Handbook of Hidden Data Scientist (Python)
  • Introduction
  • Machine Learning
    • Supervised Learning
      • Features and Labels
      • Linear Decision Surface
      • Naive Bayes
      • Support Vector Machine
      • Decision Trees
      • Regressions
  • Python
  • CSV with pandas
    • Reading CSV
    • Math Operations on Column
    • Joining CSVs
    • Plot and Normalize CSV Data
  • NumPy
    • Using NumPy from pandas DataFrame
    • Create NDArray
    • Working with NDArray
    • Timing operations
  • Statistical Analysis
    • Global Statistics
    • Rolling Statistics
    • Daily Returns
    • Cumulative Returns
  • Incomplete Data
    • Pandas fillna()
  • Histograms and Scatter Plots
    • Histogram
    • Two Histograms
    • Scatter Plot
  • Visualization
    • pyplot
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CSV with pandas

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Last updated 5 years ago

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We are going to explore library in this chapter. Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. By the way, pandas uses NumPy library to represent data strcutrues.

I was really impressed by this library. We can easily load multiple CSV files, join them together and then perform another operation. It almost feel you have your CSV loaded in database that you can operate using Python code.

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