Differences

This shows you the differences between two versions of the page.

--- pandas_groupby [2021/07/06 23:09] – [preserve the highest odd value in each group] admin
+++ pandas_groupby [2024/03/26 22:25] (current) – [groupby slicing] raju
@@ Line 78: / Line 78: @@
 ==== preserve the highest odd value in each group ====
 tags | pandas groupby transform maximum odd number, maxodd
 Given
 <code>
@@ Line 236: / Line 237: @@
 ==== extract groupby object by key ====
+tags | pandas groupby filter a group
   * groups.get_group(key_value) if grouping on a single column
   * groups.get_group(key_value_tuple) if grouping on multiple columns.
@@ Line 316: / Line 319: @@
   bar  0  6
 </code>
+==== 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 ====
@@ Line 362: / Line 427: @@
 tags | reset_index remove level_1 column, apply function to multiple columns and rename result, groupby apply name the result, groupby apply remove level_1