Dataframe containing counts where column conditions are met

Asked Jul 04 '18 at 13:57

Active Jul 04 '18 at 13:57

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I have a dataframe with several different columns where I would like to sum the counts of where a df[x] = df[y].

So to make an example of what I'm trying to do, I'll use two example columns.

Imagine a DF with a column FAVORITE_COLORS and a column COUNTRY.

I would like to sum all the different combinations of these, I.e how many times England has the favorite color "Blue", "Yellow" etc. and store this in a df.

So my desired output would be

    COUNTRY        BLUE        YELLOW     COLOR...
0  England         1232        3312       Sum
1  Hungary         5543        7661       Sum
2  ETC             ETC         ETC

I tried using the pandas groupby and count, using pythons len(df) when I created a new df containing only the matches, but it seemed very cumbersome and I couldn't get it to work properly.

Any thoughts on how to approach this?

Thanks in advance

asked Jul 04 '18 at 13:57

zagzig

Can you provide an example of what your expected output should look like? – vielkind Jul 04 '18 at 14:02
`df.groupby('Country')['Favorite_color'].value_counts().unstack()` – sacuL Jul 04 '18 at 14:03

Dataframe containing counts where column conditions are met

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