: A powerfull yet friendly programming language that can be used in almost every field.

# the hello world program in python
print('Hello World!')

Doc

NumPy : The foundation of scientific python that brings to Python the high performance of scientific computation, as well as the MATLAB experience.

import numpy as np
x = np.arange(0, 2*np.pi, 0.1) 
y = np.sin(x)

Overview Doc Video

SciPy : Extends the power of NumPy with more tools, similar to MATLAB's toolboxes.

import numpy as np 
from scipy.fftpack import fft, ifft 
x = np.random.randn(100) 
y = fft(x) 
yinv = ifft(y)

Doc

Matplotlib : Visualizes scientific data and produces publication quality figures, with a MATLAB-like interface.

import numpy as np 
import matplotlib.pyplot as plt 

N = 50 
x = np.random.rand(N) 
y = np.random.rand(N) 
colors = np.random.rand(N) 
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses 

plt.scatter(x, y, s=area, c=colors, alpha=0.5) 
plt.show()

Gallery Doc Video

Basemap : The matplotlib basemap toolkit is a library for plotting 2D data on maps in Python. It is similar in functionality to the matlab mapping toolbox, the IDL mapping facilities, GrADS, or the Generic Mapping Tools.

from mpl_toolkits.basemap import Basemap 
m = Basemap(projection='kav7',lon_0=0,resolution=None) 
map.drawcoastlines(linewidth=0.25) 
map.drawcountries(linewidth=0.25) 
map.fillcontinents(color='coral',lake_color='aqua')

Gallery Doc

IPython : Provides a rich architecture for interactive computing.

Jupyter : A web application for interactive data science and scientific computing.

NetCDF4 : Python/numpy interface to the netCDF C library.

from netCDF4 import Dataset 
ds = Dataset("test.nc", "w", format="NETCDF4") 
print(ds)

Doc

H5py : Pythonic interface to the HDF5 binary data format.

import h5py 
f = h5py.File('test.hdf5', 'w')

Pandas : The art of data analysis, especially for time series and table data.

import pandas as pd 
s = pd.Series([1,3,5,np.nan,6,8]) 
dates = pd.date_range('20130101', periods=6) 
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

Doc Video

Xarray : Brings to NumPy N-D arrays the Pandas-like labeled axes, and the NetCDF-like but in memory data structure.

import xarray as xr 
ds = xr.open_dataset('test.nc')

Doc Video

Dask : Dask enables parallel computing through task scheduling and blocked algorithms.

Bottleneck : Fast NumPy array functions written in Cython.

Install Miniconda

After Miniconda is installed, install the scientific Python packages using command conda install:

conda install numpy scipy matplotlib basemap ipython jupyter netcdf4 h5py pandas xarray dask bottleneck

You can also use conda install to install other python packages with similar syntax:

conda install PACKAGENAME

Doc

Iris : Implements a data model to create a data abstraction layer which isolates analysis and visualisation code from data format specifics.

Cartopy : Implements a data model to create a data abstraction layer which isolates analysis and visualisation code from data format specifics.

PyNGL and PyNIO : PyNGL (pronounced "pingle") is a Python language module used to visualize scientific data, with an emphasis on high quality 2D visualizations. PyNIO is a Python module used for reading and writing files in several different data formats, including netCDF, netCDF 4, GRIB1, GRIB2, HDF 4, HDF-EOS 2 and HDF-EOS5, and CCM history files.

Cdo : Allows you to use CDO in the context of Python and Ruby as if it would be a native library.

Seaborn : A Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

import seaborn as sns 
sns.set_style('darkgrid') 
sns.set_context('talk') 
g = sns.jointplot("total_bill", "tip", data=tips, kind="reg",xlim=(0, 60), ylim=(0, 12), color="r", size=7)

Gallery Doc Video

Numba : Makes Python run fast.

Scikit-learn : Machine Learning in Python.