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Groupby with handling empty bin in python


Groupby with handling empty bin in python

By : Dell Sargent
Date : November 22 2020, 03:01 PM
Any of those help Assume I have dataframe like below , There is dropna=False in stack
code :
df.groupby([df.sex,pd.cut(df.age,bin)]).size().unstack().stack(dropna=False).fillna(0)
Out[27]: 
sex  age     
1    (0, 5]      1.0
     (5, 10]     1.0
     (10, 15]    1.0
     (15, 20]    0.0
2    (0, 5]      0.0
     (5, 10]     0.0
     (10, 15]    0.0
     (15, 20]    2.0
dtype: float64


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Empty XML element handling in Python

Empty XML element handling in Python


By : Teo
Date : March 29 2020, 07:55 AM
should help you out I'm puzzled by minidom parser handling of empty element, as shown in following code section. , Cracking open xml.dom.minidom and searching for "/>", we find this:
code :
# Method of the Element(Node) class.
def writexml(self, writer, indent="", addindent="", newl=""):
    # [snip]
    if self.childNodes:
        writer.write(">%s"%(newl))
        for node in self.childNodes:
            node.writexml(writer,indent+addindent,addindent,newl)
        writer.write("%s</%s>%s" % (indent,self.tagName,newl))
    else:
        writer.write("/>%s"%(newl))
>>> doc = Document()
>>> v = doc.appendChild(doc.createElement('v'))
>>> v.toxml()
'<v/>'
>>> v.childNodes
[]
>>> v.appendChild(doc.createTextNode(''))
<DOM Text node "''">
>>> v.childNodes
[<DOM Text node "''">]
>>> v.toxml()
'<v></v>'
Pandas python Initializing groupby object with empty groups

Pandas python Initializing groupby object with empty groups


By : lycaon
Date : March 29 2020, 07:55 AM
this will help I have a large dataframe. I want to groupby three columns in the dataframe, and then apply a function to each group. However, I'm also interested in some groups and keys that are NOT in the dataframe. How do I add those to the groupby object, so I may use groupby.apply() uniformly on all groups? , I think I would probably reindex after the fact:
code :
In [11]: df = pd.DataFrame([[1, 2, 3], [2, 1, 6], [2, 2, 9]], columns=['A', 'B', 'C'])

In [12]: s = df.groupby(['A', 'B']).sum()

In [13]: s
Out[13]:
     C
A B
1 2  3
2 1  6
  2  9
In [14]: m = pd.MultiIndex.from_product([[1, 2], [1, 2]], names=['A', 'B'])

In [15]: s.reindex(m)
Out[15]:
      C
A B
1 1 NaN
  2   3
2 1   6
  2   9
python itertools groupby return empty

python itertools groupby return empty


By : John L.
Date : March 29 2020, 07:55 AM
With these it helps That's because Python iterator can only be iterated once.
For example:
code :
def f():
    for i in range(5):
        yield i

i = f() # i is an iterator

list(i) # [1, 2, 3, 4, 5]
# i has been iterated once, now it's empty
list(i) # []
l = list(i)
print(i2, l)
arr.append(l)
python pandas groupby unexpected empty column

python pandas groupby unexpected empty column


By : kukaye
Date : March 29 2020, 07:55 AM
this will help I want to aggregate some data to append to a dataframe. The following gives me the number of wins per name import pandas as pd , Try this
code :
WinCount=have.groupby(['Winner']).size().to_frame('WinCount').reset_index()
    Winner  WinCount
0   juli    2
1   nick    1
2   peter   2
3   tom     1
How to preserve rows with empty (nan) cells when doing groupby on a dataframe in python pandas

How to preserve rows with empty (nan) cells when doing groupby on a dataframe in python pandas


By : user3490553
Date : March 29 2020, 07:55 AM
seems to work fine I would suggest a slight adjustment to your suggestion. Instead of turning then NaN values into an empty string, you can replace them with Unknown. I'll give you a reproducible example:
code :
import pandas as pd
import numpy as np

df = pd.DataFrame({'Height':[20, 50, 20, 15, 10],
                   'Happy':['No', 'Yes', 'No', 'No', 'Yes'],
                   'Sex':['M', np.nan, 'M', 'M', 'F']})
print(df)
   Height Happy  Sex
0      20    No    M
1      50   Yes  NaN
2      20    No    M
3      15    No    M
4      10   Yes    F
df.fillna('Unknown').groupby(['Sex', 'Happy'])['Height'].sum()
Out[8]: 
Sex      Happy
F        Yes      10
M        No       55
Unknown  Yes      50
Name: Height, dtype: int64
B = A.fillna('Unknown').groupby(['Artist', 'Album', 'Title'])['Point'].sum()
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