Python Programmer

Technical expertise

Primary Skills (Must-have)

- Writing reusable, testable, and efficient codes
- Good Understanding of Datatypes (numeric, integer, character, float, boolean etc.) & Data Structures (List, Dictionary, tuple, set etc.), Inter-Conversion between these data structures.
- File IO & File Formats (Reading and Writing txt, csv, excel, psv, pkl & bin files)
- Conditional & Iterative control statements & Handling of date values in Python.
- Functions (User Defined & Built-in functions), Apply, Lambda Functions, Map, Filter, Transform Functions & Operators (Logical, Relational & Special Operators)
- Sub setting using indices, names, Boolean values on different data structures.
- Manipulating Pandas, Numpy & Series objects (Addition, Deletion, Subset, Filtering, Transform etc.)
- Variable Scoping & Environments (Global & Local Environments)
- Data Wrangling (Joins, Sorting, Searching etc.) & Manipulation of Data frames (Addition / Deletion of rows & columns, Filtering, Grouping, Summarizing etc.)
- Visualization (Using matplotlib, seaborn, plotly etc.)
- Automating data pipelines and knowledge of using JDBC and ODBC drivers
- Basic unix/linux commands

Secondary Skills (Good to have)

- Object Oriented Programming (Classes & Objects)
- Python Packaging (. wheel files), PyPI, Versioning
- Rest API using Flask, Django
- Knowledge of using common machine learning methods using scikit-learn
- Knowledge of PySpark, Pydoop etc. and processing of large data sets in Python
- Knowledge of using TensorFlow, Keras, PyTorch
- Connecting to ODBC Databases from Python with pyodbc
- Experience of Angular or React frameworks

Experience:

2-3 years of experience of working as a python programmer

Working knowledge of Python libraries (but not limited to) such as Pandas, NumPy, SciPy, Matplotlib, Scikit Learn, Statsmodels
Building data pipelines in Python
Manipulating large data sets using Python
Creating REST APIs using Python
Creating visualization using Python
Experience of production deployment
Experience of unit testing and integration testing

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