Data Cleaning With Python in Data Science

Today we will discuss an important step in the data science process – data cleaning. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data. Data cleaning is essential to ensure the accuracy and reliability of the results we obtain from our analyses.

Let’s start with an example from the book “Data Science from Scratch” by Joel Grus. In Chapter 7, Grus presents a case study of analyzing user ratings of movies. Grus collected data from the MovieLens website, which contains user ratings of movies. However, the data was not clean, and Grus had to perform several cleaning operations, such as removing duplicates, filling in missing values, and converting data types.

Another example is from the book “Python for Data Analysis” by Wes McKinney. In Chapter 7, McKinney presents a case study of analyzing the New York City Taxi and Limousine Commission (TLC) Trip Record Data. McKinney had to clean the data by removing outliers, handling missing values, and converting data types.

In Python, we can perform several data cleaning operations using various libraries. Let’s consider two examples. In the first example, we will remove duplicates from a dataset using the Pandas library. Here is the code:

Code Block 1.0 – Remove Duplicate Values From Dataset

 
   import pandas as pd

   # Load the dataset
   df = pd.read_csv('data.csv')

   # Remove duplicates
   df = df.drop_duplicates()

In the second example, we will handle missing values using the NumPy library. Here is the code:

Code Block 2.0 – Replace Missing Values In Dataset

 
   import numpy as np

   # Load the dataset
   data = np.genfromtxt('data.csv', delimiter=',')

   # Replace missing values with the mean of the column
   data[np.isnan(data)] = np.nanmean(data, axis=0)

In the first example, we use the drop_duplicates function of Pandas to remove duplicate rows from the dataset. In the second example, we use the genfromtxt function of NumPy to load the dataset, and the nanmean function to replace missing values with the mean of the column.

In conclusion, data cleaning is an essential step in the data science process, and it involves identifying and correcting errors, inconsistencies, and inaccuracies in the data. There are several libraries in Python that we can use to perform data cleaning operations, such as Pandas and NumPy. By performing data cleaning operations, we can ensure that our analyses are accurate, reliable, and meaningful.