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Compute number of occurance of each value and Sum another column in Pandas


Compute number of occurance of each value and Sum another column in Pandas

By : slancien
Date : November 28 2020, 12:01 PM
it fixes the issue I have a pandas dataframe with some columns in it. The column I am interested in is something like this, , Use pandas.Series.groupby with cumcount and cumsum.
code :
g = df[::-1].groupby('col')
df['col_count'] = g.cumcount().add(1)
df['X_sum'] = g['X'].cumsum()
print(df)
  col   X  col_count  X_sum
0   A  10          3     70
1   A  40          2     60
2   B  10          2     40
3   C  50          1     50
4   B  30          1     30
5   A  20          1     20


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drop all rows after first occurance of NaN in specific column (pandas)

drop all rows after first occurance of NaN in specific column (pandas)


By : Muhammad Jamshed Mah
Date : March 29 2020, 07:55 AM
wish of those help I am trying to use the dropna function in pandas. I would like to use it for a specific column. , use iloc + argmax
code :
data.iloc[:data.A.isnull().values.argmax()]

     A  B  C
0  1.0  1  0
1  2.0  2  1
2  3.0  3  2
3  4.0  4  3
4  5.0  5  4
5  6.0  6  5
6  7.0  7  6
top_data = data[:data['A'].isnull().argmax()]
How do I update the values in a pandas dataframe column until first occurance of a value in the same column?

How do I update the values in a pandas dataframe column until first occurance of a value in the same column?


By : Vamshi
Date : March 29 2020, 07:55 AM
Hope this helps Use idxmax for index value of first -1 and then set 0:
code :
idx = df['Regime'].eq(-1).idxmax()
df.iloc[:df.index.get_loc(idx), df.columns.get_loc('Regime')] = 0
print (df)
            50d-200d  Regime
Date                        
2017-02-22       NaN       0
2017-02-23       NaN       0
2017-02-24       NaN       0
2017-02-27      0.52       0
2017-02-28      0.92       0
2017-04-04      0.39       0
2017-04-05      0.16       0
2017-04-06     -0.08      -1
2017-04-07     -0.30      -1
2017-04-10     -0.51      -1
2017-08-09     -1.15      -1
2017-08-10     -0.52      -1
2017-08-11      0.07       1
2017-08-17      2.67       1
df.iloc[:df.Regime.eq(-1).values.argmax(), df.columns.get_loc('Regime')] = 0
pandas dataframe group by next occurance of column value

pandas dataframe group by next occurance of column value


By : user1773778
Date : March 29 2020, 07:55 AM
wish helps you Use a dictionary for a variable number of related variables. Here you can combine with GroupBy + cumsum:
code :
d = dict(tuple(df.groupby(df['msg'].eq('phone').cumsum())))
{1:  info        date       time      file           msg
 0  INFO:  2018-09-12  16:10:10:  view.py:         phone
 1  INFO:  2018-09-12  16:10:10:  view.py:        asdasd
 2  INFO:  2018-09-12  16:10:43:  view.py:  contactstart
 3  INFO:  2018-09-12  16:10:43:  view.py:    contactend
 4  INFO:  2018-09-12  16:11:36:  view.py:      appstart
 5  INFO:  2018-09-12  16:11:36:  view.py:     busystart
 6  INFO:  2018-09-12  16:12:08:  view.py:       busyend
 7  INFO:  2018-09-12  16:12:08:  view.py:    contactend
 8  INFO:  2018-09-12  16:12:08:  view.py:        append,

 2:  info        date       time      file         msg
 9  INFO:  2018-09-12  16:12:08:  view.py:       phone
 7  INFO:  2018-09-12  16:12:08:  view.py:  contactend}
Encoding a column in Pandas based on occurance of value 0

Encoding a column in Pandas based on occurance of value 0


By : user2958260
Date : March 29 2020, 07:55 AM
wish of those help I have a Pandas dataframe with a column like this, , IIUC, you want to count the occurrence of 0 but shifted:
code :
df['A'].eq(0).cumsum().shift(fill_value=0)+1
df['A'].shift().eq(0).cumsum()+1
0    1
1    1
2    1
3    2
4    2
5    2
Name: A, dtype: int32
Add index in pandas based on each occurance of another column specific value

Add index in pandas based on each occurance of another column specific value


By : KeyG
Date : March 29 2020, 07:55 AM
it helps some times You can use eq to match if category column equals parent and cumsum , sub is to subtract 1 since cumsum starts from 1 here:
code :
df['family_id'] = df['category'].eq('parent').cumsum().sub(1)
print(df)
  category   name  age  family_id
0   parent  harry   29          0
1    child  smith   12          0
2   parent  sally   41          1
3    child  david   19          1
4    child   mike   16          1
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