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Applied Data Science with Python and Jupyter

You're reading from   Applied Data Science with Python and Jupyter Use powerful industry-standard tools to unlock new, actionable insights from your data

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Product type Paperback
Published in Oct 2018
Publisher
ISBN-13 9781789958171
Length 192 pages
Edition 1st Edition
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Author (1):
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Alex Galea Alex Galea
Author Profile Icon Alex Galea
Alex Galea
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Toc

Chapter 2: Data Cleaning and Advanced Machine

Activity 2: Preparing to Train a Predictive Model for the Employee-Retention Problem

  1. Scroll to the Activity A section of the lesson-2-workbook.ipynb notebook file.
  2. Check the head of the table by running the following code:
    %%bash
    head ../data/hr-analytics/hr_data.csv

    Judging by the output, convince yourself that it looks to be in standard CSV format. For CSV files, we should be able to simply load the data with pd.read_csv.

  3. Load the data with Pandas by running df = pd.read_csv('../data/hr- analytics/hr_data.csv'). Write it out yourself and use tab completion to help type the file path.
  4. Inspect the columns by printing df.columns and make sure the data has loaded as expected by printing the DataFrame head and tail with df.head() and df.tail():
    Figure 2.46: Output for inspecting head and tail of columns
    Figure 2.46: Output for inspecting head and tail of columns

    We can see that it appears to have loaded correctly. Based on the tail index values, there are nearly 15,000 rows...

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