Introduction
Exploratory data analysis(EDA) is an important aspect of any analysis project. EDA uses statistical and visualization techniques to bring into focus some of the most important aspects about the data that create a better understanding of the dataset. Given a dataset with thousands of records it would be quite difficult to create meaningful understanding by reading through each record on the dataset. Statistical functions can also generate important summaries but they are limited in creating a quick impression for anyone who wants a deeper dive into the dataset. However, graphical visualizations have proved to be very important in helping data analysts quickly visualize data and develop an understanding of the main features of the dataset before they get into the analysis process. This article will explore teh EDA process with a focus on data visualization techniques.
Data Visualization
Python provides various libraries that make it easy to craete dta visulizations. The common libraries used in EDA include matplotlib and seaborn. There are also other advanced tools used in data visualization include FusionCharts, Tableau, Grafana and Microsoft PowerBI. However, for the purpose of the EDA process I will concentrate on python visualization tools.
1. Scatter Plot
A scatter plot is used to show the relationship between two continuous variables. It is also a good choice to show any outliers on the dataset. For instance, a scatter plot can help to show the relationshiup between age and income levels.
2. Histograms
A histogram is used to to visualize a continuous single variable. Interpreting a histogram can help[ a data analyst to decide whether the dataset follows a normal distribution or is skewed towards one end. A histogram can be symmetrical, right-skewed, left-skewed, unimodal, or multimodal.
Bar Charts
Ba charts are used to visualize categorical data. For example they can show the changes in total sales for a certain company over several months.
Box Plots
Box plots help to visualize the distribution of a continuous variable using measures of central tendency such as median and displaying the quartiles. A box plot is also great at showing any outliers on the variable.
The code snippet below shows how to create a bar graph using seaborn.
# Countplot of segment sizes
import seaborn as sns
plt.figure(figsize=(8, 6))
sns.countplot(x='Cluster', data=rfm_data)
plt.xlabel('Segment')
plt.ylabel('Count')
plt.title('Customer Count by Segment')
plt.show()
The snippete below creates a box plot
import seborn as sns
plt.figure(figsize=(12, 6))
plt.subplot(131)
sns.boxplot(x='Cluster', y='Recency', data=rfm_data)
Conclusion
Exploratory data analysis is very important during the data analysis process. By creating the relevant visualizations based on the type of the variable data scientists are able to formulate better hypothesis, facilitate effective decision making and communicate their findings to a wider audience.
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