<jupyter_start><jupyter_text>The purpose of this notebook is to test out different data sets in order to decide which suits best to work on  Problem statementFor this project you must create a data set by `simulating a real-world phenomenon ofyour choosing.` You may pick any phenomenon you wish – you might pick one that isof interest to you in your personal or professional life. Then, rather than collect datarelated to the phenomenon, you should `model and synthesise such data using Python.`We suggest you use the numpy.random package for this purpose.    Specifically, in this project you should:    * Choose a real-world phenomenon that can be measured and for which you could collect at least one-hundred data points across at least four different variables.* Investigate the types of variables involved, their likely distributions, and their relationships with each other.* Synthesise/simulate a data set as closely matching their properties as possible.* Detail your research and implement the simulation in a Jupyter notebook – the data set itself can simply be displayed in an output cell within the notebook.Note that `this project is about simulation` – you must synthesise a data set. Somestudents may already have some real-world data sets in their own files. It is okay tobase your synthesised data set on these should you wish (please reference it if you do),but the main task in this project is to create a synthesised data set. The next sectiongives an example project idea-------------------  Ideas for projectCorona virus * galway specefic* compare with national / international.* simulate exponential growth if no lockdown?   Corona Virus<jupyter_code>import pandas as pd
data = pd.read_csv("https://opendata-geohive.hub.arcgis.com/datasets/d9be85b30d7748b5b7c09450b8aede63_0.csv?outSR=%7B%22latestWkid%22%3A3857%2C%22wkid%22%3A102100%7D") 
data
data.head()
data["CountyName"].value_counts()

galData = data.loc[data["CountyName"] == "Galway"]
galData
df = galData[["TimeStamp", "CountyName", "ConfirmedCovidCases",  "ConfirmedCovidDeaths" ]]
df
import matplotlib.pyplot as plt
df.plot(x="TimeStamp", y="ConfirmedCovidCases")<jupyter_output><empty_output><jupyter_text>Marathon Results Marathon resultshttps://www.kaggle.com/rojour/boston-resultshttps://towardsdatascience.com/half-marathon-finish-time-prediction-part-1-5807760033ebhttps://www.kaggle.com/daniboy370/boston-marathon-2019https://raw.githubusercontent.com/adrian3/Boston-Marathon-Data-Project/master/results2019.csvhttps://github.com/adrian3/Boston-Marathon-Data-Project<jupyter_code>marathonData = pd.read_csv("./results2019.csv") 
marathonData

marathonData.head()
mdf = marathonData[["gender", "age", "official_time",  "place_overall" ]]
mdf<jupyter_output><empty_output><jupyter_text>Strava Data<jupyter_code>strava = pd.read_csv("./Strava/activities.csv") 
strava

dfStrava = strava[["Activity Date", "Activity Type", "Elapsed Time",  "Distance", "Moving Time", "Max Speed","Average Speed","Calories"  ]]
dfStrava
StravaData = dfStrava.loc[dfStrava["Activity Type"] == "Run"]
StravaData
x = StravaData["Distance"]
y = StravaData["Activity Date"]
plt.hist(x)<jupyter_output><empty_output>
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