[英]How to apply custom function to pandas data frame for each row
I want to apply a custom function and create a derived column called population2050 that is based on two columns already present in my data frame. 我想应用自定义函数并创建一个名为population2050的派生列,该列基于我的数据框中已存在的两列。
import pandas as pd
import sqlite3
conn = sqlite3.connect('factbook.db')
query = "select * from facts where area_land =0;"
facts = pd.read_sql_query(query,conn)
print(list(facts.columns.values))
def final_pop(initial_pop,growth_rate):
final = initial_pop*math.e**(growth_rate*35)
return(final)
facts['pop2050'] = facts['population','population_growth'].apply(final_pop,axis=1)
When I run the above code, I get an error. 当我运行上面的代码时,我收到一个错误。 Am I not using the 'apply' function correctly? 我没有正确使用'apply'功能吗?
Apply will pass you along the entire row with axis=1. Apply将沿着整个行传递,轴= 1。 Adjust like this assuming your two columns are called initial_pop
and growth_rate
假设您的两列名为initial_pop
和growth_rate
,请growth_rate
def final_pop(row):
return row.initial_pop*math.e**(row.growth_rate*35)
You were almost there: 你几乎在那里:
facts['pop2050'] = facts.apply(lambda row: final_pop(row['population'],row['population_growth']),axis=1)
Using lambda allows you to keep the specific (interesting) parameters listed in your function, rather than bundling them in a 'row'. 使用lambda允许您保留函数中列出的特定(有趣)参数,而不是将它们捆绑在“行”中。
You can achieve the same result without the need for DataFrame.apply()
. 无需DataFrame.apply()
即可获得相同的结果。 Pandas series (or dataframe columns) can be used as direct arguments for NumPy functions and even built-in Python operators, which are applied element-wise. Pandas系列(或数据帧列)可以用作NumPy函数的直接参数,甚至是内置的Python运算符,它们是按元素应用的。 In your case, it is as simple as the following: 在您的情况下,它就像以下一样简单:
import numpy as np
facts['pop2050'] = facts['population'] * np.exp(35 * facts['population_growth'])
This multiplies each element in the column population_growth
, applies numpy's exp()
function to that new column ( 35 * population_growth
) and then adds the result with population
. 这会将列population_growth
中的每个元素相乘,将numpy的exp()
函数应用于该新列( 35 * population_growth
),然后将结果与population
一起添加。
Your function, 你的功能,
def function(x):
// your operation
return x
call your function as, 把你的职能称为,
df['column']=df['column'].apply(function)
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.