left-join

问题 Quick Question Is there a better (i.e. more efficient / more concise) way to do this? compare-object $a $b | ?{$_.SideIndicator -eq '<='} Detail Compare-Object gives paramenters -excludeDifferent and -includeEqual to allow you to amend which results you get. using both gives you an inner join using just -includeEqual gives you a full outer join using just -excludeDifferent is pointless; as by default equal items are excluded, so it will now exclude everything. There are no options for

问题 I want merge data from a filtered set into a timeline I created with the help of the timeline package. df1 looks like Date Label Freq 2011-03-12 1 18 2011-03-14 1 16 2011-03-18 1 5 time line produces a vector with dates from a specific starting date until a specified end date. What I want to achieve is a timeline with all days in a certain period. Then I want to merge df1 into timeline. Using left_join from dplyr I first get Error in UseMethod("left_join") : not applicable for 'left_join' for

问题 My problem is the following: Lets say I have an existing dataframe with the following columns: UID, foo, result. Result is already partially filled. A second model now predicts additional rows, generating a second dataframe containing a UID and a result column: (Code to reproduce at bottom) ## df_main ## UID foo result ## <dbl> <chr> <chr> ## 1 1 moo Cow ## 2 2 rum <NA> ## 3 3 oink <NA> ## 4 4 woof Dog ## 5 5 hiss <NA> ## new_prediction ## UID result ## <dbl> <chr> ## 1 3 Pig ## 2 5 Snake I

问题 My problem is the following: Lets say I have an existing dataframe with the following columns: UID, foo, result. Result is already partially filled. A second model now predicts additional rows, generating a second dataframe containing a UID and a result column: (Code to reproduce at bottom) ## df_main ## UID foo result ## <dbl> <chr> <chr> ## 1 1 moo Cow ## 2 2 rum <NA> ## 3 3 oink <NA> ## 4 4 woof Dog ## 5 5 hiss <NA> ## new_prediction ## UID result ## <dbl> <chr> ## 1 3 Pig ## 2 5 Snake I

问题 I have one table with some statistics per date, which I want listed out with MySQL. For some dates there will be no statistics, so the result should look something like this: 2013-03-01: 3 2013-03-02: 2 2013-03-03: 0 2013-03-04: 1 I figured out that filling in the gaps with 0 -zero- could be solved with a separate table with all possible dates and LEFT JOIN. So far so good. The statistics (impressions) is in the table 'campaigndata': id - int(11) date - date campaignid - int(11) impressions -

问题 There might be a *_join version for this I'm missing here, but I have two data frames, where The merging should happen in the first data frame, hence left_join I not only want to add columns, but also update existing columns in the first data frame, more specifically: replace NA's in the first data frame by values in the second data frame The second data frame contains more rows than the first one. Condition #1 and #2 make left_join fail. Condition #3 makes rows_update fail. So I need to do

问题 I have one table transaction and another table transaction_item . One transaction has multiple transaction_items. I want to left join transaction_item if transaction_item.amount >= 2 , else perform no join. select ti.* from transaction t LEFT JOIN transaction_item ti on ti.unique_id = t.unique_id AND ti.location_id = t.location_id AND ti.transaction_date = t.transaction_date AND ti.amount >= 2 where t.pos_transaction_id = 4220 and t.location_id = 1674 and t.transaction_date = '2020-05-08'; If

问题 All, so I am trying to run the query. The query consist of 7 tables and I want to all table get left joined based on A.conn_keyy and the others with clause 'ON' My confusion comes when I want to join CPLCUR based on A , not works. (CPLCUR.conn_keyy in ( a.conn_keyy = b.conn_keyy ) It appears error : both left and right aliases encountered in join 'conn_key' set hive.support.quoted.identifiers=none; select coalesce(a.conn_keyy, b.conn_keyy,CPLCUR.conn_keyy) as rrconn_keyy, b.rfbbn, b.LINES_ID

问题 I am doing a_df = a_df.merge(b_df, how='left', on=['col1', col2]) After this, a_df actually has more rows than before the operation. How is this possible? They both have millions of rows, so it's hard for me to narrow down the problem. Probably I am missing something about how left merge works. 回答1: Problem is with duplicates, so instead left join merge return all combination of dupplicates pairs of both DataFrame s, check sample below: a_df = pd.DataFrame({'A':list('abcdef'), 'B':[4,5,4,5,5

问题 I have to display the total count of the last row by filestatus. tbl_bankdata bank_id | b_orderno| b_bankname| lead_id 1 | 01-01 | 1 | 1 2 | 01-02 | 2 | 1 3 | 02-01 | 3 | 2 4 | 03-01 | 1 | 3 tbl_fileStatus f_id | f_bankid| f_filestatus 1 | 1 | 1 2 | 2 | 1 3 | 2 | 2 4 | 1 | 2 5 | 1 | 3 6 | 3 | 2 7 | 3 | 3 I have two tables tbl_bankdata and tbl_fileStatus . I am sending bank_id in the tbl_fileStatus as a f_bank_id . Now I have to show the last f_bankid count. For example, I have to fetch the