From: arokem <arokem@gmail.com>
Date: Fri, 8 May 2015 00:50:46 +0000 (-0700)
Subject: Added some challenges to the HP analysis.
X-Git-Url: https://projects.mako.cc/source/matplotlib-cdsw/commitdiff_plain/4869907119b3a0609fa6b58ea68f2e6545d3d49e?hp=7bd9bbbbb11d7585b76e63e354c0c779d5a8c547

Added some challenges to the HP analysis.
---

diff --git a/003-plot-timeseries.py b/003-plot-timeseries.py
index ec98776..f0fe255 100644
--- a/003-plot-timeseries.py
+++ b/003-plot-timeseries.py
@@ -23,11 +23,12 @@ ax.set_ylabel('Size of the edit')
 plt.show()
 
 
-# Challenge: plot the relationship between edit size. Use 
+# Challenge: Is edit size related to how long it's been since the last edit? 
+# => Plot the relationship between edit size and the time since the last edit:
 
-## Hint 1: 
+## Hint 1: the number of seconds between two edits is: 
 
-#delta_time1 = hp.columns['timestamp'][1] - hp.columns['timestamp'][0]
+#delta_time1 = (hp.columns['timestamp'][1] - hp.columns['timestamp'][0]).total_seconds()
 
 ## Hint 2: 
 
@@ -36,3 +37,9 @@ plt.show()
 
 # ax.plot([1,2,3], [2,4,8], '.')
 # ax.plot([1,2,3], [2,4,8], 'r.')
+
+# And see online documentation here: 
+# http://matplotlib.org/api/pyplot_summary.html
+# http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
+
+
diff --git a/004-plot-histogram.py b/004-plot-histogram.py
index 785a068..b938fea 100644
--- a/004-plot-histogram.py
+++ b/004-plot-histogram.py
@@ -18,5 +18,7 @@ ax.set_title('Edit size distribution')
 
 # Maybe don't really need that axis to be so long:
 # ax.set_xlim([0, 200000])
-
 plt.show()
+
+## Challenge : A 'mega-user' is a user with more than 1000 edits. 
+# Plot a bar chart with the maximal edit size for each one of the mega-users
diff --git a/load_hp_data.py b/load_hp_data.py
index 0fe78c5..55d497c 100644
--- a/load_hp_data.py
+++ b/load_hp_data.py
@@ -22,7 +22,7 @@ for row in reader:
     row['size'] = int(row['size'])
     rows.append(row)
 
-# Sort these things, so that they give you nice time-series
+# Sort these things, so that they give you nice ordered time-series
 sort_rows = sorted(rows, key=lambda row: row['timestamp'], reverse=False)
 
 rows = sort_rows