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Pandas keep the most complete rows


Pandas keep the most complete rows

By : Liuyuqing
Date : November 22 2020, 03:01 PM
should help you out You could use a surrogate column to sort based on counts and filter with a groupby.
code :
df = df.assign(count=df.isnull().sum(1))\
       .sort_values(['id', 'count'])\
       .groupby('id', as_index=0).head(1)\
       .drop('count', 1)

print(df)
  id   q1    q2   q3
0  a  1.0   low  NaN
4  b  0.0   low  0.0
5  c  NaN  high  0.0
6  d  1.0  high  1.0
9  e  0.0   low  NaN


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Ensure rows are fully complete before new rows of data can be entered

Ensure rows are fully complete before new rows of data can be entered


By : jLEzrr27
Date : March 29 2020, 07:55 AM
it should still fix some issue I see no easy way to prevent data entry on a new row.
However, you could use the Worksheet_Change event to test for complete entry, and undo the new data entry if the previous rows is incomplete.
code :
Private Sub Worksheet_Change(ByVal Target As Range)
    Dim lo As ListObject
    Dim lr As ListRow

    ' Get reference to the first or only Table in the worksheet
    Set lo = Me.ListObjects(1)

    ' If the change was not in the Table, there is nothing to do
    If Application.Intersect(Target, lo.DataBodyRange) Is Nothing Then Exit Sub

    ' If there is only one row in the Table, there is nothing to do
    If lo.ListRows.Count <= 1 Then Exit Sub

    ' Count the number of entries in the second last row
    '  if its less than the number of columns, that row is incomplete
    If Application.CountA(lo.ListRows(lo.ListRows.Count - 1).Range) _
      <> lo.ListColumns.Count Then
        ' Delete the last (newly created) row
        lo.ListRows(lo.ListRows.Count).Delete
        lo.ListRows(lo.ListRows.Count).Range.SpecialCells(xlCellTypeBlanks).Select
        MsgBox "Please enter complete data before starting a new row!", _
          vbOKOnly + vbError, "Undoing last data entry"
    End If
End Sub
pandas apply function that returns multiple values to rows in pandas dataframe

pandas apply function that returns multiple values to rows in pandas dataframe


By : Sharat Deb
Date : March 29 2020, 07:55 AM
like below fixes the issue I have a dataframe with a timeindex and 3 columns containing the coordinates of a 3D vector: , Just return a list instead of tuple.
code :
In [81]: df
Out[81]: 
                            x         y         z
ts                                               
2014-05-15 10:38:00  0.120117  0.987305  0.116211
2014-05-15 10:39:00  0.117188  0.984375  0.122070
2014-05-15 10:40:00  0.119141  0.987305  0.119141
2014-05-15 10:41:00  0.116211  0.984375  0.120117
2014-05-15 10:42:00  0.119141  0.983398  0.118164

[5 rows x 3 columns]

In [82]: def myfunc(args):
   ....:        e=args[0] + 2*args[1]
   ....:        f=args[1]*args[2] +1
   ....:        g=args[2] + args[0] * args[1]
   ....:        return [e,f,g]
   ....: 

In [83]: df.apply(myfunc ,axis=1)
Out[83]: 
                            x         y         z
ts                                               
2014-05-15 10:38:00  2.094727  1.114736  0.234803
2014-05-15 10:39:00  2.085938  1.120163  0.237427
2014-05-15 10:40:00  2.093751  1.117629  0.236770
2014-05-15 10:41:00  2.084961  1.118240  0.234512
2014-05-15 10:42:00  2.085937  1.116202  0.235327
Pandas: How to apply a passed conditional operator to select rows in pandas?

Pandas: How to apply a passed conditional operator to select rows in pandas?


By : gerald hewes
Date : March 29 2020, 07:55 AM
I hope this helps . I think need use operator in (), what is same like operator.lt(column, value):
code :
# helper.py
def get_milage(operator, condition):
    return df[operator(df.Milage, condition)]
Add rows to pandas to complete sequence

Add rows to pandas to complete sequence


By : Bolun Liu
Date : March 29 2020, 07:55 AM
Hope this helps What's the best way of completing a pandas dataframe like so: , Using groupby then reindex
code :
df.groupby('group').\
   apply(lambda x : x.set_index('order').reindex(-np.arange(max(x['order'].abs()+1)))).\
      drop('group',1).reset_index()
Out[135]: 
  group  order  value
0     a      0    NaN
1     a     -1    NaN
2     a     -2    3.0
3     a     -3    0.0
4     b      0    NaN
5     b     -1    NaN
6     b     -2    NaN
7     b     -3    NaN
8     b     -4    2.0
How to re order rows, by moving multiple separated rows an X amount of rows below in python with either pandas or numpy

How to re order rows, by moving multiple separated rows an X amount of rows below in python with either pandas or numpy


By : Sridhar K
Date : September 26 2020, 02:00 AM
may help you . First create extra, dummy column, to mock your sorting key. In this case, as far as I understood you:
code :
ord=["One", "Two", "Three", "Base"]

df["sorting_key"]=df.groupby("A").cumcount().map(str)+":"+df["A"].apply(ord.index).map(str)
df.sort_values("sorting_key")
        A    B    C sorting_key
1     One  654  196         0:0
2     Two    2  156         0:1
3   Three  154  123         0:2
0    Base  572   55         0:3
5     One  251   78         1:0
6     Two    5   56         1:1
7   Three  321   59         1:2
4    Base   78   45         1:3
9     One    5   12         2:0
10    Two  531  231         2:1
11  Three   51  123         2:2
8    Base   48   45         2:3
df.sort_values("sorting_key").reset_index(drop=True).drop(columns="sorting_key")
        A    B    C
0     One  654  196
1     Two    2  156
2   Three  154  123
3    Base  572   55
4     One  251   78
5     Two    5   56
6   Three  321   59
7    Base   78   45
8     One    5   12
9     Two  531  231
10  Three   51  123
11   Base   48   45
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