Check If Value from One Dataframe Exists in Another Dataframe

Check if value from one dataframe exists in another dataframe

Use isin

Df1.name.isin(Df2.IDs).astype(int)

0    1
1    1
2    0
3    0
Name: name, dtype: int32

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Show result in data frame

Df1.assign(InDf2=Df1.name.isin(Df2.IDs).astype(int))

   name  InDf2
0  Marc      1
1  Jake      1
2   Sam      0
3  Brad      0

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In a Series object

pd.Series(Df1.name.isin(Df2.IDs).values.astype(int), Df1.name.values)

Marc    1
Jake    1
Sam     0
Brad    0
dtype: int32

Check if column pair in one dataframe exists in another?

I'm not sure why you don't like merge, but you can use isin with tuple:

df2['check'] = df2[['id','ref']].apply(tuple, axis=1)\
                  .isin(df1[['id','ref']].apply(tuple, axis=1))

Output:

  id     ref  check
0  a   apple   True
1  b  orange   True
2  d  banana  False

Check if value from one dataframe exists in another dataframe in R

Using the same data and outcome as the original Python example

Df1 <- data.frame(name =  c('Marc', 'Jake', 'Sam', 'Brad'))
Df2 <- data.frame(IDs = c('Jake', 'John', 'Marc', 'Tony', 'Bob'))
Df1$presentinDf2 <- as.integer(Df1$name %in% Df2$IDs)
Df1
#>   name presentinDf2
#> 1 Marc            1
#> 2 Jake            1
#> 3  Sam            0
#> 4 Brad            0

Check if value from one dataframe exists in another dataframe and create column

df.join(df2.groupby('StartDate')['User'].apply('; '.join), how='left', on='Dates').fillna('')

Output:

>>> df
       Dates          User
0 2021-10-01              
1 2021-10-02              
2 2021-10-03              
3 2021-10-04         Test1
4 2021-10-05              
5 2021-10-06              
6 2021-10-07  Test2, Test1

Check for a value from one dataframe exists in another

First Row

Option 1

You can use df.isin:

first_flag = df2[df2.score.isin([df1.marks[0]])].flag
print(first_flag)
0    T

To get the values, use .values.tolist():

print(first_flag.values.tolist())
['T']

To get a single the value as a single item, use .item:

print(first_flag.item())
'T'

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Option 2

Using df.eval:

score = df1.marks[0]
first_flag = df2[df2.eval('score == {}'.format(score))].flag
print(first_flag)
0    T

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Option 3

Using df.eq

score = df1.marks[0]
first_flag = df2[df2.score.eq(score)].flag
print(first_flag) 
0    T

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All Rows

Use df.merge.

flags = df1.merge(df2, left_on='marks', right_on='score').flag
print(flags)
0    T
1    F
Name: flag, dtype: object

If you want to retrieve NaN for rows where no flag exists, you can do a left join:

flags = df1.merge(df2, left_on='marks', right_on='score', how='left').flag
print(flags)
0      T
1    NaN
2      F

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