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()

In this post, we will learn about pandasâ€™ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

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 Basic Functions. So far, we have learned the three pandas data structure and how to create them. Due to its importance in real-time data processing, we will focus on dataframe objects right now and mention a few other data structures.

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