Pandas Datetime: Get all the sighting days of the unidentified flying object (ufo) between 1950-10-10 and 1960-10-10
Pandas Datetime: Exercise-5 with Solution
Write a Pandas program to get all the sighting days of the unidentified flying object (ufo) between 1950-10-10 and 1960-10-10.
Sample Solution :
Python Code :
import pandas as pd
df = pd.read_csv(r'ufo.csv')
df['Date_time'] = df['Date_time'].astype('datetime64[ns]')
print("Original Dataframe:")
print(df.head())
print("\nSighting days of the unidentified flying object (ufo) between 1949-10-10 and 1960-10-10:")
selected_period = df[(df['Date_time'] >= '1950-01-01 00:00:00') & (df['Date_time'] <= '1960-12-31 23:59:59')]
print(selected_period)
Sample Output:
Original Dataframe: Date_time city ... latitude longitude 0 1910-06-01 15:00:00 wills point ... 32.709167 -96.008056 1 1920-06-11 21:00:00 cicero ... 40.123889 -86.013333 2 1929-07-05 14:00:00 buchanan (or burns) ... 43.642500 -118.627500 3 1931-06-01 13:00:00 abilene ... 38.917222 -97.213611 4 1939-06-01 20:00:00 waterloo ... 34.918056 -88.064167 [5 rows x 11 columns] Sighting days of the unidentified flying object (ufo) between 1949-10-10 and 1960-10-10: Date_time ... longitude 29 1950-06-01 16:00:00 ... -89.116667 30 1950-06-01 20:00:00 ... -79.996111 31 1950-08-01 04:00:00 ... -85.759444 32 1950-10-01 11:00:00 ... -82.518889 33 1951-06-01 07:00:00 ... -99.950000 34 1951-07-01 03:00:00 ... -117.105278 35 1951-02-03 22:00:00 ... -72.599444 36 1951-06-03 13:00:00 ... -77.206944 37 1952-07-01 15:00:00 ... -95.088611 38 1952-07-01 22:00:00 ... -83.045833 39 1952-08-01 21:30:00 ... -82.458611 40 1952-10-01 12:00:00 ... -94.578333 41 1953-04-01 15:00:00 ... -71.077778 42 1953-04-01 18:00:00 ... -71.106111 43 1953-07-01 05:30:00 ... -104.820833 44 1953-08-01 12:00:00 ... -90.331111 45 1954-02-01 02:00:00 ... -147.716389 46 1954-06-01 00:00:00 ... -95.363056 47 1954-06-01 06:00:00 ... -76.823333 48 1954-06-01 08:00:00 ... -89.643611 49 1955-05-01 15:00:00 ... -71.009167 50 1955-06-01 02:00:00 ... -95.398056 51 1955-06-01 15:29:00 ... -84.456944 52 1955-06-01 17:00:00 ... -122.133056 53 1956-01-01 05:30:00 ... -80.589722 54 1956-03-01 13:00:00 ... -122.635556 55 1956-05-01 12:00:00 ... -81.378611 56 1956-06-01 19:00:00 ... -94.531667 57 1957-01-01 21:00:00 ... -96.800000 58 1957-05-01 12:00:00 ... -81.378611 59 1957-06-01 10:00:00 ... -106.486389 60 1957-06-01 20:00:00 ... -73.644444 61 1958-01-01 22:00:00 ... -102.557778 62 1958-06-01 02:00:00 ... -78.204167 63 1958-06-01 19:00:00 ... -122.418333 64 1958-06-01 21:00:00 ... -74.006389 65 1959-04-01 01:00:00 ... -80.193889 66 1959-05-01 18:30:00 ... -82.998889 67 1959-06-01 12:00:00 ... -73.026111 68 1959-06-01 18:30:00 ... -84.155556 69 1960-02-01 22:15:00 ... -93.093056 70 1960-02-01 23:00:00 ... -82.932222 71 1960-04-01 21:00:00 ... -95.363056 72 1960-05-01 20:00:00 ... -110.925833 [44 rows x 11 columns]
Python Code Editor:
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https://w3resource.com/python-exercises/pandas/datetime/pandas-datetime-exercise-5.php
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