一种干净有效的方法来更新Pandas DataFrames中的单元格

Question

I am looking for a cleaner way to achieve the following:

I have a DataFrame with certain columns that I want to update if new information arrives. This "new information" in for of a pandas DataFrame (from a CSV file) can have more or less rows, however, I am only interested in adding

Original DataFrame

DataFrame with new information

(Note the missing name " c " here and the change in "status" for name " a ")

Now, I wrote the following "inconvenient" code to update the original DataFrame with the new information

Updating the "status" column based on the "name" column

for idx,row in df_base.iterrows():
    if not df_upd[df_upd['name'] == row['name']].empty:
        df_base.loc[idx, 'status'] = df_upd.loc[df_upd['name'] == row['name'], 'status'].values

It achieves exactly what I want, but it just does neither look nice nor efficient, and I hope that there might be a cleaner way. I tried the pd.merge method, however, the problem is that it would be adding new columns instead of "updating" the cells in that column.

pd.merge(left=df_base, right=df_upd, on=['name'], how='left')

I am looking forward to your tips and ideas.

Answer 1

You could set_index("name") and then call .update :

>>> df_base = df_base.set_index("name")
>>> df_upd = df_upd.set_index("name")
>>> df_base.update(df_upd)
>>> df_base
      status
name        
a          0
b          1
c          0
d          1

More generally, you can set the index to whatever seems appropriate, update, and then reset as needed.