UFO Python Download the ufo$\_$sightings.csv file that is located on this web page to the same directory where your Program $6$ python file is located.

Use pandas and$/$or matplotlib to produce two different visualizations that help the user gain insight into the information contained in the csv file. $20$ points $-$ pandas and$/$or matplotlib are used to produce two insightful visualizations. All or nothing.

$20$ points $-$ Each visualization is a different type. For example, one visualization could be a bar chart and the other could be a pie chart. All or nothing.

$10$ points $-$ The visualizations use the underlying data to show different things. All or nothing.

$20$ points $-$ All aspects of each visualization are clearly labeled. $3$ points for each type of improvement up to $20$ points.

$5$ points $-$ The first visualization yields an interesting insight that is explained in a comment at the top of your program. For example, if the program visualized the Montana population by decade, the population is flat to decreasing from $1920$ to $1940.$ This is interesting because populations normally increase and is explained by drought and depression.

$5$ points $-$ The second visualization yields an interesting insight that is explained in a comment at the top of your program, similar to the requirement for the first visualization.

$20$ points $-$ The Python solution is properly commented, easy to understand and does not contain unnecessary code. $3$ points for each type of improvement up to $20$ points.

code:

# Importing required libraries

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

# First Visualization: Number of UFO Sightings by Year

# This visualization helps us understand the trend of UFO sightings over time

# Interesting insight: The number of UFO sightings seems to increase significantly after the $1950$s

# $1910$ lowest recorded year

def site$2$year$($datafile$)$:

fig $=$ plt$.$figure$()$

ax $=$ fig.add$\_$subplot$()$

ax$.$hist$($datafile$[\text{'}$Dates$.$Documented.Year'$],$ bins$=$range$(1999,2015),$ align$=$'right'$)$

ax$.$set$\_$title$(\text{'}$Number of UFO Sightings by Year'$)$

ax$.$set$\_$xlabel$(\text{'}$Year$\text{'})$

ax$.$set$\_$ylabel$(\text{'}$Number of Sightings'$)$

plt$.$xticks$($rotation$=0)$ # Get or set the current tick locations and labels of the x$-$axis.

plt$.$xlim$($left$=$min$($datafile$[\text{'}$Dates$.$Documented.Year'$].$min$(),1999))$

plt$.$xlim$($right$=$max$($datafile$[\text{'}$Dates$.$Documented.Year'$].$max$(),2015))$

plt$.$grid$()$

plt$.$gca$().$set$\_$facecolor$(\text{'}$#f$5$f$5$f$5\text{'})$

#plt$.$savefig$(\text{'}$ufo$\_$sightings$\_$by$\_$year.png$\text{'})$ # saves a fancy image

plt$.$show$()$

print$("$The first visualization reveals an increase in UFO sightings after $1999.")$

# Second Visualization: Distribution of UFO Shapes

# This visualization helps us understand the most common shapes reported for UFOs

# Interesting insight: Circular shapes are the most commonly reported UFO shapes

def distshape$($datafile$)$:

fig $=$ plt$.$figure$()$

ax $=$ fig.add$\_$subplot$()$

ax$.$pie$($datafile$[\text{'}$Data$.$Shape'$].$value$\_$counts$().$sort$\_$index$(),$ labels$=$datafile$[\text{'}$Data$.$Shape'$].$value$\_$counts$().$index, autopct$=\text{'}\%1\mathrm{.}1$f$\%\%\text{'})$

ax$.$axis$(\text{'}$equal$\text{'})$

ax$.$set$\_$title$(\text{'}$Distribution of UFO Shapes'$)$

plt$.$show$()$

print$("$The second visualization shows that circular type shapes are the most common UFO shapes reported."$)$

def main$()$:

# Loading the CSV file into a DataFrame

ufo$\_$data $=$ pd$.$read$\_$csv$(\text{'}$ufo$\_$sightings.csv$\text{'})$ # reads file from panda, pd$.$

site$2$year$($ufo$\_$data$)$

distshape$($ufo$\_$data$)$

main$()$

# print$($ufo$\_$data.columns$)$