Book Image

The Pandas Workshop

By : Blaine Bateman, Saikat Basak, Thomas V. Joseph, William So
5 (1)
Book Image

The Pandas Workshop

5 (1)
By: Blaine Bateman, Saikat Basak, Thomas V. 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)
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

Solution 12.1

Perform the following steps to complete the activity:

  1. For this activity, you will need the pandas and numpy libraries. Load them in the first cell of the notebook:
    import pandas as pd
    import numpy as np
  2. Read in the household_power_consumption.csv data from the Datasets directory and list the first few rows:
    data_fn = 'household_power_consumption.csv'
    household_electricity = \
        pd.read_csv('../datasets/' + data_fn, 
        sep = ';',
        low_memory = False)
    household_electricity.head()

This generates the following:

Figure 15.62 – The household_power_consumption.csv data

  1. You should inspect the data types of the columns and further investigate whether there are non-numeric values. If so, correct them by converting them to NA values and then filling them by interpolation:
    Household_electricity.dtypes

This generates the following...