Pandas Datetime: Create a comparison of the top 10 years in which the UFO was sighted vs each Month
Write a Pandas program to create a comparison of the top 10 years in which the UFO was sighted vs each Month.
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
month_vs_year = df.pivot_table(columns=df['Date_time'].dt.month,index=df['Date_time'].dt.year.apply(is_top_years),aggfunc='count',values='city')
month_vs_year.index = month_vs_year.index.astype(int)
month_vs_year.columns = month_vs_year.columns.astype(int)
print("\nComparison of the top 10 years in which the UFO was sighted vs each month:")
print(month_vs_year.head(10))
Sample Output:
Comparison of the top 10 years in which the UFO was sighted vs each month: Date_time 1 2 3 4 5 6 7 8 9 10 11 12 Date_time 1993 NaN NaN 1.0 1.0 NaN 1.0 3.0 2.0 3.0 NaN 1.0 NaN 1994 2.0 NaN 3.0 2.0 2.0 NaN NaN 1.0 NaN NaN NaN 1.0 1995 2.0 1.0 NaN 1.0 1.0 1.0 3.0 NaN 1.0 NaN 2.0 NaN 1996 NaN 1.0 NaN 1.0 1.0 1.0 3.0 3.0 1.0 NaN 1.0 NaN 1997 NaN 2.0 1.0 NaN 2.0 1.0 3.0 1.0 1.0 1.0 1.0 1.0 1998 1.0 2.0 1.0 3.0 NaN 2.0 1.0 NaN NaN 1.0 NaN 2.0 1999 NaN NaN 2.0 NaN 1.0 2.0 4.0 NaN NaN 1.0 NaN 1.0 2000 NaN 3.0 2.0 NaN 2.0 1.0 1.0 NaN NaN NaN 1.0 2.0 2001 2.0 1.0 2.0 2.0 1.0 2.0 NaN 1.0 2.0 NaN 1.0 1.0 2002 3.0 1.0 1.0 NaN 3.0 NaN 2.0 1.0 2.0 1.0 NaN NaN
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 heatmap (rectangular data as a color-encoded matrix) for comparison of the top 10 years in which the UFO was sighted vs each Month.
What is the difficulty level of this exercise?
- Weekly Trends and Language Statistics
- Weekly Trends and Language Statistics