Analysis of Data from UFO Observation Reports

I selected data from an Excel file hosted on data.world. This file contains information on UFO sightings, including parameters such as the date and time of the sighting, country, city, state, UFO shape, and a brief description of the event.

UFO sighting data holds particular value for several reasons:

1. UFO Phenomenon Research: Analyzing this data can help understand the geographical distribution of sightings, identify temporal patterns, and determine the most frequently occurring UFO shapes.

2. Social Interest: UFO sightings generate significant public interest and can stimulate discussions about the existence of extraterrestrial life.

3. Safety and Science: Studying such data can be beneficial for security services and scientific organizations seeking explanations for unusual aerial phenomena.

1. Histogram: What: The number of UAP incident reports, grouped by country. Why: The histogram allows for easy comparison of report numbers across different countries, as the height of the bars clearly illustrates the variance in the frequency of incidents.

2. Scatter Plot: What: The distribution of incident reports by month for the year 2016. Why: A scatter plot was chosen to visualize the fluctuations in UAP sightings throughout the year. This enables the identification of temporal patterns and peak periods.

3. Pie Chart: What: The distribution of UAP shapes by country. Why: The pie chart allows for a clear visualization of the proportion of various UAP shapes in each country, which is convenient for visual comparison.

4. Line Chart: What: The number of incidents by state. Why: The line chart allows visualization of the distribution of incidents across states, highlighting those with the highest and lowest number of sightings.

First, I determined the types of analyses I would be conducting. I used the Google Colab environment to write the code. Then, I began writing the code itself, utilizing the pandas library for data analysis and matplotlib.pyplot for creating graphs:

import pandas as pd import matplotlib.pyplot as plt

Next, I loaded the data for analysis using:

url = ’https://query.data.world/s/hchezsqi4kggugpqzkdg2hpqdparoh?dws=00000’ df = pd.read_excel (url)

I also used ChatGPT to refine the structure. I created the cover in Ideogram.