Contents

Python libraries

Install libraries

To install libraries, you can either use conda, pip or install from source files.

Pip only installs Python packages. The user needs to manually install the required external tools (compilers, C libraries, etc.).

Conda installs everything that is needed (compiler, libraries) and has the ability to create isolated environments, which may contain different versions of the packages.

Pip vs. Conda! For further details about the difference between Pip and Conda, visit anaconda.com

Install from Pip

All the packages available on Pip are listed on the pip webpage. To install any of them, type on a terminal:

pip install package_name

For install, to manipulate NetCDF files,

pip install netCDF4 xarray

Install from Conda

All the packages available on the Conda default channel are listed on the Conda repository. To install any of them, type on a terminal:

conda install package_name

For install, to manipulate NetCDF files,

conda install netCDF4 xarray

Conda virtual environments

To create virtual environments (for instance for spatial representation), type in a terminal:

# PyNGL env. for Python 3
conda create --name pyngl3 --channel conda-forge pynio pyngl

# PyNGL env. for Python 2
conda create --name pyngl2 --channel conda-forge pynio pyngl python=2.7

In this case, the packages are downloaded from a community channel, the conda-forge.

To change environment, type

conda activate pyngl2  # switch env. to pygnl2
conda activate pyngl3  # switch env. to pygnl3
conda activate base    # go back to default env.

To list all the environments, type:

conda env list
Default environment! The default environment is named base

Export environment

Conda allows to export an environment into a text file as follows:

conda env export > env.yaml

Import environment

You can also import an environment as follows:

conda env create -f env.yaml

Connect environments to Jupyter

You can also allow Jupyter to access your environments as follows:

conda activate pyngl
conda install ipython ipykernel
ipython kernel install --name "pyngl" --user

Install from source

To install a package from sources, unzip the archive and type:

python setup.py install --home=/my/directory/
Note. At the beginning, you are unlikely to do that.

Loading libraries

Libraries are loaded by using the import statement (generally at the beginning of the scripts) as follows:

# loading the numpy library
import numpy

# loading matplotlib with the shortname mp
import matplotlib as mp

In this case, the objects of the imported modules are stored into defined namespaces, which prevent conflicts among object names.

Calling functions

Using namespaces, a module’s function is called as follows:

module.function(arg1, arg2, arg3, ...)

For instance:

numpy.mean([0, 1, 2], keepdims=True)

array([1.])

mp.is_interactive()

False

To get some help about a function, use the help function:

help(mp.is_interactive)

Help on function is_interactive in module matplotlib:

is_interactive()
    Return whether to redraw after every plotting command.
    
    .. note::
    
        This function is only intended for use in backends. End users should
        use `.pyplot.isinteractive` instead.

Bad practice!

Libraries can also be loaded as follows:

from pandas import DataFrame

Here, we import the DataFrame from the pandas module.

from scipy.stats import *

Here, we import all the content of the scipy.stats module into the current namespace.

Warning! I strongly recommend to never use this way of importing modules, since they may be in conflict with other objects. 
import numpy as np
x = np.array([1e4, 1e6])
x

array([  10000., 1000000.])

from numpy import *
log10(x)

array([4., 6.])

from math import *
# log10(x[0]))

Here, the numpy.log10 method has been overwritten by the math.log10 one, which works on float objects, not on arrays.