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The Pandas Workshop

The Pandas Workshop

By : Blaine Bateman, Saikat Basak , Thomas Joseph, William So
4.8 (16)
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The Pandas Workshop

The Pandas Workshop

4.8 (16)
By: Blaine Bateman, Saikat Basak , Thomas Joseph, William So

Overview of this book

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Table of Contents (21 chapters)
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1
Part 1 – Introduction to pandas
6
Part 2 – Working with Data
11
Part 3 – Data Modeling
15
Part 4 – Additional Use Cases for pandas

Activity 11.01 – Multiple regression with non-linear models

As part of a research effort to improve metallic-oxide semiconductor sensors for the toxic gas carbon monoxide (CO), you are asked to investigate models of the sensor response for an array of sensors. You will review the data, perform some feature engineering for non-linear features, and then compare a baseline linear regression approach to a random forest model:

  1. For this exercise, you will need the pandas and numpy libraries, and three modules from sklearn, matplotlib, and seaborn. Load them in the first cell of the notebook:
    import pandas as pd
    import numpy as np
    from sklearn.linear_model import LinearRegression as OLS
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.preprocessing import StandardScaler
    import matplotlib.pyplot as plt
    import seaborn as sns
  2. As we have done before, create a utility function to plot a grid of histograms after being given the data, which variables to plot, the...

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