如果每个值都相等，则删除熊猫数据框行

Question

If I have a pandas dataframe which has a row containing float values and all the values are equal in the row, how do I delete that row from the dataframe?

Answer 1

Use DataFrame.nunique for test number of unique values per rows with Series.ne for filter out unique rows by boolean indexing :

df1 = df[df.nunique(axis=1).ne(1)]

Or test if not equal first column and test if at least one True per rows by DataFrame.any :

df1 = df[df.ne(df.iloc[:, 0], axis=0).any(axis=1)]

EDIT: If want remove all rows and all columns with same values solution should be changed for test columns with loc and axis=0 :

df = pd.DataFrame({
         'B':[4,4,4,4,4,4],
         'C':[4,4,9,4,2,3],
         'D':[4,4,5,7,1,0],

})

print (df)
   B  C  D
0  4  4  4
1  4  4  4
2  4  9  5
3  4  4  7
4  4  2  1
5  4  3  0

df2 = df.loc[df.nunique(axis=1).ne(1), df.nunique(axis=0).ne(1)]

And for second solution:

df2 = df.loc[df.ne(df.iloc[:, 0], axis=0).any(axis=1), df.ne(df.iloc[0], axis=1).any(axis=0)]

---

print (df2)
   C  D
2  9  5
3  4  7
4  2  1
5  3  0

Answer 2

You can use DataFrame.diff over axis=1 (per row):

# Example dataframe:
df = pd.DataFrame({'Col1':[1,2,3],
                   'Col2':[2,2,5],
                   'Col3':[4,2,9]})

   Col1  Col2  Col3
0     1     2     4
1     2     2     2  # <-- row with all same values
2     3     5     9

df[df.diff(axis=1).fillna(0).ne(0).any(axis=1)]

   Col1  Col2  Col3
0     1     2     4
2     3     5     9