Pandas Datetime: Create a comparison of the top 10 years in which the UFO was sighted vs the hours of the day
Pandas Datetime: Exercise-22 with Solution
Write a Pandas program to create a comparison of the top 10 years in which the UFO was sighted vs the hours of the day.
Sample Solution:
Python Code:
import pandas as pd
#Source: https://bit.ly/1l9yjm9
df = pd.read_csv(r'ufo.csv')
df['Date_time'] = df['Date_time'].astype('datetime64[ns]')
most_sightings_years = df['Date_time'].dt.year.value_counts().head(10)
def is_top_years(year):
if year in most_sightings_years.index:
return year
hour_v_year = df.pivot_table(columns=df['Date_time'].dt.hour,index=df['Date_time'].dt.year.apply(is_top_years),aggfunc='count',values='city')
hour_v_year.columns = hour_v_year.columns.astype(int)
hour_v_year.columns = hour_v_year.columns.astype(str) + ":00"
hour_v_year.index = hour_v_year.index.astype(int)
print("\nComparison of the top 10 years in which the UFO was sighted vs the hours of the day:")
print(hour_v_year.head(10))
Sample Output:
Comparison of the top 10 years in which the UFO was sighted vs the hours of the day: 0:00 1:00 2:00 4:00 ... 20:00 21:00 22:00 23:00 Date_time ... 1993 1.0 1.0 1.0 NaN ... 2.0 NaN NaN 4.0 1994 NaN NaN NaN NaN ... NaN 4.0 2.0 1.0 1995 NaN NaN 1.0 1.0 ... 2.0 1.0 1.0 3.0 1996 NaN 1.0 NaN NaN ... 1.0 NaN 2.0 1.0 1997 NaN 1.0 NaN 1.0 ... NaN 4.0 1.0 2.0 1998 2.0 1.0 NaN NaN ... 2.0 2.0 2.0 NaN 1999 2.0 NaN 1.0 NaN ... NaN 2.0 1.0 2.0 2000 NaN NaN 1.0 NaN ... 4.0 2.0 2.0 1.0 2001 3.0 1.0 NaN 1.0 ... 1.0 5.0 NaN NaN 2002 NaN 1.0 NaN NaN ... 2.0 NaN NaN 3.0 [10 rows x 20 columns]
Python Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
Previous: Write a Pandas program to create a comparison of the top 10 years in which the UFO was sighted vs the hours of the day.
Next: Write a Pandas program to create a comparison of the top 10 years in which the UFO was sighted vs each Month.
What is the difficulty level of this exercise?
Python: Tips of the Day
F strings:
It is a common practice to add variables inside strings. F strings are by far the coolest way of doing it. To appreciate the f strings more, let's first perform the operation with the format function.
name = 'Owen' age = 25 print("{} is {} years old".format(name, age))
Output:
Owen is 25 years old
We specify the variables that go inside the curly braces by using the format function at the end. F strings allow for specifying the variables inside the string.
name = 'Owen' age = 25 print(f"{name} is {age} years old")
Output:
Owen is 25 years old
F strings are easier to follow and type. Moreover, they make the code more readable.
A, B, C = {2, 4, 6} print(A, B, C) A, B, C = ['p', 'q', 'r'] print(A, B, C)
Output:
2 4 6 p q r
- Weekly Trends
- Java Basic Programming Exercises
- SQL Subqueries
- Adventureworks Database Exercises
- C# Sharp Basic Exercises
- SQL COUNT() with distinct
- JavaScript String Exercises
- JavaScript HTML Form Validation
- Java Collection Exercises
- SQL COUNT() function
- SQL Inner Join
- JavaScript functions Exercises
- Python Tutorial
- Python Array Exercises
- SQL Cross Join
- C# Sharp Array Exercises