Visualizing Data in One Dimension with Pandas

In this edition of my LinkedIn newsletter, I want to delve into a fundamental aspect of data analysis and visualization: representing data in one dimension using Pandas. It might sound basic, but mastering this skill is crucial for any data scientist, analyst, or anyone working with data.

Why One-Dimensional Visualization?

Before we dive into visualization, let’s briefly discuss why visualizing data in one dimension is so important. One-dimensional data, often called univariate data, typically consists of a single variable, making it the simplest form of data. It’s a starting point for exploring data and understanding its characteristics. We use one-dimensional visualizations to gain insights, identify patterns, and spot outliers or anomalies.

One of the quickest and most effective ways to visualize all numeric data and their distributions in Python is to leverage histograms using pandas

Here, we will plot a histogram plot that shows all the attributes.

Step 1: Load Data

First, load your data using Pandas. Think of it as gathering all your ingredients before cooking.

We are using a BTC cryptocurrency dataset.

import pandas as pd
import matplotlib.pyplot as plt        

btc_crypto = pd.read_csv('/content/BTC.csv')
btc_crypto        

Step 2: Peek at Data

Take a quick look at your data to understand what’s inside.

Step 3: Visualize Data

Now, let’s make it visual!

Create a simple histogram chart with the code attached to see how values spread. It’s like painting a small picture of your data.

btc_crypto.hist(bins=15, color= 'steelblue', edgecolor='black', linewidth=1.0, xlabelsize=8, ylabelsize=8, grid=False)

plt.tight_layout(rect=(0,0,1.2,1.2))        

Relevant Link: GitHub