In [10]:
# Calculate additional attributes
state_sum15['irri_mana_t'] = state_sum15['sus_irri_mana_t'] + state_sum15['unsus_irri_mana_t']
obs_t = state_sum15['obs_t'].sum()
sus_irri = state_sum15['sus_irri_t'].sum()
unsus_irri = state_sum15['unsus_irri_t'].sum()
sus_irri_mana = state_sum15['sus_irri_mana_t'].sum()
unsus_irri_mana = state_sum15['unsus_irri_mana_t'].sum()
print(sus_irri,unsus_irri,sus_irri_mana, unsus_irri_mana)
sus_irri_p=sus_irri/obs_t*100
unsus_irri_p=unsus_irri/obs_t*100
sus_irri_mana_p=sus_irri_mana/obs_t*100
unsus_irri_mana_p=unsus_irri_mana/obs_t*100
df_sum = pd.DataFrame({
'sus_irri_p': [sus_irri_p],
'unsus_irri_p': [unsus_irri_p],
'sus_irri_mana_p': [sus_irri_mana_p],
'unsus_irri_mana_p': [unsus_irri_mana_p]
})
print(df_sum)
df_sum['STATE1']='U.S.'
df_sum['t']=df_sum['sus_irri_p']+df_sum['unsus_irri_p']
df_sum['t_mana']=df_sum['sus_irri_mana_p']+df_sum['unsus_irri_mana_p']
df_sum=df_sum.merge(err_country15, left_on='STATE1', right_on='STATE1')
print(df_sum)
# Define the columns you want to plot
columns_to_plot = ['sus_irri_mana_p','unsus_irri_mana_p', 'sus_irri_p','unsus_irri_p']
columns_to_plot1 = ['t_mana', 'p5_mana_15','p95_mana_15','t','p5_15','p95_15']
# Number of attributes
num_attributes = len(columns_to_plot)
# Define the colors you want for the bars
colors = ['#fff59d', '#fff59d', '#b2ebf2', '#b2ebf2'] # Add more colors if needed
# Set the width of each bar
bar_width = 0.4
# Set the positions of the bars on the y-axis
index = np.arange(len(df_sum['STATE1']))
# Create a figure and axis
fig, ax = plt.subplots(figsize=(5, 1)) # Adjust the figure size as needed
# Initialize left positions for each group
left_group1 = np.zeros(len(df_sum['STATE1']))
left_group2 = np.zeros(len(df_sum['STATE1']))
# Create a bar for each attribute
for i in range(num_attributes):
hatch_pattern = '' # Initialize hatch pattern
if 'unsus_irri_mana_p' in columns_to_plot[i]:
hatch_pattern = '//'
elif 'unsus_irri_p' in columns_to_plot[i]:
hatch_pattern = '//' # Add a different hatch for 'unsus_irri_t' columns
if i < 2: # First two columns
plt.barh(index, df_sum[columns_to_plot[i]],
bar_width, label=f'{columns_to_plot[i]}', color=colors[i], hatch=hatch_pattern,
edgecolor='black', left=left_group1, zorder=1, capsize=3, capstyle='round')
left_group1 += df_sum[columns_to_plot[i]]
main_data = df_sum[columns_to_plot1[0]]
error_lower = (df_sum[columns_to_plot1[ 1]]).values
error_upper = (df_sum[columns_to_plot1[ 2]]).values
xerr = [error_lower, error_upper] # Correct error shape
plt.errorbar(main_data, index, xerr=np.array(xerr),
fmt='o', color='black', label=columns_to_plot1[i],alpha = 0.6,
linewidth=2, capthick=2, capsize = 3, markersize=5)
else: # Latter two columns
plt.barh(index + bar_width, df_sum[columns_to_plot[i]],
bar_width, label=f'{columns_to_plot[i]}', color=colors[i], hatch=hatch_pattern,
edgecolor='black', left=left_group2, zorder=1, capsize=3, capstyle='round')
left_group2 += df_sum[columns_to_plot[i]]
main_data = df_sum[columns_to_plot1[3]]
error_lower = (df_sum[columns_to_plot1[4]]).values
error_upper = (df_sum[columns_to_plot1[5]]).values
xerr = [error_lower, error_upper] # Correct error shape
plt.errorbar(main_data, index+bar_width, xerr=np.array(xerr),
fmt='o', color='black', label=columns_to_plot1[i],alpha = 0.6,
linewidth=2, capthick=2, capsize = 3, markersize=5)
# Set y-tick labels
plt.yticks([0], df_sum['STATE1'], fontfamily='Arial')
# Set labels and title
ax.set_yticks(index)
ax.set_yticklabels(df_sum['STATE1'], fontfamily='Arial', fontsize=20)
ax.set_xticks(np.arange(-100, 900, 200))
ax.axvline(0, color='black', linestyle='--', linewidth=1)
ax.tick_params(axis='x', labelsize=20)
# Save the entire figure as a JPG with DPI 300
plt.savefig('national_soybean_irri_15.png', format='png', dpi=300, bbox_inches='tight',
pad_inches=0.02, transparent=True)
# Show the plot
plt.show()
5.514359917403042 2.6846049953937583 -0.789165656222785 -0.5220953863398782
sus_irri_p unsus_irri_p sus_irri_mana_p unsus_irri_mana_p
0 269.744143 131.321946 -38.603359 -25.53917
sus_irri_p unsus_irri_p sus_irri_mana_p unsus_irri_mana_p STATE1 \
0 269.744143 131.321946 -38.603359 -25.53917 U.S.
t t_mana p95_15 p5_15 p95_mana_15 p5_mana_15
0 401.066089 -64.142528 41.683722 31.921113 4.534483 4.328059
