pandas.DataFrame.lt — pandas 0.24.0rc1 documentation (original) (raw)
DataFrame. lt(other, axis='columns', level=None)[source]¶
Less than of dataframe and other, element-wise (binary operator lt).
Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.
Equivalent to ==, =!, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.
| Parameters: | other : scalar, sequence, Series, or DataFrame Any single or multiple element data structure, or list-like object. axis : {0 or ‘index’, 1 or ‘columns’}, default ‘columns’ Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). level : int or label Broadcast across a level, matching Index values on the passed MultiIndex level. |
|---|---|
| Returns: | DataFrame of bool Result of the comparison. |
See also
Compare DataFrames for equality elementwise.
Compare DataFrames for inequality elementwise.
Compare DataFrames for less than inequality or equality elementwise.
Compare DataFrames for strictly less than inequality elementwise.
Compare DataFrames for greater than inequality or equality elementwise.
Compare DataFrames for strictly greater than inequality elementwise.
Notes
Mismatched indices will be unioned together.NaN values are considered different (i.e. NaN != NaN).
Examples
df = pd.DataFrame({'cost': [250, 150, 100], ... 'revenue': [100, 250, 300]}, ... index=['A', 'B', 'C']) df cost revenue A 250 100 B 150 250 C 100 300
Compare to a scalar and operator version which return the same results.
df == 100 cost revenue A False True B False False C True False
df.eq(100) cost revenue A False True B False False C True False
Compare to a list and Series by axis and operator version. As shown, for list axis is by default ‘index’, but for Series axis is by default ‘columns’.
df != [100, 250, 300] cost revenue A True False B True False C True False
df.ne([100, 250, 300], axis='index') cost revenue A True False B True False C True False
df != pd.Series([100, 250, 300]) cost revenue 0 1 2 A True True True True True B True True True True True C True True True True True
df.ne(pd.Series([100, 250, 300]), axis='columns') cost revenue 0 1 2 A True True True True True B True True True True True C True True True True True
Compare to a DataFrame of different shape.
other = pd.DataFrame({'revenue': [300, 250, 100, 150]}, ... index=['A', 'B', 'C', 'D']) other revenue A 300 B 250 C 100 D 150
df.gt(other) cost revenue A False False B False False C False True D False False
Compare to a MultiIndex by level.
df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220], ... 'revenue': [100, 250, 300, 200, 175, 225]}, ... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], ... ['A', 'B', 'C', 'A', 'B' ,'C']]) df_multindex cost revenue Q1 A 250 100 B 150 250 C 100 300 Q2 A 150 200 B 300 175 C 220 225
df.le(df_multindex, level=1) cost revenue Q1 A True True B True True C True True Q2 A False True B True False C True False