of the series (NOT THE INDEX) warn : boolean, default True Returns: freq : string or None None if no discernible frequency TypeError if the index is not datetime-like ValueError if there are less than three values.

pandas.get_option(pat) = Retrieves the value of the specified option. Available options: display.[chop_threshold, colheader_justify, column_space, date_dayfirst, date_yearfirst, encoding, expand_frame_repr, float_format, height, large_repr] display.latex.[escape, longtable, repr] display.[line_width, max_categories, max_columns, max_colwidth, max_info_columns, max_info_rows, max_rows, max_seq_items, memory_usage, mpl_style, multi_sparse, notebook_repr_html, pprint_nest_depth, precision, sh

pandas.factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None) [source] Encode input values as an enumerated type or categorical variable Parameters: values : ndarray (1-d) Sequence sort : boolean, default False Sort by values na_sentinel : int, default -1 Value to mark ?not found? size_hint : hint to the hashtable sizer Returns: labels : the indexer to the original array uniques : ndarray (1-d) or Index the unique values. Index is returned when passed values is

pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False) [source] Convert categorical variable into dummy/indicator variables Parameters: data : array-like, Series, or DataFrame prefix : string, list of strings, or dict of strings, default None String to append DataFrame column names Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternativly, prefix can be a dictionary mapping colu

pandas.eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=None) [source] Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: +, -, *, /, **, %, // (python engine only) along with the following boolean operations: | (or), & (and), and ~ (not). Additionally, the 'pandas' parser allows the use of and, or, and not with the same semantics as the c

pandas.describe_option(pat, _print_desc=False) = Prints the description for one or more registered options. Call with not arguments to get a listing for all registered options. Available options: display.[chop_threshold, colheader_justify, column_space, date_dayfirst, date_yearfirst, encoding, expand_frame_repr, float_format, height, large_repr] display.latex.[escape, longtable, repr] display.[line_width, max_categories, max_columns, max_colwidth, max_info_columns, max_info_rows, max_rows,

pandas.date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None, closed=None, **kwargs) [source] Return a fixed frequency datetime index, with day (calendar) as the default frequency Parameters: start : string or datetime-like, default None Left bound for generating dates end : string or datetime-like, default None Right bound for generating dates periods : integer or None, default None If None, must specify start and end freq : string or DateOffse

pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, dropna=True, normalize=False) [source] Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed Parameters: index : array-like, Series, or list of arrays/Series Values to group by in the rows columns : array-like, Series, or list of arrays/Series Values to group by