Introduction: Why Data Dashboards?

Hey folks! Data dashboards are incredible tools for making sense of tons of information. They can help us understand complex issues like the Israel-Palestine conflict in a clear and straightforward manner. So, today, I’m going to show you how I created my own dashboard to track injuries and deaths over the years in this conflict. Trust me, you don’t need to be a tech wizard to do this.

The Tools You’ll Need

What You Should Install

First things first, make sure you have Python installed on your computer. Next, you’ll need a Jupyter Notebook, which is a fantastic tool that lets you run Python code in small chunks. You’ll also need some Python libraries—Matplotlib, Seaborn, and Pandas—to help with the plotting and data management.

To install all these, open your terminal and run:

pip install matplotlib seaborn pandas jupyter

Gathering and Preparing the Data

Finding the Right Data

For this project, I used a dataset that includes details about injuries and deaths from both sides of the Israel-Palestine conflict. You can find similar data from reputable sources like government publications or international organizations. You can download the same dataset on the download page of this blog.

Cleaning it Up

Once you have the dataset, open a Jupyter Notebook and import Pandas to read the data:

import pandas as pd
df = pd.read_csv('your_dataset.csv')

Check for missing or inconsistent data and clean it up using Pandas. If you want step by step guide on how to clean the data, just drop a comment below.

Laying the Groundwork: Python and Matplotlib Basics

Getting Started with Simple Plots

Before diving into the complex stuff, let’s get comfortable with some basic plots. We’ll use Matplotlib to create a simple line graph. Type this code into a new cell in your Jupyter Notebook:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [1, 4, 9])

You should see a simple line graph. Congrats, that’s your first plot!

Creating Your First Plot

Your First Real Plot

Now that we’ve got our feet wet, let’s plot some real data from our dataset. Say we want to look at Palestinian injuries over the years. Your code would look something like this:

plt.plot(df['Year'], df['Palestinians_Injuries'])

Diving Deeper: Advanced Plots

Mastering Subplots

We’re going to level up by creating subplots—multiple plots in one figure. You’ll see how useful this is for our dashboard.

fig, axs = plt.subplots(2)
fig.suptitle('Injuries and Deaths Over the Years')
axs[0].plot(df['Year'], df['Palestinians_Injuries'])
axs[1].plot(df['Year'], df['Israelis_Injuries'])

Styling and Customization

Making it Pretty

Style matters! Let’s make our plots more readable and engaging. You can add labels, grid lines, or even change the color scheme.

plt.plot(df['Year'], df['Palestinians_Injuries'], label='Palestinians', color='r')

Troubleshooting Common Issues

Overlapping Axes?

If you run into any issues like overlapping axes or labels, don’t panic! Matplotlib offers lots of ways to tweak and adjust your plots.

The Final Dashboard

Bringing it All Together

Now that you’ve got all these individual plots, it’s time to assemble them into a dashboard. You’ll use the subplot feature in Matplotlib to organize multiple plots in a grid layout.

Conclusion and Next Steps

You did it! You’ve created a comprehensive dashboard that offers valuable insights into the Israel-Palestine conflict. The next steps? You can try adding more data, or maybe create interactive dashboards using libraries like Plotly.

There you have it! I hope this step-by-step guide helps you in your data visualization journey. Keep experimenting, and don’t hesitate to reach out if you have any questions or to drop your complaints in the comment section.