import pandas as pd
import matplotlib.pyplot as plt

sample = pd.read_excel(r'...\sample_code.xlsx', sheet_name='sample')

print(sample)

# read the data from the sample data "replicate of Yang and Preckel's empirical bootstrapped estimates"
Income = list(sample['Income'])

BP = list(sample['BP'])
BL = list(sample['BL'])
BU = list(sample['BU'])

MP = list(sample['MP'])
ML = list(sample['ML'])
MU = list(sample['MU'])

CP = list(sample['CP'])
CL = list(sample['CL'])
CU = list(sample['CU'])

FP = list(sample['FP'])
FL = list(sample['FL'])
FU = list(sample['FU'])

# Use subplots function and specify the space of graphs (two x two)
figure, axis = plt.subplots(2, 2)

# Starting from [0, 0]: first row, first column
plt.style.use('seaborn')
axis[0, 0].scatter(Income, BP, marker="*", s=30, edgecolors="Red", c="Red")
axis[0, 0].scatter(Income, BL, marker="X", s=30, edgecolors="Orange", c="Orange")
axis[0, 0].scatter(Income, BU, marker="h", s=30, edgecolors="mediumblue", c="mediumblue")
axis[0, 0].set_title("Bread and Cereals")

# [0, 1]: first row, second column
axis[0, 1].scatter(Income, MP, marker="*", s=30, edgecolors="Red", c="Red")
axis[0, 1].scatter(Income, ML, marker="X", s=30, edgecolors="Orange", c="Orange")
axis[0, 1].scatter(Income, MU, marker="h", s=30, edgecolors="mediumblue", c="mediumblue")
axis[0, 1].set_title("Meat, Seafood and Dairy")

# [1, 0]: second row, first column
axis[1, 0].scatter(Income, CP, marker="*", s=30, edgecolors="Red", c="Red")
axis[1, 0].scatter(Income, CL, marker="X", s=30, edgecolors="Orange", c="Orange")
axis[1, 0].scatter(Income, CU, marker="h", s=30, edgecolors="mediumblue", c="mediumblue")
axis[1, 0].set_title("Calorie-Dense Foods")

# [1, 1]: second row, second column
axis[1, 1].scatter(Income, FP, marker="*", s=30, edgecolors="Red", c="Red")
axis[1, 1].scatter(Income, FL, marker="X", s=30, edgecolors="Orange", c="Orange")
axis[1, 1].scatter(Income, FU, marker="h", s=30, edgecolors="mediumblue", c="mediumblue")
axis[1, 1].set_title("Fruits and Vegetables")

# Remove the tick labels and keep the ones at the bottom
axis[0, 0].axes.xaxis.set_ticklabels([])
axis[0, 1].axes.xaxis.set_ticklabels([])

# Set labels on X and Y axes, adjust their positions based on preferences
axis[1, 0].set_ylabel('Income Elasticities of Demand', fontweight='bold', fontsize=14, labelpad=15, y=1.11)
axis[1, 0].set_xlabel('Per Capita Expenditure in Thousand U.S. Dollars',
                      fontweight='bold', fontsize=14, labelpad=15, x=1.11)

# Plot legend, "bbox_to_anchor" may be used to allow manual placement
plt.legend(["Point Estimate", "Lower 90%", "Upper 90%"])
plt.show()