commit-live-students · Sandesh373 · Oct 9, 2018 · Dec 4, 2018 · Dec 4, 2018 · Dec 4, 2018
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_calculate_statistics/__pycache__/__init__.cpython-36.pyc b/q01_calculate_statistics/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_calculate_statistics/__pycache__/build.cpython-36.pyc b/q01_calculate_statistics/__pycache__/build.cpython-36.pyc
diff --git a/q01_calculate_statistics/build.py b/q01_calculate_statistics/build.py
@@ -1,11 +1,18 @@
+# %load q01_calculate_statistics/build.py
 # Default Imports
 import numpy as np
 import pandas as pd
 
 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
+def calculate_statistics():
+    Sales_price = pd.Series(data['SalePrice'])
+    return np.mean(sale_price),np.median(sale_price),np.int64(Sales_price.mode()[0])
+
+calculate_statistics()
+
 
diff --git a/q01_calculate_statistics/tests/__pycache__/__init__.cpython-36.pyc b/q01_calculate_statistics/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_calculate_statistics/tests/__pycache__/test_q01_plot.cpython-36.pyc b/q01_calculate_statistics/tests/__pycache__/test_q01_plot.cpython-36.pyc
diff --git a/q02_plot/__pycache__/__init__.cpython-36.pyc b/q02_plot/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_plot/__pycache__/build.cpython-36.pyc b/q02_plot/__pycache__/build.cpython-36.pyc
diff --git a/q02_plot/build.py b/q02_plot/build.py
@@ -1,12 +1,24 @@
+# %load q02_plot/build.py
 # Default Imports
 import pandas as pd
+import numpy as np
 import matplotlib.pyplot as plt
 from greyatomlib.descriptive_stats.q01_calculate_statistics.build import calculate_statistics
 
 plt.switch_backend('agg')
 dataframe = pd.read_csv('data/house_prices_multivariate.csv')
 sale_price = dataframe.loc[:, 'SalePrice']
 
+def plot():
+    mean=np.mean(sale_price)
+    median=np.median(sale_price)
+    mode=sale_price.mode()[0]
+    plt.axvline(x=mean)
+    plt.axvline(x=median)
+    plt.axvline(x=mode)
+    plt.show()
+
+
+plot()
 
-# Draw the plot for the mean, median and mode for the dataset
 
diff --git a/q02_plot/tests/__pycache__/__init__.cpython-36.pyc b/q02_plot/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_plot/tests/__pycache__/test_q02_plot.cpython-36.pyc b/q02_plot/tests/__pycache__/test_q02_plot.cpython-36.pyc
diff --git a/q03_pearson_correlation/__pycache__/__init__.cpython-36.pyc b/q03_pearson_correlation/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_pearson_correlation/__pycache__/build.cpython-36.pyc b/q03_pearson_correlation/__pycache__/build.cpython-36.pyc
diff --git a/q03_pearson_correlation/build.py b/q03_pearson_correlation/build.py
@@ -1,9 +1,23 @@
+# %load q03_pearson_correlation/build.py
 # Default Imports
 import pandas as pd
+import numpy as np
 
 dataframe_1 = pd.read_csv('data/house_prices_multivariate.csv')
 dataframe_2 = pd.read_csv('data/house_prices_copy.csv')
 
+Sales_Price_1 = dataframe_1.SalePrice
+Sales_Price_2 = dataframe_2.SalePrice
+
+
 
 # Return the correlation value between the SalePrice column for the two loaded datasets
 # Your code here
+def correlation():
+    return np.corrcoef(Sales_Price_1, Sales_Price_2)[0,1]
+
+
+
+correlation()
+
+
diff --git a/q03_pearson_correlation/tests/__pycache__/__init__.cpython-36.pyc b/q03_pearson_correlation/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_pearson_correlation/tests/__pycache__/test_q03_correlation.cpython-36.pyc b/q03_pearson_correlation/tests/__pycache__/test_q03_correlation.cpython-36.pyc
diff --git a/q04_spearman_correlation/__pycache__/__init__.cpython-36.pyc b/q04_spearman_correlation/__pycache__/__init__.cpython-36.pyc
diff --git a/q04_spearman_correlation/__pycache__/build.cpython-36.pyc b/q04_spearman_correlation/__pycache__/build.cpython-36.pyc
diff --git a/q04_spearman_correlation/build.py b/q04_spearman_correlation/build.py
@@ -1,8 +1,21 @@
+# %load q04_spearman_correlation/build.py
 # Default Import
 import pandas as pd
+from scipy.stats import spearmanr
 
 dataframe_1 = pd.read_csv('data/house_prices_multivariate.csv')
 dataframe_2 = pd.read_csv('data/house_prices_copy.csv')
 
-# Your code here
+SalesPrice_1=dataframe_1.SalePrice
+SalesPrice_2=dataframe_2.SalePrice
+
+
+def spearman_correlation():
+    coef, p = spearmanr(SalesPrice_1, SalesPrice_2)
+    return coef
+
+
+
+spearman_correlation()
+
 
diff --git a/q04_spearman_correlation/tests/__pycache__/__init__.cpython-36.pyc b/q04_spearman_correlation/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q04_spearman_correlation/tests/__pycache__/test_q04_spearman_correlation.cpython-36.pyc b/q04_spearman_correlation/tests/__pycache__/test_q04_spearman_correlation.cpython-36.pyc