以与 pyspark 类似的方式在 pandas 中分配一个新列

Question

I have the following dataframe:

df = pd.DataFrame([['A', 1],['B', 2],['C', 3]], columns=['index', 'result'])

index	result
A	1
B	2
C	3

I would like to create a new column, for example multiply the column 'result' by two, and I am just curious to know if there is a way to do it in pandas as pyspark does it.

In pyspark:
df = df\
.withColumn("result_multiplied", F.col("result")*2)

I don't like the fact of writing the name of the dataframe everytime I have to perform an operation as it is done in pandas such as:

In pandas:
df['result_multiplied'] = df['result']*2

Answer 1

Use DataFrame.assign :

df = df.assign(result_multiplied = df['result']*2)

Or if column result is processing in code before is necessary lambda function for processing counted values in column result :

df = df.assign(result_multiplied = lambda x: x['result']*2)

Sample for see difference column result_multiplied is count by multiple original df['result'] , for result_multiplied1 is used multiplied column after mul(2) :

df = df.mul(2).assign(result_multiplied = df['result']*2,
                      result_multiplied1 = lambda x: x['result']*2)
print (df)
  index  result  result_multiplied  result_multiplied1
0    AA       2                  2                   4
1    BB       4                  4                   8
2    CC       6                  6                  12