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#Aggregating Data summary statistics with pandas Part - 3 Efficient summaries The .agg() method
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Efficient summaries
While pandas and NumPy have tons of functions, sometimes, you may need a different function to summarize your data.
The .agg() method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super-efficient. For example,
df['column'].agg(function)
In the custom function for this exercise, "IQR" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains outliers.
sales is available and pandas is loaded as pd.
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While pandas and NumPy have tons of functions, sometimes, you may need a different function to summarize your data.
The .agg() method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super-efficient. For example,
df['column'].agg(function)
In the custom function for this exercise, "IQR" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains outliers.
sales is available and pandas is loaded as pd.
Like And Share The Video!
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