commit-live-students · preetiail · Oct 7, 2018 · Oct 7, 2018 · Oct 7, 2018 · Oct 7, 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 +1,22 @@
+# %load q01_calculate_statistics/build.py
 # Default Imports
 import numpy as np
 import pandas as pd
+import statistics
 
 data = pd.read_csv('data/house_prices_multivariate.csv')
-sale_price = data.loc[:, "SalePrice"]
-
+sale_price = data.loc[:, 'SalePrice']
+sale_price
+def calculate_statistics():
+    mean1=np.mean(sale_price)
+    median1=np.median(sale_price)
+    series1=np.array(sale_price)
+    mode1=statistics.mode(series1)
+    return(mean1,median1,np.int64(mode1))
 
+calculate_statistics()
 # Return mean,median & mode for the SalePrice Column
 # Write your code here
 
+
+
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 +1,27 @@
+# %load q02_plot/build.py
 # Default Imports
 import pandas as pd
 import matplotlib.pyplot as plt
 from greyatomlib.descriptive_stats.q01_calculate_statistics.build import calculate_statistics
+import statistics
+import numpy as np
 
 plt.switch_backend('agg')
 dataframe = pd.read_csv('data/house_prices_multivariate.csv')
 sale_price = dataframe.loc[:, 'SalePrice']
 
+def plot():
+ #plt.hist(sale_price)
+    plt.hist(sale_price)
+    plt.axvline(sale_price.mean(),color='c')
+    plt.axvline(sale_price.median(),color='k')
+    series1=np.array(sale_price)
+    mode1=statistics.mode(series1)
+    plt.axvline(mode1,color='k')
+    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 +1,14 @@
+# %load q03_pearson_correlation/build.py
 # Default Imports
 import pandas as pd
+from scipy.stats.stats import pearsonr
 
 dataframe_1 = pd.read_csv('data/house_prices_multivariate.csv')
 dataframe_2 = pd.read_csv('data/house_prices_copy.csv')
 
 
-# Return the correlation value between the SalePrice column for the two loaded datasets
-# Your code here
+def correlation():
+    result=pearsonr(dataframe_1['SalePrice'],dataframe_2['SalePrice'])
+    return(result[0])
+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 +1,15 @@
+# %load q04_spearman_correlation/build.py
 # Default Import
 import pandas as pd
 
 dataframe_1 = pd.read_csv('data/house_prices_multivariate.csv')
 dataframe_2 = pd.read_csv('data/house_prices_copy.csv')
 
+from scipy.stats import spearmanr
 # Your code here
 
+def spearman_correlation():
+    result=spearmanr(dataframe_1['SalePrice'],dataframe_2['SalePrice'])
+    return(result[0])
+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