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--- pandas_groupby [2024/03/26 22:17] – [extract groupby object by key] raju
+++ pandas_groupby [2024/05/07 20:46] – [extract groupby object by key] raju
@@ Line 235: / Line 235: @@
 Ref: https://stackoverflow.com/questions/49859182/understanding-level-0-and-group-keys
+==== filter elements from groups that dont satisfy a criterion ====
+tags | pandas groupby filter groups
+<code>
+In [2]:
+import pandas as pd
+df = pd.DataFrame({
+    'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'],
+    'B' : [1, 2, 3, 4, 5, 6],
+    'C' : [2.0, 5., 8., 1., 2., 9.]})
+df
+Out[2]:
+     A  B    C
+  foo  1  2.0
+  bar  2  5.0
+  foo  3  8.0
+  bar  4  1.0
+  foo  5  2.0
+  bar  6  9.0
+In [3]:
+grouped = df.groupby('A')
+In [4]:
+grouped.filter(lambda x: x['B'].mean() > 3.)
+Out[4]:
+     A  B    C
+  bar  2  5.0
+  bar  4  1.0
+  bar  6  9.0
+</code>
 ==== extract groupby object by key ====
@@ Line 321: / Line 352: @@
 ==== groupby slicing ====
+Consider
+<code>
+In [1]:
+import pandas as pd
+import numpy as np
+rand = np.random.RandomState(1)
+df = pd.DataFrame({'A': ['foo', 'bar'] * 3,
+                   'B': rand.randn(6),
+                   'C': rand.randint(0, 20, 6)})
+In [2]:
+df
+Out[2]:
+     A         B   C
+  foo  1.624345   5
+  bar -0.611756  18
+  foo -0.528172  11
+  bar -1.072969  10
+  foo  0.865408  14
+  bar -2.301539  18
+</code>
+Group by on column 'A'
+<code>
+In [3]:
+gb = df.groupby(['A'])
+</code>
+You can use get_group() to get a single group
+<code>
+In [4]:
+gb.get_group('foo')
+Out[4]:
+     A         B   C
+  foo  1.624345   5
+  foo -0.528172  11
+  foo  0.865408  14
+</code>
+You can select different columns using the groupby slicing:
+<code>
+In [5]:
+gb[['A', 'B']].get_group('foo')
+Out[5]:
+     A         B
+  foo  1.624345
+  foo -0.528172
+  foo  0.865408
+In [6]:
+gb[['C']].get_group('foo')
+Out[6]:
+    C
+   5
+  11
+  14
+</code>
+Ref:
+  * https://stackoverflow.com/questions/14734533/how-to-access-subdataframes-of-pandas-groupby-by-key
 ==== apply a function on each group ====