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I am having the following python/pandas command:
df.groupby('Column_Name').agg(lambda x: x.value_counts().max() where I am getting the value counts for ALL columns in a DataFrameGroupBy object.
How do I do this action in PySpark?
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PySpark users can access the full PySpark APIs by calling DataFrame. to_spark() . pandas-on-Spark DataFrame and Spark DataFrame are virtually interchangeable. However, note that a new default index is created when pandas-on-Spark DataFrame is created from Spark DataFrame.
In very simple words Pandas run operations on a single machine whereas PySpark runs on multiple machines. If you are working on a Machine Learning application where you are dealing with larger datasets, PySpark is a best fit which could processes operations many times(100x) faster than Pandas.
Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element.
Convert PySpark Dataframe to Pandas DataFramePySpark DataFrame provides a method toPandas() to convert it to Python Pandas DataFrame. toPandas() results in the collection of all records in the PySpark DataFrame to the driver program and should be done only on a small subset of the data.
It's more or less the same:
spark_df.groupBy('column_name').count().orderBy('count') In the groupBy you can have multiple columns delimited by a ,
For example groupBy('column_1', 'column_2')
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try this when you want to control the order:
data.groupBy('col_name').count().orderBy('count', ascending=False).show() If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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