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Many row corresponding to the same date, how to compute sum of the values of a column corresponding to such column


By : kamal
Date : October 17 2020, 06:10 AM
this will help I have the following DataFrame: , resample
code :
df.resample('D', on='Date').mean()

            Polarity  Subjectivity
Date                              
2018-05-23       0.4           0.6
2018-05-24       0.5           0.0


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Fill missing date values in column by adding delivery interval to another date column


By : Abhishek Mishra
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further You want to do date-arithmetic, and fill NAs in deliveryDate column by adding a date-interval of two days to orderDate column. lubridate supplies convenience functions for time intervals like days(), weeks(), months(), years(), hours(), minutes(), seconds() for exactly that purpose. And first, you have to parse your (European-format) datestrings into R date objects.
Something like the following, using lubridate for date-arithmetic and dplyr for dataframe manipulation:
code :
require(dplyr)

DB1$orderDate    = as.POSIXct(DB1$orderDate, format="%d.%m.%y", tz='UTC')
DB1$deliveryDate = as.POSIXct(DB1$deliveryDate, format="%d.%m.%y", tz='UTC')

DB1 %>% group_by(orderDate) %>%
        summarize(delivery_time = (deliveryDate - orderDate)) %>%
        ungroup() %>% summarize(median(delivery_time, na.rm=T))

# median(delivery_time, na.rm = T)
#                         1.5 days
# so you round up to 2 days
delivery_days = 2.0

require(lubridate)
DB1 <- DB1 %>% filter(is.na(deliveryDate)) %>%
                mutate(deliveryDate = orderDate + days(2))

# orderItemID  orderDate deliveryDate
#           3 2013-04-12   2013-04-14
#           6 2014-02-19   2014-02-21

How to compute a column depending on previous values of one and current values of another column


By : shaeen
Date : March 29 2020, 07:55 AM
I hope this helps you . I have not too much experience with pandas, and I have the following DataFrame:
code :
In [73]: (df.drop('B',1)
    ...:   .merge(df.drop('A',1)
    ...:            .assign(month=df.month + pd.offsets.MonthEnd(12)),
    ...:          on='month', how='left')
    ...:   .eval("C = A * B", inplace=False)
    ...:   .fillna(0)
    ...: )
    ...:
Out[73]:
        month         A          B         C
0  2017-02-28  0.737757   0.000000  0.000000
1  2017-03-31  0.759479   0.000000  0.000000
2  2017-04-30  0.750831   0.000000  0.000000
3  2017-05-31  0.703881   0.000000  0.000000
4  2017-06-30  0.692021   0.000000  0.000000
5  2017-07-31  0.680161   0.000000  0.000000
6  2017-08-31  0.668301   0.000000  0.000000
7  2017-09-30  0.707592   0.000000  0.000000
8  2017-10-31  0.698944   0.000000  0.000000
9  2017-11-30  0.625951   0.000000  0.000000
10 2017-12-31  0.611976   0.000000  0.000000
11 2018-01-31  0.633000   0.000000  0.000000
12 2018-02-28  0.598000   0.000000  0.000000
13 2018-03-31  0.673000   3.797394  2.555646
14 2018-04-30  0.673000   3.754154  2.526546
15 2018-05-31  0.609000   7.038814  4.286638
16 2018-06-30  0.609000  11.072340  6.743055
17 2018-07-31  0.609000  11.562738  7.041707
18 2018-08-31  0.609000  10.692814  6.511924
19 2018-09-30  0.673000  11.321464  7.619345
20 2018-10-31  0.673000   7.688380  5.174280
21 2018-11-30  0.598000   4.381660  2.620233
22 2018-12-31  0.598000   3.671854  2.195769
In [77]: df.drop('A',1).assign(month=df.month + pd.offsets.MonthEnd(12))
Out[77]:
        month          B
0  2018-02-28   0.000000
1  2018-03-31   3.797394
2  2018-04-30   3.754154
3  2018-05-31   7.038814
4  2018-06-30  11.072340
5  2018-07-31  11.562738
6  2018-08-31  10.692814
7  2018-09-30  11.321464
8  2018-10-31   7.688380
9  2018-11-30   4.381660
10 2018-12-31   3.671854
11 2019-01-31   3.798000
12 2019-02-28   4.784000
13 2019-03-31   5.384000
14 2019-04-30   1.346000
15 2019-05-31   0.000000
16 2019-06-30   0.000000
17 2019-07-31   0.000000
18 2019-08-31   0.000000
19 2019-09-30   0.000000
20 2019-10-31   0.000000
21 2019-11-30   0.000000
22 2019-12-31   0.000000
In [79]: (df.drop('B',1)
    ...:    .merge(df.drop('A',1)
    ...:             .assign(month=df.month + pd.offsets.MonthEnd(12)),
    ...:           on='month', how='left'))
Out[79]:
        month         A          B
0  2017-02-28  0.737757        NaN
1  2017-03-31  0.759479        NaN
2  2017-04-30  0.750831        NaN
3  2017-05-31  0.703881        NaN
4  2017-06-30  0.692021        NaN
5  2017-07-31  0.680161        NaN
6  2017-08-31  0.668301        NaN
7  2017-09-30  0.707592        NaN
8  2017-10-31  0.698944        NaN
9  2017-11-30  0.625951        NaN
10 2017-12-31  0.611976        NaN
11 2018-01-31  0.633000        NaN
12 2018-02-28  0.598000   0.000000
13 2018-03-31  0.673000   3.797394
14 2018-04-30  0.673000   3.754154
15 2018-05-31  0.609000   7.038814
16 2018-06-30  0.609000  11.072340
17 2018-07-31  0.609000  11.562738
18 2018-08-31  0.609000  10.692814
19 2018-09-30  0.673000  11.321464
20 2018-10-31  0.673000   7.688380
21 2018-11-30  0.598000   4.381660
22 2018-12-31  0.598000   3.671854

