如何创建 function 以使 Pandas 中的此步骤更容易？

Question

I have a DataFrame that looks like this below:

ID	Clicks	Clicks_GA	Discrep_%	Discrep_Found
5939	18482	18480	.01	False

#Calculates the discrepancy % (I also import numpy as np)

df['Discrep_%'] = np.absolute(df['Clicks'] - df['Clicks_GA']) / (df['Clicks_GA'] * 100)

#Returns true or false if the discrepancy is less than the abs value of 5%

df['Discrep_Found'] = (df['Discrep_%'] >.05)

The problem is that I have multiple dataframes, and I don't want to copy and paste the same line of code a bunch of times.

Is there a function I can use to make this process simpler?

Thanks!

Answer 1

Try this:

def count_some(df):
    val = np.absolute(df['Clicks'] - df['Clicks_GA']) / (df['Clicks_GA']  * 100)
    return val, val > .05

df[["Discrep_%", "Discrep_Found"]] = df.apply(count_some, axis=1, result_type='expand')

Answer 2

You could loop through the DataFrames. For example:

for df in [df1, df2, df3, ...]:
    df['Discrep_%'] = np.absolute(df['Clicks'] - df['Clicks_GA']) / (df['Clicks_GA']  * 100)
    df['Discrep_Found'] =  (df['Discrep_%'] > .05)