Check if column values exists in different dataframe

Question

I have a pandas DataFrame 'df' with x rows, and another pandas DataFrame 'df2' with y rows (x < y). I want to return the indexes of where the values of df['Farm'] equals the value of df2['Fields'], in order to add respective 'Manager' to df.

the code I have is as follows:

data2 = [['field1', 'Paul G'] , ['field2', 'Mark R'], ['field3', 'Roy Jr']]
data = [['field1'] , ['field2']]
columns = ['Field']
columns2 = ['Field', 'Manager']
df = pd.DataFrame(data, columns=columns)
df2 = pd.DataFrame(data2, columns=columns2)

farmNames = df['Farm']
exists = farmNames.reset_index(drop=True) == df1['Field'].reset_index(drop=True)

This returns the error message:

ValueError: Can only compare identically-labeled Series objects

Does anyone know how to fix this?

Yes! I want to use a boolean series 'exists' to merge another column (say 'Manager') in df2, into df — python_newbie
– python_newbie, Commented Feb 27, 2022 at 4:08
Not sure what you mean - could you post two example input dataframes for df1 and df2, plus the output? They don't need to be big. In fact, the smaller the better as long as it shows the problem. — Nick ODell
– Nick ODell, Commented Feb 27, 2022 at 4:13

Manjunath K Mayya · Accepted Answer · 2022-02-27 04:42:41Z

1

As @NickODell mentioned, you could use a merge, basically a left join. See below code.

df_new = pd.merge(df, df2, on = 'Field', how = 'left')
print(df_new)

Output:

    Field Manager
0  field1  Paul G
1  field2  Mark R

answered Feb 27, 2022 at 4:42

Manjunath K Mayya

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1 Comment

python_newbie Over a year ago

oh wow, another reason to love python i guess. Thanks!

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