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.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.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

pandas.read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, i

Resampler.ohlc(_method='ohlc', *args, **kwargs) [source] Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

TimedeltaIndex.sort_values(return_indexer=False, ascending=True) [source] Return sorted copy of Index

Index.all(*args, **kwargs) [source] Return whether all elements are True Parameters: All arguments to numpy.all are accepted. Returns: all : bool or array_like (if axis is specified) A single element array_like may be converted to bool.

Grouper.groups

Series.cov(other, min_periods=None) [source] Compute covariance with Series, excluding missing values Parameters: other : Series min_periods : int, optional Minimum number of observations needed to have a valid result Returns: covariance : float Normalized by N-1 (unbiased estimator).

DatetimeIndex.size return the number of elements in the underlying data