You're reading from Data Science Projects with Python - Second Edition

Product typeBook

Published inJul 2021

Reading LevelIntermediate

PublisherPackt

ISBN-139781800564480

Edition2nd Edition

Languages

Python

Concepts

Data Science

Author (1)

Stephen Klosterman

Exploring the Financial History Features in the Dataset

We are ready to explore the rest of the features in the case study dataset. First set up the environment and load data from the previous exercise. This can be done using the following snippet:

import pandas as pd
import matplotlib.pyplot as plt #import plotting package
#render plotting automatically
%matplotlib inline
import matplotlib as mpl #additional plotting functionality
mpl.rcParams['figure.dpi'] = 400 #high resolution figures
import numpy as np
df = pd.read_csv('../../Data/Chapter_1_cleaned_data.csv')

Note

The path to your CSV file may be different depending on where you saved it.

The remaining features to be examined are the financial history features. They fall naturally into three groups: the status of the monthly payments for the last 6 months, and the billed and paid amounts for the same period. First, let's look at the payment statuses. It is convenient to break these out...

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Data Science Projects with Python - Second Edition

Published in: Jul 2021Publisher: PacktISBN-13: 9781800564480

Author (1)

Stephen Klosterman

Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.
Read more about Stephen Klosterman

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You're reading from Data Science Projects with Python - Second Edition

Exploring the Financial History Features in the Dataset

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Author (1)

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