Your complete guide to Python

  • Traditional libraries: PyPDF2, pdfminer, tika, PyMuPDF and fitz
  • OCR libraries: textract and pytesseract
  • pandas: to perform data transformations, aggregations, validations and cleansing
  • numpy: to carry out fast numerical dataset operations
  • nltk: to perform a wider range of NLP pre-processing functionality
  • SciPy: to perform different mathematical computations
  • scikit-learn: the most commonly used library for predictive analytics
  • nltk: to perform NLP analysis, including sentiment scoring
  • spacy: to classify entity names through entity recognition modelling
  • textblob and vaderSentiment: to perform sentiment analysis on textual data
  • keras: to build and transform deep-learning networks and datasets
  • pytorch: to build advanced and flexible deep-learning models
  • tensorflow: another advanced framework to build deep-learning models
  • word2vec, glove, BERT and USE: pre-trained embedded language models to build advanced NLP
  • huggingface: open source repository of many different kinds of NLP pre-trained models
  • matplotlib, seaborn, and plotly: to visualise data through graphs
  • jupyter notebook: to rapidly code, visualise and present data applications and reports
  • powerbiclient: to visualise data insights and predictions
  • dash: to build graphical and user interface web page components



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Acuity Knowledge Partners

Acuity Knowledge Partners


We write about financial industry trends, the impact of regulatory changes and opinions on industry inflection points.