Movie lens

DataFrame_Groupby.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-__author__ = 'Who ?'
+__author__ = 'Kevin'
 
 import pandas as pd
 import numpy as np
@@ -59,7 +59,7 @@ def cloneDF(df):
 
 
 # Sort data ratings by created field (groupby + lambda function + sorted)
-sortRatingsField = cloneDF(mergeRatings)
-sortRatingsField = sortRatingsField.groupby(['movie_id', 'title'])['rating'].agg(
-    {'COUNT': np.size, 'myAVG': lambda x: x.sum() / float(x.count())}).sort('COUNT', ascending=False)
+Hamtaro = cloneDF(mergeRatings)
+Hamtaro = sortRatingsField.groupby(['movie_id', 'title'])['rating'].agg(
+    {'COUNT': np.size, 'myAVG': lambda x: x.sum() / float(x.count())}).sort_values('COUNT', ascending=False)
 print('My info sorted: \n%s' % sortRatingsField[:15])

DataFrame_pivot_table.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-__author__ = 'Who ?'
+__author__ = 'Louis'
 
 import pandas as pd
 import numpy as np

Example_MovieLens.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-__author__ = 'Who ?'
+__author__ = 'Luciana'
 
 import pandas as pd
 
@@ -46,3 +46,4 @@
 # Example 4: show specific columns
 ratings_info = merge_ratings[['user_id', 'title', 'rating']]
 print('Show specific columns: \n%s' % ratings_info[:10])
+

Series_vs_DataFrame.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-__author__ = 'Who ?'
+__author__ = 'Mathieu'
 
 import pandas as pd