landanalysis

landanalysis.py

+
+
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+
+brfss_data = np.genfromtxt('tankrelatedarea.csv', dtype=np.int32, delimiter=',', skip_header=1)
+
+
+print()
+print("First Five land of the Data:")
+# Indexing starts at 0
+print(brfss_data[:5, ])
+print("area of the land:", brfss_data.shape)
+print()
+
+
+
+weight_change = brfss_data[:, 2] - brfss_data[:, 3]
+print("Descriptive Statistics for hight from tank:")
+
+# Calculate & display mean for weight change
+land_val = np.mean(weight_change)
+print("Mean:", land_val.round(decimals=2))
+
+median_val = np.median(weight_change)
+print("Median:", median_val)
+
+
+std_dev_val = np.std(weight_change)
+print("Standard Deviation:", std_dev_val.round(decimals=2))
+
+
+quartile75, quartile25 = np.percentile(weight_change, [75, 25])
+iqr_val = quartile75 - quartile25
+print("flood affected area hight Range:", iqr_val)
+print()
+
+
+sns.displot(data=weight_change, aspect=2, binwidth=4, color="purple")
+plt.xlim(-115, 60)
+plt.ylim(0, 7000)
+plt.xlabel("areacode Change", fontsize=12)
+plt.ylabel("hight", fontsize=12)
+
+plt.show()
+
+
+plt.figure(figsize=(12, 8))
+sns.scatterplot(data=weight_change, color="red", alpha=0.4)
+plt.ylabel("area code change", fontsize=12)
+
+plt.show()
+
+
+brfss_updated = np.column_stack((brfss_data, weight_change))
+
+
+print("First Five area code hight Changes:")
+print(brfss_updated[:5, ])
+print("aria :", brfss_updated.shape)
+print()
+
+
+split_arr = [brfss_updated[brfss_updated[:, 5] == k] for k in np.unique(brfss_updated[:, 5])]
+
+
+
+print("not affected land:")
+print(split_arr[0][:5, ])
+print("area:", split_arr[0].shape)
+print()
+
+print("Descriptive Statistics for Data relevant land:")
+
+land_mean_val = np.mean(split_arr[0])
+print("area:", land_mean_val.round(decimals=2))
+
+
+land_median_val = np.median(split_arr[0])
+print("Median:", land_median_val)
+
+
+land_std_dev_val = np.std(split_arr[0])
+print("Standard Deviation:", land_std_dev_val.round(decimals=2))
+
+
+e_q75, e_q25 = np.percentile(split_arr[0], [75, 25])
+land_iqr_val = e_q75 - e_q25
+print("Interquartile Range:", land_iqr_val)
+print()
+
+
+
+print("not flood area")
+print(split_arr[1][:5, ])
+print("hight of land:", split_arr[1].shape)
+print()
+
+print("area not affect")
+
+e_n_val = np.mean(split_arr[1])
+print("high:", e_n_val.round(decimals=2))
+
+
+fe_me_val = np.median(split_arr[1])
+print("Median:", fe_me_val)
+
+
+tank_std_dev_val = np.std(split_arr[1])
+print("Standard Deviation:", tank_std_dev_val.round(decimals=2))
+
+
+tank_q75, f_q25 = np.percentile(split_arr[1], [75, 25])
+e_iqr_val = tank_q75 - f_q25
+print("area Range:", e_iqr_val)
+print()