Pandas Datetime: Get the average mean of the UFO sighting was reported
Pandas Datetime: Exercise-17 with Solution
Write a Pandas program to get the average mean of the UFO (unidentified flying object) sighting was reported.
import pandas as pd #Source: https://bit.ly/32kGinQ df = pd.read_csv(r'ufo.csv') df['date_documented'] = df['date_documented'].astype('datetime64[ns]') print("Original Dataframe:") print(df.head()) # Add a new column instance, this adds a value to each instance of ufo sighting df['instance'] = 1 # set index to time, this makes df a time series df and then you can apply pandas time series functions. df.set_index(df['date_documented'], drop=True, inplace=True) # create another df by resampling the original df and counting the instance column by Month ('M' is resample by month) ufo2 = pd.DataFrame(df['instance'].resample('M').count()) # just to find month of resampled observation ufo2['date_documented'] = pd.to_datetime(ufo2.index.values) ufo2['month'] = ufo2['date_documented'].apply(lambda x: x.month) print("Average mean of the UFO (unidentified flying object) sighting was reported:") print(ufo2.groupby(by='month').mean())
Original Dataframe: Date_time city ... latitude longitude 0 06-01-1910 15:00 wills point ... 32.709167 -96.008056 1 06-11-1920 21:00 cicero ... 40.123889 -86.013333 2 07-05-1929 14:00 buchanan (or burns) ... 43.642500 -118.627500 3 06-01-1931 13:00 abilene ... 38.917222 -97.213611 4 06-01-1939 20:00 waterloo ... 34.918056 -88.064167 [5 rows x 11 columns] Average mean of the UFO (unidentified flying object) sighting was reported: instance month 1 2.875000 2 2.500000 3 1.764706 4 2.250000 5 1.375000 6 1.187500 7 1.562500 8 2.062500 9 1.312500 10 1.687500 11 1.437500 12 1.562500
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Previous: Write a Pandas program to manipulate and convert date times with timezone information.
Next: Write a Pandas program to create a graphical analysis of UFO (unidentified flying object) Sightings year.
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Python: Tips of the Day
Returns the symmetric difference between two lists, after applying the provided function to each list element of both
def tips_symmetric_difference_by(p, q, fn): _p, _q = set(map(fn, p)), set(map(fn, q)) return [item for item in p if fn(item) not in _q] + [item for item in q if fn(item) not in _p] from math import floor print(tips_symmetric_difference_by([4.2, 2.4], [4.6, 6.8],floor))
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