How to update a Dataframe values from an another Dataframe based on condition-CodePudding

I'm trying to update a "qty" column in a Dataframe based on another Dataframe "qty" column only for specific rows (according to specific types).

Here are my example Dataframes :

df = pd.DataFrame({'op': ['A', 'A', 'A', 'B', 'C'], 'type': ['X', 'Y', 'Z', 'X', 'Z'], 'qty': [3, 1, 8, 0, 4]})
df_xy = pd.DataFrame({'op': ['A', 'B', 'C'], 'qty': [10, 20, 30]})

print(df)
print(df_xy)

  op type  qty
0  A    X    3
1  A    Y    1
2  A    Z    8
3  B    X    0
4  C    Z    4

  op  qty
0  A   10
1  B   20
2  C   30

I try to use the loc function to choose the concerned rows and to compare with the other Dataframe according to my reference column "op" but without success

# Select df rows where "type" is in "types" and set "qty" according to "qty" from df_xy
types = ['X', 'Y']
df.loc[df['type'].isin(types), 'qty'] = df_xy.loc[df_xy['op'] == df['op'], 'qty']
print(df)

I would like to have a Dataframe that is like this :

  op type  qty
0  A    X    10
1  A    Y    10
2  A    Z    8
3  B    X    20
4  C    Z    4

But I have an error specifying that I cannot compare Series Objects that are not labeled the same way

ValueError: Can only compare identically-labeled Series objects

Any help is much appreciated! Thanks in advance!

CodePudding user response：

Use Series.map only for filtered rows in both sides for avoid processing not matched rows, here Z rows:

types = ['X', 'Y']
mask = df['type'].isin(types)
df.loc[mask, 'qty'] = df.loc[mask, 'op'].map(df_xy.set_index('op')['qty'])
print (df)
  op type  qty
0  A    X   10
1  A    Y   10
2  A    Z    8
3  B    X   20
4  C    Z    4

CodePudding user response：

You could combine loc and merge to align your 2 Series:

df.loc[df['type'].isin(types), 'qty'] = df[['op']].merge(df_xy, on='op')['qty']

output:

  op type  qty
0  A    X   10
1  A    Y   10
2  A    Z    8
3  B    X   20
4  C    Z    4