Pandas Datetime: Timewheel of Hour Vs Year comparison of the top 10 years in which the UFO was sighted

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.cm as cm
#Source: https://bit.ly/2XDY2XN
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:")
def pie_heatmap(table, cmap='coolwarm_r', vmin=None, vmax=None,inner_r=0.25, pie_args={}):
   n, m = table.shape
   vmin= table.min().min() if vmin is None else vmin
   vmax= table.max().max() if vmax is None else vmax

   centre_circle = plt.Circle((0,0),inner_r,edgecolor='black',facecolor='white',fill=True,linewidth=0.25)
   plt.gcf().gca().add_artist(centre_circle)
   norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
   cmapper = cm.ScalarMappable(norm=norm, cmap=cmap)

   for i, (row_name, row) in enumerate(table.iterrows()):
       labels = None if i > 0 else table.columns
       wedges = plt.pie([1] * m,radius=inner_r+float(n-i)/n, colors=[cmapper.to_rgba(x) for x in row.values],
           labels=labels, startangle=90, counterclock=False, wedgeprops={'linewidth':-1}, **pie_args)
       plt.setp(wedges[0], edgecolor='grey',linewidth=1.5)
       wedges = plt.pie([1], radius=inner_r+float(n-i-1)/n, colors=['w'], labels=[row_name], startangle=-90, wedgeprops={'linewidth':0})
       plt.setp(wedges[0], edgecolor='grey',linewidth=1.5)
plt.figure(figsize=(8,8))
plt.title("Timewheel of Hour Vs Year",y=1.08,fontsize=30)
pie_heatmap(month_vs_year, vmin=-20,vmax=80,inner_r=0.2)