Pandas 迭代数据帧中单列的值

Question

I am a beginner to python and pandas.

I have a 5000-row data frame that looks something like this:

INDEX  COL1  COL2  COL3
0      10.0  12.0  15.0
1      14.0  16.0  153.8
2      18.0  20.0  16.3
3      22.0  24.0  101.7

I wish to iterate over the values in COL3 and carry out calculations, such that:

For each row in the data frame, if the value in COL3 is <= 100.0, multiply that value by 10 and assign to variable "New_Value"; Else, multiply the value by 5 and assign to variable "New_Value"

I understand that if statement cannot be directly applied to the data frame series, as it will lead to ambiguous value error. However, I am stuck trying to find the right tool for this task, and would appreciate some guidance.

Cheers

Answer 1

Using np.where :

df['New_Value'] = np.where(df['COL3']<=100,df['COL3']*10,df['COL3']*5)

Answer 2

One liner

df.COL1.apply(lambda x: x*10 if x<=100 else 5*x)

for this example, you can use apply, which will apply a function on each row of your data.

lambda is a quick function that you can define. It will have a bit of a difference compared to normal functions.

The condition is => x*10 if x<=100 so for each x under or equal to 100, multiply it by 10. ELSE multiply it by 5.

Answer 3

Try this:

df['New_Value']=df.COL3.apply(lambda x: 10*x if x<=100 else 5*x)