Creating a new column in a Pandas dataframe by aggregating values from different columns

I have recently included a new column in the table called
and my intention now is to populate this column with values for each entry, based on the conditions of the
and
column values.
Query:
My dataframe consists of values like
My objective is to create a new column by combining values from column A and B, such as
I am under the impression that this can be achieved using a lambda function, but I am struggling to comprehend the process.


Question:

I have a dataframe with values like

A B
1 4
2 6
3 9

It is necessary to create an additional column by combining the values in column A and B, referred to as
adding values
.

A B C
1 4 5
2 6 8
3 9 12

I think a lambda function can achieve this, but I’m unsure about the implementation.


Solution 1:

Very simple:

df['C'] = df['A'] + df['B']


Solution 2:


Expanding on Anton’s response, an alternative approach would be to include all the columns in the following manner:

df['sum'] = df[list(df.columns)].sum(axis=1)


Solution 3:


One option is to utilize the DeepSpace answer as the easiest approach. Nonetheless, if the preference is to use an anonymous function, the apply method can be employed.

df['C'] = df.apply(lambda row: row['A'] + row['B'], axis=1)


Solution 4:

As mentioned in the comment by @EdChum, you have the option of utilizing the

sum

function to accomplish this.

df['C'] =  df[['A', 'B']].sum(axis=1)
In [245]: df
Out[245]: 
   A  B   C
0  1  4   5
1  2  6   8
2  3  9  12

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