Differences

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

--- get_the_first_non_null_value_in_each_column [2021/09/15 21:16] – raju
+++ get_the_first_non_null_value_in_each_column [2021/09/15 22:03] (current) – [Use case] raju
@@ Line 1: / Line 1: @@
 ===== Get the first non null value in each column =====
 ==== Task ====
-Get the first non null value in each column
+Get the first non null value in each column.
 Corner cases:
@@ Line 11: / Line 11: @@
   1.0    NaN   NaN
   NaN    2.0   NaN
+  3.0    NaN   NaN
+  NaN    4.0   NaN
 </code>
@@ Line 17: / Line 19: @@
    jim  joe  jolie  jack
   1.0    2.0   NaN
+  3.0    4.0   NaN
 </code>
@@ Line 27: / Line 30: @@
 import pandas as pd
 import numpy as np
-df = pd.DataFrame({'jim': [0, 0], 'joe': [1, np.nan],
+df = pd.DataFrame({'jim': [0, 0, 1, 1],
-                   'jolie': [np.nan, 2], 'jack': [np.nan, np.nan]})
+                   'joe': [1, np.nan, 3, np.nan],
+                   'jolie': [np.nan, 2, np.nan, 4],
+                   'jack': [np.nan, np.nan, np.nan, np.nan]})
 df
 Out[1]:
@@ Line 34: / Line 39: @@
     0  1.0    NaN   NaN
     0  NaN    2.0   NaN
+    1  3.0    NaN   NaN
+    1  NaN    4.0   NaN
 In [2]:
@@ Line 40: / Line 47: @@
     value = values.iloc[0] if not values.empty else np.nan
     return value
 In [3]:
@@ Line 47: / Line 55: @@
 jim
     1.0    2.0   NaN
+    3.0    4.0   NaN
 In [4]:
@@ Line 53: / Line 62: @@
    jim  joe  jolie  jack
     0  1.0    2.0   NaN
+    1  3.0    4.0   NaN
 </code>
 ==== meta ====
-Used Python 3.9.4 and IPython 7.22.0
+Used | Python 3.9.4 and IPython 7.22.0
+demonstrates | apply a function on each column of a dataframe after doing a groupby
+===== Get the first value not equal to a number =====
+==== Use case ====
+One downside of using np.nan to denote a missing value is that an integer column of a
+dataframe gets "promoted" to a floating point column even if there is a single np.nan in it.
+One work around is to use a specific integer to denote a missing value. For example, if we expect
+all integers to be positive, we can use -9999 to denote a missing value. Let's call this
+special integer NAN_INT.
+==== Task ====
+Get the first value not equal to NAN_INT
+Corner cases:
+  * If a column is all NAN_INTs, return a NAN_INT
+For example, give
+<code>
+   jim   joe  jolie  jack
+     1  -9999 -9999
+-9999      2 -9999
+     3  -9999 -9999
+-9999      4 -9999
+</code>
+We want
+<code>
+   jim  joe  jolie  jack
+    1      2 -9999
+    3      4 -9999
+</code>
+==== Solution ====
+<code>
+$ ipython
+In [1]:
+import pandas as pd
+import numpy as np
+NAN_INT = -9999
+df = pd.DataFrame({'jim': [0, 0, 1, 1],
+                   'joe': [1, NAN_INT, 3, NAN_INT],
+                   'jolie': [NAN_INT, 2, NAN_INT, 4],
+                   'jack': [NAN_INT, NAN_INT, NAN_INT, NAN_INT]})
+df
+Out[1]:
+   jim   joe  jolie  jack
+    0     1  -9999 -9999
+    0 -9999      2 -9999
+    1     3  -9999 -9999
+    1 -9999      4 -9999
+In [2]:
+df.dtypes
+Out[2]:
+jim      int64
+joe      int64
+jolie    int64
+jack     int64
+dtype: object
+In [3]:
+def get_first_non_missing(s):
+    NAN_INT = -9999
+    values = s.loc[(s != NAN_INT)]
+    value = values.iloc[0] if not values.empty else NAN_INT
+    return value
+In [4]:
+df.groupby('jim').agg(get_first_non_missing)
+Out[4]:
+     joe  jolie  jack
+jim
+      1      2 -9999
+      3      4 -9999
+In [5]:
+df.groupby('jim').agg(get_first_non_missing).reset_index()
+Out[5]:
+   jim  joe  jolie  jack
+    0    1      2 -9999
+    1    3      4 -9999
+</code>
+==== meta ====
+Used | Python 3.9.4, IPython 7.22.0
-demonstrates | apply a function on each column of a dataframe