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Combine the data based on common index with dates

By : Paul Connell
Date : October 18 2020, 08:10 AM
Any of those help You can merge using the aptly named merge method:
code :
>>> df1[['Date','Exact']].merge(df2.rename(columns={'Date (LT)':'Date'}))
                  Date  Exact  Raw Conc.
0  2017-10-01 05:00:00    0.0        9.0
1  2017-10-01 06:00:00    0.0        7.0

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How can I subset a data frame based on dates, when my dates column is not the index in Python?

By : user2565044
Date : March 29 2020, 07:55 AM
Hope this helps Using pandas, you can first create a datetime column and then create a quarter column using the date/time quarter attribute:
code :
from datetime import datetime
date_format = "%Y-%m-%d %H:%M:%S"
df['datetime'] = [datetime.strptime(dt, date_format) for dt in df['date']]
df['quarter'] = [dt.quarter for dt in df['datetime']]
dfQ1 = df[df.quarter == 1]
dfQ2 = df[df.quarter == 2]
dfQ3 = df[df.quarter == 3]
dfQ4 = df[df.quarter == 4]

Finding the common dates in a data set based on a categorical variable

By : Chanaria Fussell
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I have a data set that looks something like this: , Another one:
code :
df <- read.table(header=TRUE, text="id    eye    date
1      L   01-01-2000
             1      L   01-06-2000
             1      R   01-01-2000
             1      R   01-03-2000
             1      R   01-06-2000
             2      L   01-01-2000
             2      L   01-04-2000
             2      R   01-04-2000
             2      R   01-07-2000
             2      R   04-09-2001
             3      L   01-01-2000
             4      L   01-01-2000
             4      L   03-03-2001
             4      R   03-03-2001")


left_join(df %>%
             group_by(id,date) %>%
             summarize(n=n()) %>%
             filter(n==2) %>%
           by=c("id","date")) %>%

Using R to combine rows in a data frame based on a common variable

By : user2797180
Date : March 29 2020, 07:55 AM
hope this fix your issue You can get the first non-NA for each column within each group with coalesce. No real reason to prefer that over na.omit though unless you have >1 non-NA value.
code :

df %>% 
  group_by(Person) %>% 
  summarise_all(reduce, coalesce)

# # A tibble: 3 x 5
#   Person     Q1    Q2    Q3    Q4
#   <chr>   <dbl> <dbl> <dbl> <dbl>
# 1 Person1     1     3     2     5
# 2 Person2     2     2     4     5
# 3 Person3     1     4     1     5

Comparing start/end dates between different rows based on common data field

By : user2911088
Date : March 29 2020, 07:55 AM
I wish this helpful for you The way to process rows in groups in a DATA step is to sort by the key, then use BY key_field (event in your example) in the DATA step, retaining the data from the previous rows in a group, while identifying the group's first and last row using if first.key_field and if last.key_field).
That would be easy if you have a maximum of two subjects per event. If you have more you'd have to write duplicate code to retain the values from the first, second, etc rows, or retain the data into an array.
code :
proc sql;
    create table result as
    select i1.*
      from example_input i1,
           example_input i2
     where i1.event = i2.event
       and i1.subject ^= i2.subject
       and (
             (i1.start_date < i2.start_date and
              i1.end_date > i2.start_date
           or /* the other way around */
             (i2.start_date < i1.start_date and
              i2.end_date > i1.start_date
   ; /* untested - I don't have SAS handy */
| subject | event | start_date | end_date   |
| ------- | ----- | ---------- | ---------- |
| s1      | e1    | 2019-03-02 | 2019-06-01 |
| s2      | e1    | 2019-03-01 | 2019-05-01 |

Loop through MySQL data based on common dates

By : Abdullah Awaysheh
Date : March 29 2020, 07:55 AM
help you fix your problem Easy update your query like so:
mysql_query("SELECT * FROM Shows WHERE timestamp >= '".date('Y-m-d H:i:s').'") or die(mysql_error());
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