Koalas is an important package to use when dealing with Data Science and big data in Python. Koalas implements the pandas
DataFrame API on top of Apache Spark, making life easier for data scientists who are constantly interacting with big data.
pandas itself is widely used in the field of Data Science. The only difference between pandas and Spark is that pandas is a single node DataFrame implementation, whereas Spark is the standard for big data processing.
The Koalas package ensures that a user can immediately start working with Spark as long as they have experience working in pandas. Additionally, it provides a single codebase that works with both Spark and pandas.
A groupby operation involves some combination of splitting the object, applying a function, and combining the results. We can use this to group large amounts of data and perform operations on these groups.
groupby
in Koalas is similar to groupby
in pandas. groupby
splits a DataFrame based on one feature to return a groupby object. Next, A function is applied on the groupby object, which groups large sets of data.
Click here for a detailed list of all the functions.
It is one of the most widely used techniques to group large amounts of data.
Let’s work through a coding example:
groupby_koalas_df = ks.DataFrame({'Species': ['Reptiles', 'Reptiles',
'Mammals', 'Mammals'],
'Length': [2.5, 3.2, 1.5 , 1.75]},
columns=['Species', 'Length'])
# grouping the length by Species and applying the totalsum function.
>> groupby_koalas_df.groupby('Species').sum()
Length
Species
Mammals 3.25
Reptiles 5.70
# grouping the length by Species and applying the mean function.
>> groupby_koalas_df.groupby('Species').mean()
Length
Species
Mammals 1.625
Reptiles 2.850