• Perform any additional steps (parsing dates, creating additional columns, merging multiple dataset etc.)

• Compute the mean, sum, range and other interesting statistics for numeric columns

• Explore distributions of numeric columns using histograms etc.

• Explore relationship between columns using scatter plots, bar charts etc.

• Make a note of interesting insights from the exploratory analysis

• Create new columns, merge multiple dataset and perform grouping/aggregation wherever necessary

• Wherever you're using a library function from Pandas/Numpy/Matplotlib etc. explain briefly what it does

• Write a summary of what you've learned from the analysis

• Include interesting insights and graphs from previous sections

• Share ideas for future work on the same topic using other relevant datasets

• Share links to resources you found useful during your analysis

Project Title: Asteroid Mining - basic study

Inspired by the Asteroid dataset in Kaggle, I created my asteroid datasets using NASA database https://ssd.jpl.nasa.gov/sbdb_query.cgi#x and https://cneos.jpl.nasa.gov/ca/. These asteroid datasets include data used for estimation of the astoroid properties, useful for discussion about Asteroid Mining.
First dataset simple_asteroid_db.csv includes:

• full_name - object full name/designation
• GM - standard gravitational parameter: mass (M) * gravitational constant (G) (km^3/s^2)
• diameter - object diameter (from equivalent sphere) (km)
• diameter_sigma - 1-sigma uncertainty in object diameter (km)
• spec_B - spectral taxonomic type (SMASSII)
• neo - Near-Earth Object (NEO) flag (Y/N)

Second dataset cneos_closeapproach_data.csv includes:

• object - object primary designation
• close-approach(CA)_date - date and time (TDB) of closest Earth approach. "Nominal Date" is given to appropriate precision. The 3-sigma uncertainty in the time is given in the +/- column in days_hours:minutes format (for example, "2_15:23" is 2 days, 15 hours, 23 minutes; "< 00:01" is less than 1 minute)
• CA_distance_nominal - the most likely (Nominal) close-approach distance (Earth center to NEO center), in au - astronomical unit (1 au = 149597871 km)
• V_relative - object velocity relative to Earth at close-approach in (km/s).

Using these parameters I'll try to show, which asteroids are interesting from the point of view of their composition and also from the possibility of mining.

This is a very basic study, made for the final project in the course Data Analysis with Python: Zero to Pandas

#, and what you've learned from it.

Downloading the Dataset

Dataset can be downloaded from kaggle.

Instructions for downloading the dataset (delete this cell)

• Find an interesting dataset on this page: https://www.kaggle.com/datasets?fileType=csv
• The data should be in CSV format, and should contain at least 3 columns and 150 rows
• Download the dataset using the opendatasets Python library