DataFrame.equals(other) [source] Determines if two NDFrame objects contain the same elements. NaNs in the same location are considered equal.

DataFrame.eq(other, axis='columns', level=None) [source] Wrapper for flexible comparison methods eq

DataFrame.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

DataFrame.duplicated(*args, **kwargs) [source] Return boolean Series denoting duplicate rows, optionally only considering certain columns Parameters: subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns keep : {?first?, ?last?, False}, default ?first? first : Mark duplicates as True except for the first occurrence. last : Mark duplicates as True except for the last occurrence. False : Mark all

DataFrame.dtypes Return the dtypes in this object.

DataFrame.drop_duplicates(*args, **kwargs) [source] Return DataFrame with duplicate rows removed, optionally only considering certain columns Parameters: subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns keep : {?first?, ?last?, False}, default ?first? first : Drop duplicates except for the first occurrence. last : Drop duplicates except for the last occurrence. False : Drop all duplicates.

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

DataFrame.drop(labels, axis=0, level=None, inplace=False, errors='raise') [source] Return new object with labels in requested axis removed. Parameters: labels : single label or list-like axis : int or axis name level : int or level name, default None For MultiIndex inplace : bool, default False If True, do operation inplace and return None. errors : {?ignore?, ?raise?}, default ?raise? If ?ignore?, suppress error and existing labels are dropped. New in version 0.16.1. Returns: dr

DataFrame.dot(other) [source] Matrix multiplication with DataFrame or Series objects Parameters: other : DataFrame or Series Returns: dot_product : DataFrame or Series

DataFrame.divide(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator truediv). Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this