Pandas：使一列的值等于另一列的值

Question

Hopefully a very simple question from a Pandas newbie.

How can I make the value of one column equal the value of another in a dataframe? Replace the value in every row. No conditionals, etc.

Context:

I have two CSV's, loaded into dataframe 'a' and dataframe 'b' respectively.

These CSVs are basically the same, except 'a' has a field that was improperly carried forward from another process - floats were rounded to ints. Not my script, can't influence it, I just have the CSVs now.

In reality I probably have 2mil rows and about 60-70 columns in the merged dataframe - so if it's possible to address the columns by their header (in the example these are Col1 and xyz_Col1), that would sure help.

I have joined the CSVs on their common field, so now I have a scenario where I have a dataframe that can be represented by the following:

+--------+------+--------+------------+----------+----------+
| CellID | Col1 |  Col2  | xyz_CellID | xyz_Col1 | xyz_Col2 |
+--------+------+--------+------------+----------+----------+
|      1 |    0 | apple  |          1 | 0.23     | apple    |
|      2 |    0 | orange |          2 | 0.45     | orange   |
|      3 |    1 | banana |          3 | 0.68     | banana   |
+--------+------+--------+------------+----------+----------+

The result should be such that Col1 = xyz_Col1:

+--------+------+--------+------------+----------+----------+
| CellID | Col1 |  Col2  | xyz_CellID | xyz_Col1 | xyz_Col2 |
+--------+------+--------+------------+----------+----------+
|      1 | 0.23 | apple  |          1 | 0.23     | apple    |
|      2 | 0.45 | orange |          2 | 0.45     | orange   |
|      3 | 0.68 | banana |          3 | 0.68     | banana   |
+--------+------+--------+------------+----------+----------+

What I have in code so far:

import pandas as pd

a = pd.read_csv('csv1.csv')
b = pd.read_csv('csv2.csv')
#b = b.dropna(axis=1) drop any unnamed fields

#defind 'b' cols by adding an xyz_ prefix as xyz is unique
b = b.add_prefix('xyz_')

#Join the dataframes into a new dataframe named merged
merged = pd.merge(a, b, left_on='Col1', right_on='xyz_Col1')

merged.head(5)

#This is where the xyz_Col1 to Col1 code goes...

#drop unwanted cols
merged = merged[merged.columns.drop(list(merged.filter(regex='xyz')))]

#output to file
merged.to_csv("output.csv", index=False)

Thanks

Answer 1

merged['col1'] = merged['xyz_Col1']

或者

merged.loc[:, 'col1'] = merged.loc[:, 'xyz_Col1']