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R: how to join the duplicate rows in one dataframe


R: how to join the duplicate rows in one dataframe

By : Nyinechan Aung Love
Date : November 22 2020, 02:42 PM
will help you I have one dataframe with some duplicated rows, which I want to join only duplicated rows. Given an example below: , Here's a dplyr approach:
code :
library(dplyr)
df %>% group_by(name) %>% summarise_each(funs(first(.[!is.na(.)])))
#Source: local data frame [3 x 4]
#
#    name     b     c     d
#  (fctr) (int) (int) (int)
#1     IG    NA     3    NA
#2     OG     4     1     0
#3     yp     3     1    NA


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Converting groupby dataframe (with dropped duplicate rows by group) into normal dataframe

Converting groupby dataframe (with dropped duplicate rows by group) into normal dataframe


By : user2070220
Date : March 29 2020, 07:55 AM
like below fixes the issue Instead of creating a groupby to drop duplicates, have you considered:
code :
df4 = df3.drop_duplicates(['Organization', 'Name'])
Pandas: Update Multiple Dataframe Columns Using Duplicate Rows From Another Dataframe

Pandas: Update Multiple Dataframe Columns Using Duplicate Rows From Another Dataframe


By : El ouardi Mohamed
Date : March 29 2020, 07:55 AM
around this issue I think that combine_first will be an elegant solution, as per JohnE, provided you set Display Name as an index. This brings me to another point. I think that your task is well-defined only if 'Display Name' corresponds to exactly one set of attributes within each table. Assuming that, you can drop duplicates, set index and use .update like so:
code :
df1 = df1.drop_duplicates()

df1 = df1.set_index('Display Name')
df2 = df2.set_index('Display Name')

df2_c = df2.copy()

df2.update(df1)
df1.update(df2_c)

del df2_c
Finding duplicate rows in a Pandas Dataframe then Adding a column in the Dataframe that states if the row is a duplicate

Finding duplicate rows in a Pandas Dataframe then Adding a column in the Dataframe that states if the row is a duplicate


By : justPlayin
Date : March 29 2020, 07:55 AM
around this issue You need Series.duplicated with parameter keep=False for all duplicates first, then cast boolean mask (Trues and Falses) to 1s and 0s by astype by int and if necessary then cast to str:
code :
df['C'] = df['A'].duplicated(keep=False).astype(int).astype(str)
print (df)
   A  B  C
1  1  x  1
2  2  y  0
3  1  x  1
4  3  z  0
df['C'] = df.duplicated(subset=['A','B'], keep=False).astype(int).astype(str)
print (df)
   A  B  C
1  1  x  1
2  2  y  0
3  1  x  1
4  3  z  0
df['C'] = np.where(df['A'].duplicated(keep=False), '1', '0')
print (df)
   A  B  C
1  1  x  1
2  2  y  0
3  1  x  1
4  3  z  0
What is the most efficient way to join a very large dataframe(1000300 rows) and a relatively smaller dataframe(6090 rows

What is the most efficient way to join a very large dataframe(1000300 rows) and a relatively smaller dataframe(6090 rows


By : user1313826
Date : March 29 2020, 07:55 AM
Hope that helps This is called Broadcast join.
If your DataFrame's size is below spark.sql.autoBroadcastJoinThreshold, Spark will automatically use this type of join. If not, wrap your DataFrame inside broadcast function:
code :
import org.apache.spark.sql.functions._
df1.join(broadcast(df2))
panda dataframe indexes get messed up and duplicate rows when adding one column to another dataframe

panda dataframe indexes get messed up and duplicate rows when adding one column to another dataframe


By : Matthew Jourard
Date : March 29 2020, 07:55 AM
hop of those help? If I understand correctly, just use the array of values from df_sku_list['SKU_list']:
code :
df_orders['SKU_list'] = df_sku_list['SKU_list'].values
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