Differences

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--- matches_to_coplays [2023/02/24 16:51] – [problem description] admin
+++ matches_to_coplays [2023/02/24 17:03] (current) – [Solution] admin
@@ Line 104: / Line 104: @@
 Groupby on player_id1, player_id2 and get the size of each group
 <code>
-In [4]:
+In [9]:
-df.merge(df, on='match_id', how='inner', suffixes=('1', '2')).groupby(['player_id1', 'player_id2']).size()
+df.merge(df, on='match_id', how='inner', suffixes=('1', '2'))\
-Out[4]:
+.groupby(['player_id1', 'player_id2'])\
+.size()
+Out[9]:
 player_id1  player_id2
 a           a             2
@@ Line 117: / Line 119: @@
             b             1
             c             2
+dtype: int64
 </code>
 We want player_id1 and player_id2 as columns instead of as index.
 <code>
-In [5]:
+In [11]:
-df.merge(df, on='match_id', how='inner', suffixes=('1', '2')).groupby(['player_id1', 'player_id2'], as_index=False).si ze()
+df.merge(df, on='match_id', how='inner', suffixes=('1', '2'))\
-Out[5]:
+.groupby(['player_id1', 'player_id2'], as_index=False)\
+.size()
+Out[11]:
   player_id1 player_id2  size
           a          a     2
@@ Line 136: / Line 141: @@
 </code>
+See also:
+  * https://stackoverflow.com/questions/75537816/transform-a-dataframe-for-network-analysis-using-pandas
+    * It is worth going through this page in its entirety.
+    * I got the answer from here. I just added some intermediate steps to understand what is going on behind the scenes.
+    * It shows some alternative solutions which are worth exploring.
+    * It shows how to get the adjacency matrix
+    * It also shows how to visualize the result with some cool graphs produced by the networkx package.