13

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?

Cranjis
  • 1,590
  • 8
  • 31
  • 64
  • 2
    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` – anky Feb 17 '20 at 14:20

5 Answers5

14
df['col1'] = df.apply(lambda x: x['col3'] if x['col1'] < x['col2'] else x['col1'], axis=1)
Eric Yang
  • 2,678
  • 1
  • 12
  • 18
7

The most eficient way is by using the loc operator:

mask = df["col1"] < df["col2"]
df.loc[mask, "col1"] = df.loc[mask, "col3"]
villoro
  • 1,469
  • 1
  • 11
  • 14
  • 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`? – Benison Sam Apr 27 '22 at 09:35
  • You can do multiple `df.loc` statements with different filters – villoro May 04 '22 at 09:47
  • 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. – Benison Sam May 04 '22 at 13:44
3
df.loc[df["col1"] < 2, "col1"] = df["col3"]
mkk
  • 76
  • 2
3

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'])
ManojK
  • 1,570
  • 2
  • 9
  • 17
0

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.

sirdoug9
  • 94
  • 5