Add a New Column to a Dataframe Using Matching Values of Another Dataframe

Add a new column to a dataframe using matching values of another dataframe

merge(table1, table2[, c("pid", "val2")], by="pid")

Add in the all.x=TRUE argument in order to keep all of the pids in table1 that don't have matches in table2...

You were on the right track. Here's a way using match...

table1$val2 <- table2$val2[match(table1$pid, table2$pid)]

Create new column in dataframe with match values from other dataframe

Need map by Series first for new column:

df1['Total2'] = df1['Name'].map(df2.set_index('Name')['Total2'])
print (df1)
         Name  Total  Total2
0  Accounting      3     120
1   Reporting      1     100
2     Finance      1     400
3       Audit      1     500
4    Template      2    1200

And then set_index with DataFrame.plot.bar:

df1.set_index('Name').plot.bar()

Append a new column on a dataframe based on other dataframe with matching rows and fill the non-matching ones with value from the existing column

Use numpy.where :

df1['new'] = np.where(df1['UserID'].isin(df2['UserID']), 'test-Red', 
             np.where(df1['group'] == 'test','test-NonRed',df1['group']))
print (df1)
   UserID    group       day  sp   PU          new
0     213     test  12/11/14   3  311     test-Red
1     314  control  13/11/14   4  345      control
2     354     test  13/08/14   5  376  test-NonRed

Or numpy.select:

m1 = df1['UserID'].isin(df2['UserID'])
m2 = df1['group'] == 'test'
df1['new'] = np.select([m1,m2], ['test-Red', 'test-NonRed'],default=df1['group'])
print (df1)
  UserID    group       day  sp   PU          new
0     213     test  12/11/14   3  311     test-Red
1     314  control  13/11/14   4  345      control
2     354     test  13/08/14   5  376  test-NonRed

New column based on matching values from another dataframe pandas

Check with stack df1's list columns after re-create with DataFrame then map the value from df2

Also since you asking not using for loop I am using sum , and sum for this case is much slower than *for loop* or itertools

s=pd.DataFrame(df1.column2.tolist()).stack()
df1['New']=s.map(df2.set_index('column3').column4).sum(level=0).apply(set)
df1
Out[36]: 
  column1    column2              New
0      a1     [A, B]     {2, 4, 3, 1}
1      a2  [A, B, C]  {3, 5, 4, 2, 1}
2      a3     [B, C]     {4, 3, 1, 5}

As I mentioned and most of us suggested , also you can check with For loops with pandas - When should I care?

import itertools
d=dict(zip(df2.column3,df2.column4))


l=[set(itertools.chain(*[d[y] for y in x ])) for x in df1.column2.tolist()]
df1['New']=l

How to add new column from another dataframe based on values in column of first dataframe?

This is done via a join operation which in pandas can be done with .merge().

Kindly try using the following:

df = df.merge(population,how='left',on='Province')

Also please consider reading the following answer for a detailed guide on joins and merges

Pandas populate new dataframe column based on matching columns in another dataframe

Consider the following dataframes df and df2

df = pd.DataFrame(dict(
        AUTHOR_NAME=list('AAABBCCCCDEEFGG'),
        title=      list('zyxwvutsrqponml')
    ))

df2 = pd.DataFrame(dict(
        AUTHOR_NAME=list('AABCCEGG'),
        title      =list('zwvtrpml'),
        CATEGORY   =list('11223344')
    ))

option 1

merge

df.merge(df2, how='left')

option 2

join

cols = ['AUTHOR_NAME', 'title']
df.join(df2.set_index(cols), on=cols)

both options yield

Sample Image

R: How do I add a column to a dataframe based on paired values in another dataframe with different length?

Using left_join from dplyr package:

library(dplyr)

data <- left_join(df1,df2, by="id")

Adding value to new column based on if two other columns match

I tried with this little dataset and it's working, let me know if you have some problems

df1 = pd.DataFrame({"title" :   ["Biocryst ($BCRX) continues to remain undervalued", "AAPL is good, buy it"], 'score' : [120,420]   , 'Stock_name' : ["",""]    })
df2 = pd.DataFrame({'Company name' : ['BioCryst Pharmaceuticals, Inc.','GameStop','Apple Inc.'], 'Symbol' : ["BCRX","GME","AAPL"]})

df1
                                               title    score   Stock_name
0   Biocryst ($BCRX) continues to remain undervalued    120 
1   AAPL is good, buy it                                420


df2
                     Company name   Symbol
0   BioCryst Pharmaceuticals, Inc.  BCRX
1   GameStop                        GME
2   Apple Inc.                      AAPL

for j in range(0,len(df2)):
  for i in range(0,len(df1)):
    if df2['Symbol'][j] in df1['title'][i]:
      df1['Stock_name'][i] = df2['Symbol'][j]


df1

                                               title    score   Stock_name
0   Biocryst ($BCRX) continues to remain undervalued    120     BCRX
1   AAPL is good, buy it                                420     AAPL

Add a New Column to a Dataframe Using Matching Values of Another Dataframe