<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 The Scientific Python Ecosystem
Python
Python is a interpreted, high-level programming language that is meant to be easily understandable and usable for a multitude of purposes. It is composed of libraries that contain useful tools for you to do quick and efficient data analysis and visualization. These libraries are like Lego blocks - you can pick and choose which ones you want to build your end product. The Scientific Python Ecosystem is composed of key libraries (i.e. NumPy, SciPy, Pandas, Matplotlib) that serve as a basis for most other libraries (i.e. MetPy). In this notebook, we'll briefly touch on several of these foundational libraries of the SciPy Ecosystem.
Getting Python
Step1:  Pandas
Pandas is an excellent library for handling tabular data and quickly performing data analysis on it. It can handle many textfile types.
Step2:  xarray
xarray is a Python library meant to handle N-dimensional arrays with metadata (think netCDF files). With the Dask library, it can work with Big Data efficiently in a Python framework.
Step3:  Dask
Dask is a parallel-computing library in Python. You can use it on your laptop, cloud environment, or on a high-performance computer (NCAR's Cheyenne for example). It allows for lazy evaluations so that computations only occur after you've chained all of your operations together. Additionally, it has a built-in scheduler to scale with your computational demand to optimize your parellel resources.
SciPy
The SciPy library has a lot of advanced mathematical functions that are not contained in Numpy, including Fast Fourier Transforms, interpolation methods, and linear algebra operations.
Scikit-learn
Scikit-learn is the primary machine learning library for Python. It can do simple things like regressions and classifications, or more advanced techniques like random forests. It can perform some neural network operations, but for big data implementations, check out the keras library.
Scikit-image
An image processing library built on NumPy
Visualization Libraries
Matplotlib
Matplotlib is one of the core visualization libraries in Python and produces publication-quality figures without much configuration.
<ASSISTANT_TASK:>
Python Code:
import numpy as np
x = np.arange(1,11)
y = np.arange(100,110)
mean_x_y = np.mean([x,y])
print(mean_x_y)
Explanation: The Scientific Python Ecosystem
Python
Python is a interpreted, high-level programming language that is meant to be easily understandable and usable for a multitude of purposes. It is composed of libraries that contain useful tools for you to do quick and efficient data analysis and visualization. These libraries are like Lego blocks - you can pick and choose which ones you want to build your end product. The Scientific Python Ecosystem is composed of key libraries (i.e. NumPy, SciPy, Pandas, Matplotlib) that serve as a basis for most other libraries (i.e. MetPy). In this notebook, we'll briefly touch on several of these foundational libraries of the SciPy Ecosystem.
Getting Python: Anaconda
Anaconda provides distributions of Python and the main third-party packages either as a full distribution or as a lighter-weight verison, "Miniconda". We recommend using Anaconda to build and maintain your Python stack, as it provides command line tools to download and update Python libraries. You can check it out at https://www.anaconda.com/distribution/.
Jupyter
The Jupyter library provides "literate programming" interfaces for Python and other programming languages. This file is displayed using the Jupyter library, either within Jupyter Notebook or Lab. It incorporates code, prose, and other text (equations, HTML) to make a seamless document for your analysis or presentation by working in small blocks. This also allows for quick prototyping and debugging of code as you write!
The Building Blocks of the SciPy World
While Python is the basis for everything, this figure demonstrates how packages build on top of each other (causing dependencies). Additionally, packages are constantly under development, so this structure does have some transient nature to it, as the SciPy world continue to expand (see Dask as a recent addition to this framework).
Data Analysis/Computation Libraries
NumPy
NumPy is the primary numerical computation library in Python. It works with N-dimensional arrays and matrices and performs basic computations on them.
End of explanation
import pandas as pd
df = pd.read_csv('../Pandas/Jan17_CO_ASOS.txt', sep='\t')
df.head()
Explanation: Pandas
Pandas is an excellent library for handling tabular data and quickly performing data analysis on it. It can handle many textfile types.
End of explanation
import xarray as xr
ds = xr.open_dataset('../../data/NARR_19930313_0000.nc')
ds
Explanation: xarray
xarray is a Python library meant to handle N-dimensional arrays with metadata (think netCDF files). With the Dask library, it can work with Big Data efficiently in a Python framework.
End of explanation
import matplotlib.pyplot as plt
plt.plot(x,y)
plt.title('Demo of Matplotlib')
plt.show()
Explanation: Dask
Dask is a parallel-computing library in Python. You can use it on your laptop, cloud environment, or on a high-performance computer (NCAR's Cheyenne for example). It allows for lazy evaluations so that computations only occur after you've chained all of your operations together. Additionally, it has a built-in scheduler to scale with your computational demand to optimize your parellel resources.
SciPy
The SciPy library has a lot of advanced mathematical functions that are not contained in Numpy, including Fast Fourier Transforms, interpolation methods, and linear algebra operations.
Scikit-learn
Scikit-learn is the primary machine learning library for Python. It can do simple things like regressions and classifications, or more advanced techniques like random forests. It can perform some neural network operations, but for big data implementations, check out the keras library.
Scikit-image
An image processing library built on NumPy
Visualization Libraries
Matplotlib
Matplotlib is one of the core visualization libraries in Python and produces publication-quality figures without much configuration.
End of explanation
<END_TASK>