Data Science Tools
Python
How to know pip packages size’ good for removal
Coroutines generators async wait
Async io
Clean code:
Virtual Environments
Introduction to venv complementary to the above
pyenv, virtualenv and using them with Jupyter - a make sense tutorial and instructions on how to use all.
Create isolated Jupyter ipython kernels with pyenv and virtualenv by alfredo motta
Jupyter Notebook in a virtual env by Christine Egan
PYENV
pyenv virtualenv
JUPYTER
Important colaboratory commands for jupytr
(Debugging in Jupyter, how?) - put a one liner before the code and query the variables inside a function.
Virtual environments in jupyter
Enter your project directory
$ python -m venv projectname
$ source projectname/bin/activate
(venv) $ pip install ipykernel
(venv) $ ipython kernel install --user --name=projectname
Run jupyter notebook * (not entirely sure how this works out when you have multiple notebook processes, can we just reuse the same server?)
Connect to the new server at port 8889
(how does reshape work?) - a shape of (2,4,6) is like a tree of 2->4 and each one has more leaves 4->6.
As far as i can tell, reshape effectively flattens the tree and divide it again to a new tree, but the total amount of inputs needs to stay the same. 2*4*6 = 4*2*3*2 for example
code:
import numpy
rng = numpy.random.RandomState(234)
a = rng.randn(2,3,10)
print(a.shape)
print(a)
b = numpy.reshape(a, (3,5,-1))
print(b.shape)
print (b)
*** A tutorial for Google Colaboratory - free Tesla K80 with Jup-notebook
How to add extensions to jupyter: extensions
Connecting from COLAB to MS AZURE
Streamlit vs. Dash vs. Shiny vs. Voila vs. Flask vs. Jupyter
SCIPY
Optimization problems, a nice tutorial to finding the minima
Minima / maxima finding it in a 1d numpy array
NUMPY
Using numpy efficiently - explaining why vectors work faster. Fast vector calculation, a benchmark between list, map, vectorize. Vectorize wins. The idea is to use vectorize and a function that does something that may involve if conditions on a vector, and do it as fast as possible.
PANDAS
Great introductory tutorial about using pandas, loading, loading from zip, seeing the table’s features, accessing rows & columns, boolean operations, calculating on a whole row\column with a simple function and on two columns even, dealing with time\date parsing.
Visualizing pandas pivoting and reshaping functions by Jay Alammar - pivot melt stack unstack
def mask_with_values(df): mask = df['A'].values == 'foo' return df[mask]
Accessing dataframe rows, columns and cells- by name, by index, by python methods.
Dealing with time series in pandas,
Create a new column based on a (boolean or not) column and calculation:
Using python (map)
Using numpy
using a function (not as pretty)
Given a DataFrame, the shift() function can be used to create copies of columns that are pushed forward (rows of NaN values added to the front) or pulled back (rows of NaN values added to the end).
df['t'] = [x for x in range(10)]
df['t-1'] = df['t'].shift(1)
df['t-1'] = df['t'].shift(-1)
Dataframe Validation In Python - A Practical Introduction - Yotam Perkal - PyCon Israel 2018
In this talk, I will present the problem and give a practical overview (accompanied by Jupyter Notebook code examples) of three libraries that aim to address it: Voluptuous - Which uses Schema definitions in order to validate data [https://github.com/alecthomas/voluptuous] Engarde - A lightweight way to explicitly state your assumptions about the data and check that they're actually true [https://github.com/TomAugspurger/engarde] * TDDA - Test Driven Data Analysis [ https://github.com/tdda/tdda]. By the end of this talk, you will understand the Importance of data validation and get a sense of how to integrate data validation principles as part of the ML pipeline.
Stop using itterows, use apply.
Exploratory Data Analysis (EDA)
Sweetviz - "Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application.
The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks."
TIMESERIES
SCI-KIT LEARN
cuML - Multi gpu, multi node-gpu alternative for SKLEARN algorithms
Awesome code examples about using svm\knn\naive\log regression in sklearn in python, i.e., “fitting a model onto the data”
Also Insanely fast, see here.
Functional api for sk learn, using pipelines. thank you sk-lego.
Images by SK-Lego
FAST.AI
Medium on all fast.ai courses, 14 posts
PYCARET
1. What is? by vidhaya - PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It is easy to use and you can do almost every data science project task with just one line of code.
NVIDIA TF CUDA CUDNN
Failed to initialize NVML: Driver/library version mismatch
GCP
Resize google disk size, 1, **[2](https://www.cloudbooklet.com/how-to-resize-disk-of-a-vm-instance-in-google-cloud/),**
GIT / Bitbucket
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