Pandas series.le() is used to compare each item in a Caller series against a skipped series. It returns True for every element that is less than or equal to an element in the passed series.
Note: results are returned based on series comparison of callers & lt; = 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 several zero values and several equal values with the same indices. The series are compared using the le () method and 10 is passed to the fill_value parameter to replace the NaN values with 10.
# pandas module import import pandas as pd
# numpy module import import numpy as np
# create episode 1 series1 = pd.Series ([ 11 , 0 , 2 , 43 , < code class = "value"> 9 , 27 , np.nan , 10 , np.nan])
# create series 2 series2 = pd.Series ([ 16 , np. nan, 2 , 23 , 5 , 40 , 54 , 3 , 19 ])
# Replacing NaN repla ce_nan = 10
# call and return to the result variable result = series1.le (series2, fill_value = replace_nan)
# display
result
Output:
As shown in the output, True was returned wherever the value in the caller row was less than or equal to the value in the passed row. You can also see that the zero values have been replaced with 10 and the comparison has been done using that value.
Example # 2: Calling Series with str objects
In this example, two series are created using Output: pd.Series () . The series also contains some string values. In the case of strings, the comparison is performed against their # pandas module import import pandas as pd
# numpy module import import numpy as np
# create series 1 series1 = pd.Series ([ `A` , 0 , `c` , 43 , 9 , `e` , np.nan, `x` , np.nan])
# create episode 2 series2 = pd.Series ([ `v ` , np.nan, ` c` , 23 , 5 , `D` , 54 , `p` , 19 ])
# NaN replacement replace_nan = 10
# call and return to the result variable result = series1.le (series2, fill_value = replace_nan)
# display
result
As you can see from the output, in the case of strings, the comparison was performed using their ASCII values.
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