Find same values from 2 df columns python

Question

I have two different dfs with the following columns:

col1                    col2

0 programming           0 programming
1 chess                 1 python
2 leadership            2 leadership
                        3 abba
                        4 games

I want to find what percentage of values of col1 present in col2

For that, I thought I could construct a new df which will contain the same values and then its len to len(col2). First I want to get this:

col3

0 programming
1 leadership

Here is what I tried, but I dont want to use two for loops

bb=[]
for i in company.col2:
    for b in student.col1:
        if i==b:
            bb.append(i)

Vidhya G · Accepted Answer · 2018-09-15 10:37:30Z

1

>>> df1 = pd.DataFrame(["programming", "chess", "leadership"], columns=["col1"])
>>> df2 = pd.DataFrame(["programming", "python", "leadership", "abba", "games"], columns=["col2"])

To find which values of df1['col1'] are in df2['col2'] use isin. The result is a boolean column. Use sum to find the number of values:

>>> df1['col1'].isin(df2['col2']).sum()

Use size to find the number of elements in df2 and divide thus:

>>> df1['col1'].isin(df2['col2']).sum() / df2.size

edited Sep 15, 2018 at 10:37

answered Sep 15, 2018 at 10:05

Vidhya G

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Comments

sameera sy · Accepted Answer · 2018-09-15 08:55:25Z

0

You can use list comprehension, try the below

list1 = ["programming", "chess", "leadership"]
list2 = ["programming", "python", "leadership", "abba", "games"]
list3 = [value for value in list1 if value in list2]

list3 = ["programming", "leadership"]

In your use case, you can just try the below.

list3 = [value for value in company.col2 if value in student.col1]

answered Sep 15, 2018 at 8:55

sameera sy

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Comments

Paolo Irrera · Accepted Answer · 2018-09-15 09:05:52Z

0

You can use pandas.merge to perform an inner join:

def main():

    df = pd.DataFrame(["programming", "chess", "leadership"], columns=["col1"])
    df2 = pd.DataFrame(["programming", "python", "leadership", "abba", "games"], columns=["col2"])
    df3 = df.merge(df2, left_on="col1", right_on="col2", how="inner")
    print(df3["col1"])

answered Sep 15, 2018 at 9:05

Paolo Irrera

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