cbpb-box-plot.py

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set()

# Read csv
bloom = pd.read_csv("./assets/data/peak-bloom-clean.csv")

df = pd.DataFrame(bloom, columns = ['year', 'date', 'period', 'estimated_temp'])

# Convert str to date
df['date'] = df['date'].astype('str').str.zfill(4)
df['year_date'] = df['year'].astype(str) + df['date'].astype(str)
df['year_date'] = pd.to_datetime(df['year_date'])
df = df.sort_values('date')

# Set a color palette
color_palette = sns.cubehelix_palette(36, reverse=True)

# Generate box plot
sns.boxplot(x=df['year_date'].dt.strftime("%b %d"), y='year', data=df, palette=color_palette)
# The total height of the box contains the middle 50% of the data, and is called the interquartile range, or IQR.

# Label the axes
plt.xlabel('Cherry blossom peak-bloom date').set_color('#2d1e3e')
plt.ylabel('Year').set_color('#2d1e3e')
plt.xticks(rotation=70, color='#2d1e3e')
plt.yticks(color='#2d1e3e')

# Plot title
plt.title('Cherry blossom peak-bloom date in Kyoto, Japan (1678 - 2021)').set_color('#2d1e3e')

# Show the plot
plt.show()