Printing distribution of floats over 1 second intervals

.py

"""
This scripts loads a file that contains stringified floats expected
to be seconds since epoch listed in a comma-separated fashion as
follows:

"1638201720.793","1638201720.778","1638201720.770","1638201719.866","1638201710.152","1638201710.140","1638201709.727","1638201707.715","1638201704.069","1638201702.769","1638201702.757","1638201699.529","1638201699.363","1638201699.362","1638201689.308","1638201688.406","1638201686.458","1638201686.097","1638201685.893","1638201685.620","1638201685.350","1638201685.035","1638201684.650","1638201682.229","1638201679.695","1638201678.098","1638201677.825","1638201676.972","1638201676.640","1638201675.442","1638201674.020","1638201672.890","1638201672.862","1638201669.458","1638201669.364","1638201665.908","1638201665.606","1638201665.271","1638201665.138","1638201664.315","1638201664.298","1638201663.204","1638201662.234","1638201661.479","1638201661.368","1638201660.295","1638201660.256","1638201660.108","1638201660.097","1638201660.010","1638201659.797","1638201658.931","1638201658.926","1638201658.925","1638201658.894","1638201657.563","1638201656.421","1638201653.347","1638201652.219","1638201651.557","1638201651.417","1638201651.280","1638201650.007","1638201649.329","1638201649.284","1638201648.912","1638201648.256","1638201648.028","1638201648.016","1638201648.010","1638201648.007","1638201647.939","1638201647.014","1638201646.966","1638201645.884","1638201645.354","1638201644.284","1638201644.047","1638201643.989","1638201643.496","1638201643.395","1638201643.183","1638201643.074","1638201642.951","1638201642.801","1638201642.519","1638201640.536","1638201640.495","1638201639.679","1638201639.616","1638201639.566","1638201639.361","1638201639.357","1638201638.091","1638201637.022","1638201636.501","1638201636.446","1638201636.441","1638201636.357","1638201635.587","1638201635.326","1638201635.324","1638201634.504","1638201633.299","1638201633.267","1638201633.207","1638201632.386","1638201630.017","1638201629.448","1638201629.388","1638201628.803","1638201628.253","1638201627.983","1638201627.928","1638201627.361","1638201626.763","1638201626.584","1638201626.532","1638201625.730","1638201625.392","1638201624.553","1638201623.685","1638201623.682","1638201623.680","1638201623.660","1638201620.751","1638201618.390","1638201617.259","1638201617.192","1638201617.148","1638201617.107","1638201616.227","1638201616.097","1638201615.958","1638201615.857","1638201615.842","1638201615.591","1638201614.538","1638201613.650","1638201613.477","1638201613.469","1638201613.468","1638201613.462","1638201613.431","1638201611.697","1638201610.543","1638201609.388","1638201609.382","1638201606.520","1638201606.030","1638201605.886","1638201605.745","1638201605.581","1638201605.430","1638201605.284","1638201600.651","1638201600.141","null","null","null",%

and prints an output like the following to show distribution of times
in buckets where each bucket represents a second:

2021-11-29 16:02:00: 2      ▇▇
2021-11-29 16:01:59: 1      ▇
2021-11-29 16:01:50: 2      ▇▇
2021-11-29 16:01:49: 1      ▇
2021-11-29 16:01:47: 1      ▇
2021-11-29 16:01:44: 1      ▇

...
"""
import collections
from datetime import datetime
import pprint


with open('us-east-1/start_times') as file:
    start_times = [
        float(start_time.strip().strip('"'))
        for start_time in file.read().split(",")
        if start_time.strip().strip('"') and start_time.strip().strip('"') != "null"
    ]
    start_times = sorted(start_times, reverse=True)
    periods = collections.Counter()
    for i in range(1, len(start_times), 1):
        periods[int(start_times[i])] += 1
    for key, value in periods.items():
        val = '▇' * value
        key = str(datetime.fromtimestamp(key))
        print(f"{key}: {str(value).ljust(6)} {val}")