Skip to content
Lab-Notebook on Python for Data Science (SciKit and TensorFlow)
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.ipynb_checkpoints
codes
data
files
html
images
logs
models
tf_logs
tmp
01_Python_Bokeh.ipynb
01_Python_Datashader_NYC taxi trips.ipynb
MIT License.txt
PyTorch_01_Basics.ipynb
PyTorch_02_autograd_tutorial.ipynb
PyTorch_03_neural_networks_tutorial.ipynb
PyTorch_04_cifar10_tutorial.ipynb
PyTorch_deploy_seq2seq_hybrid_frontend_tutorial.ipynb
PyTorch_neural_style_tutorial.ipynb
Python_01_Basic.ipynb
Python_02_Numpy.ipynb
Python_03_Read_and_Write_Files.ipynb
Python_04_Matplotlib_Data_Visualization .ipynb
Python_05_Pandas.ipynb
README.md
TensorFlow_01_Graph_and_Session.ipynb
TensorFlow_02_Linear_Regression_with_TF.ipynb
TensorFlow_03_Saving and Restoring Models.ipynb
TensorFlow_04_Visualizing the Graph and Training Curves Using TensorBoard.ipynb
TensorFlow_05_Neural_Networks.ipynb
TensorFlow_06_Training_Deep_Nets.ipynb
TensorFlow_07_Transfer_Learning_(Reusing Pretrained Layers or Models).ipynb
TensorFlow_08_Faster_Optimizers_for_Building_Deep_Nets.ipynb
TensorFlow_09_Avoiding_Overfitting_by_Regularizations.ipynb
TensorFlow_10_Distributed_TF_Computation (Draft).ipynb
TensorFlow_11_Convolutional_Neural_Networks_(Deep ConvNets).ipynb
TensorFlow_12_Autoencoders.ipynb
TensorFlow_Keras_01 Keras API.ipynb
TensorFlow_Keras_02 Basic classification.ipynb
TensorFlow_Keras_03 Predict house prices regression.ipynb
TensorFlow_Keras_04 Save and restore models.ipynb
TensorFlow_Keras_05 Text classification with movie reviews.ipynb
checkpoint
deploy_seq2seq_hybrid_frontend_tutorial (1).py
finetuning_torchvision_models_tutorial.ipynb
finetuning_torchvision_models_tutorial.py
lines.html
my_array.csv
my_array.npy
my_arrays.npz
my_df.csv
my_df.html
my_df.json
my_df.xlsx
neural_style_tutorial.py
training_2.htm

README.md

Data_Science_Python

Logo

Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib, tensorflow) it becomes a powerful environment for scientific computing and data analysis.

Introduction to Python

Python is a high-level, dynamically typed multi-paradigm programming language. Python code is often said to be almost like pseudocode, since it allows you to express very powerful ideas in very few lines of code while being very readable. Here is the formal web of Python: https://www.python.org, you can find a lot of useful learning materials here. Python Versions

There are currently two different supported versions of Python, 2.7.x version, and 3.5.x/3.6.x version. Somewhat confusingly, Python 3.x.x introduced many backwards-incompatible changes to the language, so code written for 2.7 may not work under 3.x and vice versa. For this class, all codes will use Python 3.6 on the most popular python platform Anaconda for data science.

Install Anaconda

The reason why we use Anaconda instead of the original Python is because that the open source Anaconda Distribution is the easiest way to do Python data science and machine learning. It includes hundreds of popular data science packages, the conda package and virtual environment manager for Windows, Linux and MacOS. Conda makes it quick and easy to install, run and upgrade complex data science and machine learning environments like scikit-learn, TensorFlow and SciPy. Anaconda Distribution is the foundation of millions of data science projects as well as Amazon Web Services' Machine Learning AMIs and Anaconda for Microsoft on Azure and Windows.

  • Step 1: Download Anaconda Distribution (Anaconda 5.0.1) Python 3.6 64-Bit version (Windows, MacOS or Linux): https://www.anaconda.com/download/.
  • Step 2: Install Anaconda 5.0.1 with the default settings (unless you know what you are doing.)
  • Step 3: Open Anaconda Navigator, you will see similar GUI as below, which means you have successfully installed Anaconda, congratulations!

Install TensorFlow

Follow the official documentation to install the TensorFlow package: https://www.tensorflow.org/install/

After installation, type the following commands:

import tensorflow as tf
print(tf.__version__) # this should be tensorflow 1.8.0

Basic Anaconda

One good feature about Anaconda is its virtual environment manager, it avoids the confusion of different version python interpreters mess your computer default or previous projects. In the follow section, we will learn the basic operations about using environments. There are two ways to start the Conda Command Line Window:

    1. On Windows, go to Start > Anaconda3(64bit) > Anaconda Prompt.
    1. On the Anaconda Navigator, go to Environments > Anaconda3 > Open Terminal.

Conda Basics

In the Conda Command Window, type the following commands to get familiar with the Anaconda. Verify conda is installed, check version number:

conda info

Update conda to the current version:

conda update conda

Command line help:

conda install --help

Using Environments

Python is still a developing programming language with an active community. The downside of this is that some predeveloped packages may not work in the newer python version. Yes, it is not always a good choice to use the most advantage technology in real project. As we know the current python version in Environments>base(root) is version 3.6.3. Assume that you join a team where everyone is working on a project in Python 3.5, we need to create an independent developing environment to work with the team members without intervened by any issues caused by the Python version. The following steps will help you to install Python 3.5 in an Anaconda environment. There are two ways to set up the environment of Python 3.5, one is in the Conda Command Window and the other one is on the Anaconda Navigator.

Conda Command Window

In the Conda Command Window, create a new environment named “py35”, install Python 3.5, by invoking the following command::

conda create --name py35 python=3.5

The anaconda will suggest that several packages will by installed, type “y” and wait the installation done. Activate the conda environment by issuing the following command:

activate py35 

LINUX, macOS: source activate py35 Then, you can check your python version by type:

python --version 

You will get the results like: Python 3.5.4: Anaconda, Inc. This means that this environment is Python 3.5.

Installing and updating packages

Now, we fist need to list all packages and versions installed in active environment, enter the following command:

conda list

Assume we want to install a new package in the active environment (py35), enter the command:

conda install numpy

and then enter “y” to proceed.

Check the package Numpy is properly installed by running “conda list” again.Or you can try the following code to use the Numpy package.

import numpy as np
print(np.version.version)

Update a package in the current environment, for example

conda update numpy

Deactivate the current environment:

deactivate

LINUX, macOS: source deactivate.

License This Data_Science_Python is distributed under the MIT license (see MIT LICENCE.txt).

You can’t perform that action at this time.