[英]Add new column to Pandas Dataframe from functions' output
I wrote function to estimate parameters of simple linear regression. 我写了函数来估计简单线性回归的参数。 The function produces several outputs. 该功能产生多个输出。 Function inputs are two lists . 功能输入是两个列表 。 Also, I have initial DataFrame df from where I derived two lists. 此外,我有从我导出两个列表的地方的初始DataFrame df。
I want to add some outputs from function to the initial DataFrame as a new columns or either have new lists outside to function. 我想将函数中的一些输出作为新列添加到初始DataFrame中,或者在函数外部添加新列表。
for example: 例如:
def predict(X,Y):
beta1 = sum([(X[i] - mean_X)*(Y[i] - mean_Y) for i in range(len(X))]) / sum([(X[i] - mean_X)**2 for i in range(len(X))])
beta0 = mean_Y - beta1 * mean_X
y_hat = [beta0 + beta1*X[i] for i in range(len(X))]
return df.assign(prediction = y_hat)
Here, mean_X and mean_Y is sample average for list X and list Y, respectively. 这里,mean_X和mean_Y分别是列表X和列表Y的样本平均值。
Also I tried numpy.insert() to add y_hat into not initial DataFrame but into X which I converted into numpy array. 我还尝试了numpy.insert()将y_hat添加到非初始DataFrame中,但添加到X中,我将其转换为numpy数组。
I have no success to achieve desired result so can someone help me? 我没有成功达到预期的效果,所以有人可以帮助我吗?
As far as I understood your question, you want to use your function in your existing/new column. 据我所知,你想在现有/新专栏中使用你的功能。 If that is case, here is one way to do it. 如果是这种情况,这是一种方法。 If not, then Let me know, I will remove the answer. 如果没有,那么让我知道,我会删除答案。 Thanks 谢谢
import pandas as pd
def Somefunction(x, y):
a = 2 *x
b = 3 * y
return pd.Series([a, b], index= ['YourColumn1', 'YourColumn2'])
df = pd.read_csv('YourFile')
df = df.join(df.apply(lambda x:
Somefunction(x['ColumnYouWantToApplyFunctionReturnValue a'],
x['ColumnYouWantToApplyFunctionReturnValue B']), axis=1))
Your code doesn't seem very clear. 你的代码似乎不太清楚。 What are the mean_X
and mean_Y
variables ? mean_X
和mean_Y
变量是什么?
EDIT : Added variable declaration. 编辑:添加变量声明。
Anyhow, here's a simple suggestion : 无论如何,这是一个简单的建议:
import numpy as np
def predict(X, Y, df):
mean_X = np.mean(X)
mean_Y = np.mean(Y)
beta1 = sum([(X[i] - mean_X)*(Y[i] - mean_Y) for i in range(len(X))]) / sum([(X[i] - mean_X)**2 for i in range(len(X))])
beta0 = mean_Y - beta1 * mean_X
y_hat = [beta0 + beta1*X[i] for i in range(len(X))]
df['prediction'] = y_hat
return df
A cleverer way to proceed would be to use the apply() function called on your DataFrame. 一种更聪明的方法是使用在DataFrame上调用的apply()函数。
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