How to use Lambda function In Python Pandas
In this method, the user has to call the lambda function used above to return, the count of the corresponding rows present in the dataframe in the R programming language.
Example:
Under this example, as you can see, the car – BMW and the price 63 corresponds to the 4th row in the dataset. Applying the lambda function as above returns that the car BMW is present three times and the price 63 is present 2 times.
Python3
# import python pandas packageimport pandas as pd# create a sample dataframedata = pd.DataFrame({'cars': ['benz', 'benz', 'benz', 'benz', 'bmw', 'bmw', 'bmw'], 'Price_in_million': [15, 12, 23, 23, 63, 34, 63]})# get the 4th row present in the datadata.iloc[4]# Now apply lambda function to get the number# of times the row is present in the datasetdata.apply(lambda x: sum(x==x.iloc[4])) |
Output:
Pandas GroupBy – Count last value
A groupby operation involves grouping large amounts of data and computing operations on these groups. It is generally involved in some combination of splitting the object, applying a function, and combining the results. In this article let us see how to get the count of the last value in the group using pandas.
Syntax:
DataFrame.groupby(by, axis, as_index)
Parameters:
- by (datatype- list, tuples, dict, series, array): mapping, function, label, or list of labels. The function passed is used as-is to determine the groups.
- axis (datatype int, default 0): 1 – splits columns and 0 – splits rows.
- as_index (datatype bool, default True.): Returns an object with group labels as the index, for all aggregated output,
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