CODEWITHSUNDEEP
pythonpandasdataframe
cvluepke
cvluepke

Reputation: 105

Compare values of multiple pandas columns

let's say I have four columns with strings in each column (pandas df). If I want to compare if they are all the same, I came up with something like this:

df['same_FB'] = np.where( (df['FB_a'] == df['FB_b']) & (df['FB_a'] == df['FB_c']) & (df['FB_a'] == df['FB_d']), 1,0)

It works fine, but it doesn't look good and if I had to add a fifth or sixth column it get's even uglier. Is there another way to test if all columns are the same? Alternatively, I would be ok with counting the distinct values in these four columns.

Upvotes: 1

Views: 172

Answers (2)

Shubham Sharma
Shubham Sharma

Reputation: 71707

You can use DataFrame.eq + DataFrame.all:

x,*y = ['FB_a', 'FB_b', 'Fb_c', 'FB_d']
df['same_FB'] = df[y].eq(df[x], axis=0).all(1).view('i1')

Alternatively you can use nunique:

c = ['FB_a', 'FB_b', 'Fb_c', 'FB_d']
df['same_FB'] = df[c].nunique(axis=1, dropna=False).eq(1).view('i1')

Example:

print(df)

    A  B  C  D  E
0  10  1  1  1  1
1  20  2  2  2  2
2  30  3  3  3  3
3  40  4  4  4  4

x,*y = ['B', 'C', 'D', 'E']
df['same'] = df[y].eq(df[x], axis=0).all(1).view('i1')

print(df)

    A  B  C  D  E  same
0  10  1  1  1  1     1
1  20  2  2  2  2     1
2  30  3  3  3  3     1
3  40  4  4  4  4     1

Upvotes: 2

DapperDuck
DapperDuck

Reputation: 2859

You can use chained python logic. Here is the code:

df['same_FB'] = np.where((df['FB_a'] == df['FB_b'] == df['FB_c'] == df['FB_d']), 1,0)

Upvotes: 1

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