Profile
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 forpandas-profiling
and can be installed withpip install ydata_profiling
.
import pandas as pd
import ydata_profiling as pp
df = pd.read_csv('data.csv')
profile = pp.ProfileReport(df)
profile.to_file('report.html')
The html file produced will look like this:
Other data engineering terms related to
‘Data Aggregation and Summarization’:
Aggregate
Combine data from multiple sources into a single dataset.
Anomaly Detection
Identify data points or events that deviate significantly from expected patterns or behaviors.
Consolidate
Combine data from multiple sources into a single dataset.
Feature Extraction
Identify and extract relevant features from raw data for use in analysis or modeling.
Feature Selection
Identify and select the most relevant and informative features for analysis or modeling.
Geospatial Analysis
Analyze data that has geographic or spatial components to identify patterns and relationships.
Normality Testing
Assess the normality of data distributions to ensure validity and reliability of statistical analysis.
Sampling
Extract a subset of data for exploratory analysis or to reduce computational complexity.
Sentiment Analysis
Analyze text data to identify and categorize the emotional tone or sentiment expressed.
Time Series Analysis
Analyze data over time to identify trends, patterns, and relationships.
Unstructured Data Analysis
Analyze unstructured data, such as text or images, to extract insights and meaning.