What're the logistics behind having the extra .compute() in the numpy and pandas mimicked functionalities? Is it just to support some kind of lazy evaluation?

What're the logistics behind having the extra .compute() in the numpy and pandas mimicked functionalities? Is it just to support some kind of lazy evaluation?

Example from Dask documentation below:

import pandas as pd import dask.dataframe as dd df = pd.read_csv('2015-01-01.csv') df = dd.read_csv('2015-*-*.csv') df.groupby(df.user_id).value.mean() df.groupby(df.user_id).value.mean().compute()

Python Pandas Tutorial - Data Analysis with Python Pandas will help you get started with Python Pandas Library for various applications including Data analysis. You'll learn: Introduction to Pandas; DataFrames and Series; How To View Data? Selecting Data; Handling Missing Data; Pandas Operations; Merge, Group, Reshape Data; Time Series And Categoricals; Plotting Using Pandas

Python Pandas Tutorial will help you get started with Python Pandas Library for various applications including Data analysis. Introduction to Pandas. DataFrames and Series. How To View Data? Selecting Data. Handling Missing Data. Pandas Operations. Merge, Group, Reshape Data. Time Series And Categoricals. Plotting Using Pandas

In this Pandas Tutorial, we will learn to insert/add a new row to an existing Pandas Dataframe. We will use pandas.DataFrame.loc, pandas.concat() and numpy.insert(). Using these methods you can add multiple rows/lists to an existing or an empty...