Deploy Python Web application using Docker and Amazon EC2

Introduction

Many of us usually think about the whole life cycle of an application (Web or Standalone), the deployment and testing phase comes with many tough thoughts as a developer. For example, a machine learning model is created, tested locally, and passes all test cases. Now the main part comes for deployment and testing to be done by the testing team. Many times, at least in my early days of Development/Technical Consulting, the tester used to be my biggest enemy though being one of the best friends otherwise. The first test…


Many times when we do our EDA or machine learning predictions, in the end, we end up importing tons of libraries. Python, NumPy, Pandas, Sklearn, Seaborn, Matplotlib, Cross-validation, Regression etc. are the most commonly used libraries we import to achieve the magic.

This lovely tragedy comes in below format:

What if we don’t need to import all regular libraries again and again when we start new EDA or predictions. It will save a lot of time for us and avoid the mistakes made during import.

Thanks to python community who takes these issues seriously and blessed other developers with Pyforest


Many a time we as aspiring Data Scientists, I often think what if:

  1. My dataset has many categorical columns
  2. Every column has many unique values
  3. Doing OneHot Endcoing give me 100 additional column
  4. Facing the curse of high dimensionality due to #3

Luckily Pandas and Sklearn give us quite a few functionalities that deal with high cardinal category columns. As a novice, we always see OHC (One Hot Encoding) for dummying the categorical values but the biggest drawback of this approach is that OHC always adds columns whose number will depend upon the number of different unique values in the…


I was in the process of analyzing AirQuality data set. I found there are two different columns capturing Date and Time in the different column. For better understanding, I thought to make Date and Time as one column and use it as Index for easy access to data. The dataFrame.info() has given me as below:

info about data frame

i.e. Date is Date time and Time is Object (basically String). I can’t concatenate the two columns straight forward, So I tried to do the following and finally the formula came to achieve this as below:

df[“new time”]=df[“Date”].dt.strftime(‘%Y-%m-%d’).astype(‘str’) +” “+ df[“Time”].astype(‘str’)

Nitesh Srivastava

Strategic and Technical Consultant, A Cloud change enabler, helping business to meet technology for better ROI.

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