# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from google.colab import drive
# Mount Google Drive
drive.mount('/content/drive')
# Load the data
file_path = '/content/drive/My Drive/dataset/az_0704.csv'
data = pd.read_csv(file_path)
# Descriptive statistics
desc_stats = data.describe()
print("Descriptive Statistics:")
print(desc_stats)
# Box plot to visualize distribution and check for outliers
plt.figure(figsize=(16, 10))
sns.boxplot(data=data.select_dtypes(include=[np.number])) # Only plot numerical columns
plt.xticks(rotation=90)
plt.title('Box Plot of Features')
plt.show()
# Pearson correlation coefficient
numerical_data = data.select_dtypes(include=[np.number]) # Select only numerical columns
correlation_matrix = numerical_data.corr(method='pearson')
# Heatmap to visualize correlation between variables
plt.figure(figsize=(16, 10))
sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm')
plt.title('Heatmap of Pearson Correlation Coefficients')
plt.show()
Descriptive Statistics:
location_id year az100k gdppp2017 pop_65y_pct \
count 5278.000000 5278.000000 5278.000000 5084.000000 5277.000000
mean 121.653846 2004.000000 482.064222 16294.764183 6.783686
std 79.268908 8.367393 416.042637 18744.217062 4.976065
min 6.000000 1990.000000 33.614658 430.413544 0.170000
25% 60.000000 1997.000000 166.808465 3323.306472 3.180000
50% 115.500000 2004.000000 302.029322 9404.266637 4.560000
75% 173.000000 2011.000000 715.899212 21989.983638 9.760000
max 522.000000 2018.000000 2989.455411 120647.823000 28.900000
primaryedu_year hyperten_100k depress100k nonhdl_mgdl AZDALYS_100K \
count 5256.000000 5278.000000 5278.000000 5278.000000 5278.000000
mean 5.715373 146.414885 3465.718431 130.069746 308.305664
std 0.912770 102.381942 861.010131 20.029327 268.414948
min 3.000000 15.575297 1425.210658 77.339520 23.282751
25% 5.000000 75.126248 2826.171856 112.142305 115.806096
50% 6.000000 112.906913 3334.594173 131.477185 199.230829
75% 6.000000 190.788855 4035.816012 143.078113 433.587815
max 9.000000 767.819542 7113.698255 181.747873 1843.962872
Gini coefficient (Pretax) (Estimated) \
count 1446.000000
mean 50.406392
std 9.317352
min 29.731230
25% 43.604179
50% 48.478420
75% 55.305236
max 76.166683
Richest decile - Share (Pretax) (Estimated) \
count 1446.000000
mean 38.925754
std 9.243309
min 22.710000
25% 32.222503
50% 35.915000
75% 44.267501
max 67.830000
Top 1% - Share (Pretax) (Estimated) gdpg1 OECD
count 1446.000000 5278.000000 5278.000000
mean 13.013458 2.000000 0.208791
std 4.892647 1.422097 0.406483
min 3.680000 0.000000 0.000000
25% 9.670000 1.000000 0.000000
50% 11.460000 2.000000 0.000000
75% 15.595000 3.000000 0.000000
max 35.870000 4.000000 1.000000
