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I have 2 dataframes.
Df1 = pd.DataFrame({'name': ['Marc', 'Jake', 'Sam', 'Brad'] Df2 = pd.DataFrame({'IDs': ['Jake', 'John', 'Marc', 'Tony', 'Bob'] I want to loop over every row in Df1['name'] and check if each name is somewhere in Df2['IDs'].
The result should return 1 if the name is in there, 0 if it is not like so:
Marc 1 Jake 1 Sam 0 Brad 0 Thank you.
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You can check if a column contains/exists a particular value (string/int), list of multiple values in pandas DataFrame by using pd. series() , in operator, pandas. series. isin() , str.
The compare method in pandas shows the differences between two DataFrames. It compares two data frames, row-wise and column-wise, and presents the differences side by side. The compare method can only compare DataFrames of the same shape, with exact dimensions and identical row and column labels.
isin() function check whether values are contained in Series. It returns a boolean Series showing whether each element in the Series matches an element in the passed sequence of values exactly.
Use isin
Df1.name.isin(Df2.IDs).astype(int) 0 1 1 1 2 0 3 0 Name: name, dtype: int32 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 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 By using merge
s=Df1.merge(Df2,left_on='name',right_on='IDs',how='left') s.IDs=s.IDs.notnull().astype(int) s Out[68]: name IDs 0 Marc 1 1 Jake 1 2 Sam 0 3 Brad 0 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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