In [6]:
df_sum = pd.DataFrame({
'sum3': [sum3],
'sum15': [sum15]
})
df_sum['STATE1'] = 'U.S.'
print(df_sum)
df_sum=df_sum.merge(err_country, left_on='STATE1', right_on='STATE1')
print(df_sum)
# Define columns for plotting
columns_to_plot = ['sum15', 'p5_15', 'p95_15',
'sum3', 'p5_3', 'p95_3']
num_attributes = len(columns_to_plot)
# Define the colors you want for the bars
colors = [ 'cadetblue','lightsalmon']
# Set the width of each bar
bar_width = 0.4
# Set the positions of the bars on the x-axis
index = np.arange(len(df_sum['STATE1']))
# Expand the length of the y-axis
plt.figure(figsize=(5, 1))
# Create a bar for each attribute
for i in range(num_attributes // 3):
plt.barh(index + i * bar_width, df_sum[columns_to_plot[i * 3]],
bar_width,label=f'{columns_to_plot[i * 3]}', color=colors[i], edgecolor='black', # Add edge color
linewidth=0.7, capsize=3, capstyle='round') # Set capstyle to 'round'
main_data = df_sum[columns_to_plot[i* 3]]
error_lower = (df_sum[columns_to_plot[i* 3 + 1]]).values
error_upper = (df_sum[columns_to_plot[i* 3 + 2]]).values
xerr = [error_lower, error_upper] # Correct error shape
plt.errorbar(main_data, index + (i) * bar_width, xerr=np.array(xerr),
fmt='o', color='black', label=columns_to_plot[i],alpha = 0.6,
linewidth=2, capthick=2, capsize = 3, markersize=5)
# Set the y-axis labels to be the state names
plt.yticks([0.2], df_sum['STATE1'],fontsize=24, fontfamily='Arial')
# Set the x-ticks
x_ticks = np.arange(-360, 190, 180)
plt.xticks(x_ticks)
# Add a dashed line at x=0
ax = plt.gca()
# Set tick label font sizes
ax.tick_params(axis='both', labelsize=24)
ax.set_xticklabels(ax.get_xticklabels(), fontfamily='Arial')
ax.set_yticklabels(ax.get_yticklabels(), fontfamily='Arial')
# Save the entire figure as a JPG with DPI 300
plt.savefig('national_maize_net_benefit.svg', format='svg', dpi=300, bbox_inches='tight', pad_inches=0.02, transparent=True)
# Show the plot
plt.show()
sum3 sum15 STATE1
0 -88.202629 -21.977725 U.S.
sum3 sum15 STATE1 p95_15 p5_15 p95_3 p5_3
0 -88.202629 -21.977725 U.S. 80.847734 82.150331 200.491853 228.465142
