Below is my Dataframe:
X1 X2 X3 X4 X5
A B C 10 BAM
A A A 12 BAM
B B B 10 BAM
A B B 60 BAM
I want those rows having same values in columns(X1, X2,X3). Here we can see 2nd and 3rd rows are having same values for above 3 columns.
My desired output is:
X1 X2 X3 X4 X5
A A A 12 BAM
B B B 10 BAM
I tried like below:
yourdf1=df[df.nunique(0)==0]
print(yourdf1)
But here i am getting an error. Could anyone please help me.
Select columns in list for test number of unique values per rows by axis=1 in DataFrame.nunique and test 1 for filter by boolean indexing:
yourdf1 = df[df[['X1','X2','X3']].nunique(axis=1) == 1]
print(yourdf1)
X1 X2 X3 X4 X5
1 A A A 12 BAM
2 B B B 10 BAM
Another solution is use DataFrame.eq with filtered DataFrame, compare by first column and get all Trues per rows by DataFrame.all:
df1 = df[['X1','X2','X3']]
yourdf1 = df[df1.eq(df1.iloc[:, 0], axis=0).all(axis=1)]
print(yourdf1)
X1 X2 X3 X4 X5
1 A A A 12 BAM
2 B B B 10 BAM
Try
yourdf = df[~df.duplicated(subset=['X1','X2','X3'])]
Please see attached
df[df[['X1','X2','X3']].duplicated(keep=False)]
You can iterate over each row and compare columns with each other, and attach the rows which are the same to a new dataframe. The code would look something like this:
df2 = pd.DataFrame()
for row in df.rows:
if (row['X1'] == row['X2'] and row['X2'] == row['X3']):
df2.append(row)
display(df2)
