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Data Profiling

Generate statistical summaries and distributions of data to understand its characteristics.

Data profiling definition:

Data profiling is the process of examining and analyzing data to gain insights into its quality, completeness, accuracy, and overall structure. It is an important step in data engineering as it helps to identify data issues and anomalies that could impact downstream processes.

Data profiling example using Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

There are several Python libraries that can be used for data profiling, including:

  • Pandas Profiling: Pandas Profiling is a library that generates interactive HTML reports from pandas DataFrames. It provides a quick and easy way to perform exploratory data analysis and identify data quality issues such as missing values, duplicate data, and outliers. \ This example uses ydata_profiling which is a replacement for pandas-profiling and can be installed with pip install ydata_profiling.
import pandas as pd
import ydata_profiling as pp

df = pd.read_csv('data.csv')
profile = pp.ProfileReport(df)

The html file produced will look like this:

Other data engineering terms related to
Data Analysis: