Replace rows in a Pandas df with rows from another df

Question

I have 2 Pandas dfs, A and B. Both have 10 columns and the index \'ID\'. Where the IDs of A and B match, I want to replace the rows of B with the rows of A. I have tried to

Answer 1

below code should do the trick

s1 = pd.Series([5, 1, 'a'])
s2 = pd.Series([6, 2, 'b'])
s3 = pd.Series([7, 3, 'd'])
s4 = pd.Series([8, 4, 'e'])
s5 = pd.Series([9, 5, 'f'])



df1 = pd.DataFrame([list(s1), list(s2),list(s3),list(s4),list(s5)],  columns =  ["A", "B", "C"])

s1 = pd.Series([5, 6, 'p'])
s2 = pd.Series([6, 7, 'q'])
s3 = pd.Series([7, 8, 'r'])
s4 = pd.Series([8, 9, 's'])
s5 = pd.Series([9, 10, 't'])

df2 = pd.DataFrame([list(s1), list(s2),list(s3),list(s4),list(s5)],  columns =  ["A", "B", "C"])

df1.loc[df1.A.isin(df2.A), ['B', 'C']] = df2[['B', 'C']]
print df1

output

   A   B  C
0  5   6  p
1  6   7  q
2  7   8  r
3  8   9  s
4  9  10  t

Edit from comments:

To replace the whole row instead of only some columns:

cols = list(df1.columns) 
df1.loc[df1.A.isin(df2.A), cols] = df2[cols]

Answer 2

You can empty your target cells in A (by setting them to NaN) and use the combine_first() method to fill those with B's values. Although it may sound counter-intuitive, this approach gives you the flexibility to both target rows and specific columns in 2 lines of code. Hope that helps.

An example replacing the full row's that have an index match:

# set-up
cols = ['c1','c2','c3']
A = pd.DataFrame(np.arange(9).reshape((3,3)), columns=cols)
B = pd.DataFrame(np.arange(10,16).reshape((2,3)), columns=cols)

#solution
A.loc[B.index] = np.nan
A = A.combine_first(B)

An example of only replacing certain target columns for row's that have an index match:

A.loc[B.index, ['c2','c3']] = np.nan
A = A.combine_first(B)