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diff --git a/q01_calculate_statistics/build.py b/q01_calculate_statistics/build.py
index a556241..c7f33fc 100644
--- a/q01_calculate_statistics/build.py
+++ b/q01_calculate_statistics/build.py
@@ -1,11 +1,33 @@
+# %load q01_calculate_statistics/build.py
 # Default Imports
 import numpy as np
 import pandas as pd
+from scipy import stats
+#import statistics 
+#stats.mode(expenditure)
 
 data = pd.read_csv('data/house_prices_multivariate.csv')
-sale_price = data.loc[:, "SalePrice"]
+sale_price = data.loc[:, 'SalePrice']
 
 
 # Return mean,median & mode for the SalePrice Column
 # Write your code here
 
+
+print(sale_price)
+mean = np.average(list(map(float, sale_price)))
+
+print(mean)
+median = np.median(list(map(float, sale_price)))
+
+def calculate_statistics():
+#    mode_series = pd.Series()
+    mean = np.average(list(map(float, sale_price)))
+    median = np.median(list(map(float, sale_price)))
+  # mode = pd.Series(list(stats.mode(sale_price)))
+    mode = pd.Series.mode(sale_price)
+    return mean , median ,mode[0]
+    
+calculate_statistics()
+
+
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diff --git a/q02_plot/build.py b/q02_plot/build.py
index 70276d6..222c208 100644
--- a/q02_plot/build.py
+++ b/q02_plot/build.py
@@ -1,3 +1,4 @@
+# %load q02_plot/build.py
 # Default Imports
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -10,3 +11,15 @@
 
 # Draw the plot for the mean, median and mode for the dataset
 
+list_1 = calculate_statistics()
+def plot():
+    list_1 = calculate_statistics()
+    plt.hist(sale_price)
+    plt.axvline(list_1[0])
+    plt.axvline(list_1[1])
+    plt.axvline(list_1[2])
+    plt.show()
+
+#calculate_statistics()
+plot()
+
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