Severity index

import os
import matplotlib.pyplot as plt
import geopandas as gpd
import seaborn as sns
import matplotlib as mpl
import pandas as pd
import matplotlib.ticker as ticker
from sklearn.metrics import mean_squared_error
import numpy as np

data = {
    1946: np.nan,
    1947: 5,
    1948: 2,
    1950: 2,
    1955: 2,
    1956: 2,
    1958: 1,
    1962: 3,
    1963: 3,
    1974: 3,
    1975: 3,
    1976: 5,
    1985: 3,
    1986: 3,
    1987: 1,
    1995: 1,
    1998: 1,
    1999: 1,
    2001: 1,
    2002: 1,
    2007: 1,
    2008: 1,
    2009: 1,
    2012: 5,
    2013: 5,
    2018: 3,
    2020: 1,
    2022: 1,
}

data_ = pd.DataFrame(data, index=[0]).transpose().reset_index()

data_["time"] = pd.to_datetime(data_["index"], format="%Y")

data_ = data_.set_index("time").resample("Y").mean()

data_["year"] = data_.index.year

plt.figure(figsize=(12, 5))
ax = sns.barplot(data_, x="year", y=0, color="#762a83")
ax.set_xlabel("Year")
ax.set_ylabel("Severity Index")
ax.grid(which="major", axis="y")
ax.tick_params(axis="x", labelrotation=90)
ax.xaxis.set_major_locator(ticker.MultipleLocator(2))
sns.despine()
plt.tight_layout()
plt.show()