Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas series.le() is used to compare every element of Caller series with passed series. It returns True for every element which is Less than or Equal to the element in passed series.
Note: The results are returned on the basis of comparison caller series <= other series.
Syntax: Series.le(other, level=None, fill_value=None, axis=0)
Parameters:
other: other series to be compared with
level: int or name of level in case of multi level
fill_value: Value to be replaced instead of NaN
axis: 0 or ‘index’ to apply method by rows and 1 or ‘columns’ to apply by columns.Return type: Boolean series
Example #1: NaN handling
In this example, two series are created using pd.Series(). The series contains some Null values and some equal values at same indices too. The series are compared using le() method and 10 is passed to fill_value parameter to replace NaN values by 10.
# importing pandas module import pandas as pd # importing numpy module import numpy as np # creating series 1 series1 = pd.Series([11, 0, 2, 43, 9, 27, np.nan, 10, np.nan]) # creating series 2 series2 = pd.Series([16, np.nan, 2, 23, 5, 40, 54, 3, 19]) # NaN replacement replace_nan = 10 # calling and returning to result variable result = series1.le(series2, fill_value = replace_nan) # display result |
Output:
As shown in output, True was returned wherever value in caller series was less than or Equal value in passed series. Also it can be seen Null values were replaced by 10 and the comparison was made using that value.
Example #2: Calling on Series with str objects
In this example, two series are created using pd.Series(). The series contains some string values too. In case of strings, the comparison is made with their ASCII values.
# importing pandas module import pandas as pd # importing numpy module import numpy as np # creating series 1 series1 = pd.Series(['A', 0, 'c', 43, 9, 'e', np.nan, 'x', np.nan]) # creating series 2 series2 = pd.Series(['v', np.nan, 'c', 23, 5, 'D', 54, 'p', 19]) # NaN replacement replace_nan = 10 # calling and returning to result variable result = series1.le(series2, fill_value = replace_nan) # display result |
Output:
As it can be seen in output, in case of strings, the comparison was made using their ASCII values.
