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How can I do groupy.apply() without sort my index?


How can I do groupy.apply() without sort my index?

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How can I do groupy.apply() without sort my index?
Tag : python , By : damomurf
Date : November 26 2020, 03:01 PM

help you fix your problem I have the data like follow: , Use sort = False when using groupby for example
myData.groupby('Type',sort=False).mean()
            K     R      S
Type                         
C     823.333333  0.05  978.0
P     827.500000  0.05  978.0
B     839.000000  0.04  944.5
A     842.000000  0.04  925.0

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jQuery: how to go into sort mode, get out of it, apply order, and cancel sort?


Tag : jquery-ui , By : Sandip
Date : March 29 2020, 07:55 AM
This might help you If you have a button that you want to use to "Start Sorting" the sortable, I would recommend this approach, assuming you have a DIV with an ID of "MyList"...
On document load or Init, create the sortable and deactivate it...
$(init);
function init() {
    $("#MyLIst").sortable();
    $("#MyLIst").sortable("disable");
}
$("#MyLIst").sortable("enable");
$("#MyLIst").sortable("disable");

How to apply a secondary sort for ruby array with reversed sort direction?


Tag : ruby-on-rails , By : kakashi_
Date : March 29 2020, 07:55 AM
I hope this helps you . I have a ruby array in the format of [["Anna",70],["Billy",90],["Billy",100]]... , You can easily do it with
sort_by{ |o| [o.name, -o.score]}

Pandas: How to flag a column with groupy & apply()


Tag : python , By : Pavel K.
Date : March 29 2020, 07:55 AM
To fix the issue you can do Right now, my code will return the rows that meet the condition in my function. How can I instead return all original rows and flag a new column ('GreaterDate) if the condition is true? , Here is one way:
def date_compare(df):
    df['dftest'] = df['Date2'] == df['Date1'].shift(-1)
    return df

dftest = pd.concat([df[df.KEY == k].pipe(date_compare) \
                   for k in set(df.KEY)], ignore_index=True)

#       Date1     Date2        KEY  dftest
# 0  20120506  20120507  100000009    True
# 1  20120507  20120615  100000009   False
# 2  20120608  20120629  100000009   False
# 3  20120620  20120206  100000009   False
# 4  20120206  20120305  100000034   False
# 5  20120306  20120506  100000034    True
# 6  20120506  20120506  100000034   False
# 7  20120506  20120528  100000003   False

ValueError: cannot reindex from a duplicate axis using groupy and apply pct_change in Pandas


Tag : python , By : B3CFT
Date : March 29 2020, 07:55 AM
This might help you I get this same error if i have duplicates in my index. You'll need to reset_index():
In [726]: df.append(df)                                                                                                                                                                        
Out[726]: 
  customer brand product  quantity  price  new_quantity
0       C1    B1      P1       100      5           500
1       C1    B1      P2        10     20           200
2       C1    B2      P3        50      7           350
3       C2    B1      P1        75      5           375
4       C2    B2      P3         5      7            35
0       C1    B1      P1       100      5           500
1       C1    B1      P2        10     20           200
2       C1    B2      P3        50      7           350
3       C2    B1      P1        75      5           375
4       C2    B2      P3         5      7            35

df.groupby('customer')['quantity'].apply(lambda x: x.pct_change()) 

# ValueError: cannot reindex from a duplicate axis

In [730]: df.append(df).reset_index().groupby('customer')['quantity'].apply(lambda x: x.pct_change())                                                                                          
Out[730]: 
0          NaN
1    -0.900000
2     4.000000
3          NaN
4    -0.933333
5     1.000000
6    -0.900000
7     4.000000
8    14.000000
9    -0.933333
Name: quantity, dtype: float64

Ascending sort order Index versus descending sort order index when performing OrderBy


Tag : asp.net-mvc , By : joshboles
Date : March 29 2020, 07:55 AM
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