How to create a calculated column in DAX, based on one column values and on a specific date of other column?


By : blake_tn
Date : March 29 2020, 07:55 AM
wish helps you For your expected output I tried successfully this SWITCH in DAX. Considering that Table1 have your columns.
Output = SWITCH([Shop];"ADD_TO_CART";Table1[Number];"PRODUCT_VIEW";CALCULATE(CALCULATE(SUM(Table1[Number]);FILTER(Table1;Table1[Shop]="ADD_TO_CART"));ALLEXCEPT(Table1;Table1[Date]))-CALCULATE(CALCULATE(SUM(Table1[Number]);FILTER(Table1;Table1[Shop]="PRODUCT_VIEW"));ALLEXCEPT(Table1;Table1[Date]));"CHECKOUT";CALCULATE(CALCULATE(SUM(Table1[Number]);FILTER(Table1;Table1[Shop]="PRODUCT_VIEW"));ALLEXCEPT(Table1;Table1[Date]))-CALCULATE(CALCULATE(SUM(Table1[Number]);FILTER(Table1;Table1[Shop]="CHECKOUT"));ALLEXCEPT(Table1;Table1[Date])))

MySQL update a column's values to equal the sum of another column's values based on date and another column's value


By : user1961602
Date : March 29 2020, 07:55 AM
I wish this helpful for you You need SUM() aggregation with Group By on team and game_date:
code :
SELECT 
  team, 
  game_date, 
  SUM(minutes) AS team_minutes_played 
FROM 
  your_table_name 
GROUP BY 
  team, 
  game_date 

how to use previous row value as well as values in other column in same row to compute value of a column in pandas


By : daniel
Date : March 29 2020, 07:55 AM
I wish this help you I have a dataframe df: , This should do it:
code :
def f(g):
    g.B = (g.B.shift() + g.C + g.D).cumsum()
    return g

df.B.replace(0, df.groupby('A').apply(f).B)
   A   B  C  D
0  1  10  1  2
1  1  14  1  3
2  1  20  2  4
3  2   5  2  5
4  2  10  3  2
5  2  16  3  3
6  2  23  3  4
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