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I have a pandas dataframe, df1:

id  level  val
1   l1     0.2
1   l2     0.1

and another dataframe which contains type for l1 level, df2:

id  type
1   A

Similarly, df3 which contains type for l2 level as well as new ids:

id  new_id  type
1   19      A
1   19      B
1   20      A
1   20      B

Now I want type for l1 level from df2. And for l2 level from df3, while also resetting ids and level. This is what I am doing:

df2['level'] = 'l1'
df3['level'] = 'l2'
df1 = pd.merge(df1, df2, how='outer', on=['id','level'])
df1 = pd.merge(df1, df3, how='outer', on=['id','level'], suffixes=["_", ""])
df1['id'] = np.where(df1['new_id'].isnull(), df1['id'], df1['new_id'])
df1['type'] = np.where(df1['type'].isnull(), df1['type_'], df1['type'])
df1['level'] = np.where(df1['level']=='l2', 'l3', df1['level'])
df1 = df1.drop('new_id', 1)
df1 = df1.drop('type_', 1)

Which finally gives me, df1:

id  level  val  type
1   l1     0.2  A
19  l3     0.1  A
19  l3     0.1  B
20  l3     0.1  A
20  l3     0.1  B

Since df1 is a large dataframe, I want to avoid using np.where thrice, i.e., I am looking for a faster approach to get this. Thanks!

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keyword arguments

I had to check the documentation to find out what the 1 means in df1.drop('new_id', 1). More clear would be df1.drop('new_id', axis=1), or even better: df1.drop(columns=["type_", "new_id"])

Don't overwrite raw data

You add columns and values to df1, which makes finding problems harder.

With a bit of reshuffling, and using df.join instead of pd.merge, you can make your intent a bit more clear

df6= df1.join(
    df2.assign(level="l1").set_index(["id", "level"]),
    how="outer",
    on=["id", "level"],
).join(
    df3.assign(level="l2").set_index(["id", "level"]),
    how="outer",
    on=["id", "level"],
    lsuffix="_",
)

np.where

If you're looking for null, you can use fillna or combine_first. To replace 'l2' with 'l3', you can use Series.replace.

df7 = df6.assign(
    id=df6["new_id"].fillna(df6["id"]).astype(int),
    type=df6["type_"].fillna(df6["type"]),
    level=df6["level"].replace("l2", "l3")
).drop(columns=["type_", "new_id"])

I don't know whether this approach will be faster, but to me, what happens here is a lot more clear.

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