Pandas.DataFrame - create a new column, based on whether value in another column has occur-ed or not

Question

I'm an amateur user having some experiences VBA but trying to switch to Python because my beautiful new MBP runs VBA miserably. I'm trying to create a df column, based on whether another column value has occur-ed already. If it has, then the new column value is 0 on that row, if not 1.

For example: I want to create column C in the example below. How do I do it quickly?

 AB C (to create column C) 0 001 USA 1 1 002 Canada 1 3 003 China 1 4 004 India 1 5 005 UK 1 6 006 Japan 1 7 007 USA 0 8 008 UK 0

Answer 1

You can check for duplicates on the 'B' column and set duplicates to 0. Then set any non-duplicates to 1 like this:

 df = pd.DataFrame({'A':[1, 2, 3, 4, 5, 6, 7, 8], 'B':['USA', 'Canada', 'China', 'India', 'UK', 'Jpan', 'USA', 'UK']}) df.loc[df['B'].duplicated(), 'C'] = 0 df['C'] = df['C'].fillna(1).astype(int) print(df)

Output:

 AB C 0 1 USA 1 1 2 Canada 1 2 3 China 1 3 4 India 1 4 5 UK 1 5 6 Jpan 1 6 7 USA 0 7 8 UK 0

Answer 2

After creating your dataframe:

 import pandas as pandas data = [["001", "USA"], ["002", "Canada"], ["003", "China"], ["004", "India"], ["005", "UK"], ["006", "Japan"], ["007", "USA"], ["008", "UK"]] # Create a dataframe df = pandas.DataFrame(data, columns=["A", "B"])

You can apply a function to each value of one of the columns (in your case, the B column) and have the output of the function as the value of your column.

 df["C"] = df.B.apply(lambda x: 1 if df.B.value_counts()[x] == 1 else 0)

This will check if the value in the B column appears somewhere else in the column, and will return 1 if unique and 0 if duplicated.

The dataframe looks like this:

 AB C 0 001 USA 0 1 002 Canada 1 2 003 China 1 3 004 India 1 4 005 UK 0 5 006 Japan 1 6 007 USA 0 7 008 UK 0

If you want the values to be recalculated each time you need to have the command

df["C"] = df.B.apply(lambda x: 1 if df.B.value_counts()[x] == 1 else 0)

executed each time after you add a row.