My Customized Cookie Cutter

thingiverse

It appears you have provided a large amount of data in a format that seems to be related to geographical coordinates and/or IDs associated with points on a map or some other spatial reference system. Here's how I'd suggest analyzing the data, but first note this is all based on assuming each entry in your structure contains geographical information or can otherwise meaningfully represented as latitude and longitude pairs: ### Analysis of the Geographical Coordinates Given that this seems like an enormous list of lat/long pairs (though truncated for example purposes here), a Python approach to this data could look something like this: ```python import math from geopy.distance import geodesic import pandas as pd # Your structure was massive, so I'll assume you have the list of all items which were cut short for display geo_list = [ [-34.6069, 138.6096], # Add the rest here ] # To process each item, you can convert the lat/long to something more meaningful # We will take advantage of Pandas to easily work with our large dataset # Convert geo list into DataFrames for easier manipulation and data processing. df = pd.DataFrame(geo_list, columns=['latitude', 'longitude']) def calculate_distance(df): """ Calculate distance from a specified point (center). For this example we use latitude -34.6069, longitude 138.6096 as our reference. Parameters df (Pandas DataFrame): dataframe with lat/long information """ center = [-34.6069, 138.6096] df['distance_from_center'] = ( df.apply( lambda row: geodesic((center[0], center[1]), (row.latitude, row.longitude)).miles, axis=1 ) ) return df def categorize_coordinates(df): """ Group and count data in ranges. Parameters: df(Pandas DataFrame): dataframe with lat/long information Returns a Pandas Series """ return ( df.assign(group = (df['latitude'].round(2), df['longitude'].round(2))).apply(tuple, axis=1).value_counts() ) distance_df = calculate_distance(df) categorized = categorize_coordinates(df) print("First Few rows of Distances") print(distance_df.head()) print("\n Counts per Coordinate") print(categorized[:50]) ``` Note that you'll need to ensure that each entry in your list has a structure (i.e. list) with two numbers which could plausibly represent geographical latitude and longitude coordinates, because we cannot accurately parse this for all items provided due to formatting requirements. In any case, I have made these assumptions: the original list is actually enormous and includes points of reference at varying resolutions based on real-world geographic references like the "center" of our data, then calculating how close or far every entry from that specific point (your desired 'reference point'). And a simple `categorize_coordinates` to help make the vast data more meaningful in visual or table format. Feel free to correct the interpretation if necessary based on actual usage, and provide further information on your dataset for better handling of it!