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Pandas comparing specific rows


By : Seth Straayer
Date : July 31 2020, 04:00 AM
I hope this helps you . As I understand, you want to assign a value to every unique combination of two columns in your DataFrame.
You can use this approach to create a dict with codes, or generate it with itertools, if there aren't all combinations in the dataframe.
code :
combs = set(zip(df['winner'], df['newcol2']))
codes = dict(zip(combs, range(len(combs))))
df['result'] = df.apply(lambda x: codes[x['winner'], x['newcol2']], axis=1)


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Comparing Pandas Dataframe Rows & Dropping rows with overlapping dates


By : pazer
Date : March 29 2020, 07:55 AM
To fix this issue You should use some kind of boolean mask to do this kind of operation.
One way is to create a dummy column for the next trade:
code :
df['EntryNextTrade'] = df['EntryDate'].shift()
msk = df['EntryNextTrade'] > df'[ExitDate']
df.loc[msk, ['EntryDate', 'ExitDate']]

Comparing consecutive rows and select rows where are subsequent is a specific value


By : pdtluong
Date : March 29 2020, 07:55 AM
I wish this helpful for you We can use data.table. Convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'HospNum_Id', we get the index ('i1') where 'EVENT' is "EMR" and the previous value is "nothing". Use that index to get the previous element index ('i1-1') sort and get the row index (.I). With that, we subset the rows.
code :
library(data.table)
v1 <- setDT(df1)[,  {i1 <- which(EVENT == "EMR" & shift(EVENT)=="nothing")
              .I[sort(c(i1, i1-1))] } , by = HospNum_Id]$V1
df1[v1]
#   HospNum_Id VisitDate   EVENT
#1:          2  13/12/06 nothing
#2:          2  13/04/12     EMR
#3:          2  13/05/13 nothing
#4:          2  13/06/14     EMR
library(dplyr)
df1 %>%
    group_by(HospNum_Id) %>% 
    mutate(ind = EVENT=="nothing" & lead(EVENT)=="EMR") %>% 
    slice(sort(c(which(ind),which(ind)+1))) %>% 
    select(-ind)
#   HospNum_Id VisitDate   EVENT   
#      <int>     <chr>   <chr>
#1          2  13/12/06 nothing
#2          2  13/04/12     EMR
#3          2  13/05/13 nothing
#4          2  13/06/14     EMR

remove specific rows based on comparing 2 rows if not a match in python


By : Aditya Saky
Date : March 29 2020, 07:55 AM
will be helpful for those in need I have 2 dataframes with 2 columns each, 1 column of both dataframes has the same values but not some of them does not match , Try this:
code :
df_1[df_1['ID_2'].isin(df_2.ID_1)]

pandas - are specific rows of dataframeA subset of specific rows of dataframeB?


By : Shashank Shree Neupa
Date : March 29 2020, 07:55 AM
Does that help here is one way to get something similar to your expected output, at least on the data you provide. First create the column 'invalidProps' in dfB
code :
dfB.loc[dfB['propValid'] == 'no','invalidProps'] = dfB.loc[dfB['propValid'] == 'no','propId']
dfB['invalidProps'] = dfB['invalidProps'].fillna('')
dfA_g = (dfA.sort_values(['property', 'value'])
              .groupby(['entityId','entityName'],as_index=False).agg(tuple))
dfB_g = (dfB.sort_values(['property', 'value'])
              .groupby(['entityId','entityValid'],as_index=False)
               .agg({'property':lambda x: tuple(x), 
                     'value':lambda x: tuple(x), 
                     'invalidProps':lambda x: tuple(filter(None,x))}))
resultDf  = (dfA_g.merge(dfB_g, how='left', on=['property', 'value'],suffixes=('','_'))
                  .fillna('---').drop(['property', 'value'],1)
                  .rename(columns={'entityId_':'dfBEntityIdMatch', 'entityValid':'valid'}))
   entityId entityName dfBEntityIdMatch     valid invalidProps
0         1        bob              123       yes           ()
1         2       dave              124        no       (4.0,)
2         3        bob              125  not sure           ()
3         4       alex              ---       ---          ---

Comparing rows of pandas dataframe (rows have some overlapping values)


By : hazzas
Date : March 29 2020, 07:55 AM
hope this fix your issue Here's a quick solution to return only the columns in which the first two rows differ.
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