Data Visualization Techniques in Python

Python Data Visualization Examples

Data visualization is a crucial aspect of data analysis, enabling us to effectively communicate insights and patterns present in our data. In this article, we will explore some data visualization techniques for Exploratory Data analysis using Python.

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Data visualization involves representing data in graphical or pictorial formats, making it easier for users to understand complex information. Python offers several libraries for data visualization, including Matplotlib, Seaborn, and Plotly.

Preparing the Dataframe

We’ll use a sample dataset to demonstrate various visualization techniques.

# Importing libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Sample DataFrame creation
data = {
    'Year': [2015, 2016, 2017, 2018, 2019],
    'Sales': [50000, 60000, 75000, 90000, 100000],
    'Expenses': [35000, 40000, 45000, 55000, 60000]
}
df = pd.DataFrame(data)

1. Bar Plot

Bar plots are effective for comparing categories.