pandas copy value from one column to another if condition is met

Question

I have a dataframe:

df = 
col1  col2  col3 
1      2     3
1      4     6
3      7     2

I want to edit df, such that when the value of col1 is smaller than 2 , take the value from col3.

So I will get:

new_df = 
col1  col2  col3 
3      2     3
6      4     6
3      7     2

I tried to use assign and df.loc but it didn't work.

What is the best way to do so?

Terrell

you can use df['col1'] = df['col1'].mask(df['col1'] < 2,df['col3']), or df = df.assign(col1=df['col1'].mask(df['col1'] < 2,df['col3'])) or similar with np.where – Kor

Answer 1

df['col1'] = df.apply(lambda x: x['col3'] if x['col1'] < x['col2'] else x['col1'], axis=1)

Hub

Answer 2

The most eficient way is by using the loc operator:

mask = df["col1"] < df["col2"]
df.loc[mask, "col1"] = df.loc[mask, "col3"]

Expatiate

How can the same be achieved when values from multiple columns are to be copied? Something like ["col1", "col2"] instead of "col1" in the second parameter for loc? – Boone

You can do multiple df.loc statements with different filters – Expatiate

You mean this? -> df.loc[mask, ["col1", "col2"]] = df.loc[mask, ["col3", "col4"]]. It doesn't work for me. But it works when I copy them one by one. – Boone

Answer 3

df.loc[df["col1"] < 2, "col1"] = df["col3"]

Bleacher

Answer 4

As mentioned by @anky_91 use np.where to update the 'col1' values:

df['col1'] = np.where(df['col1'] < df['col2'], df['col3'], df['col1'])

Donate

Answer 5

You could look at using the apply function.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html

df['col1'] = df.apply(lambda c: c['col3'] if c['col1'] < 2 else c['col1'], axis=1)

Edit: Sorry, I see from your mock outcome you are referring to col2 rather than an int of 2. Eric Yang's answer will solve your problem.

Omland