python-3.x - 如何查找基于其他列的列值是被低估还是被高估？

因此，我正在尝试解决此pandas练习。我从Kaggle获得了房地产公司的数据集，数据框df看起来像这样。

           id           location       type     price
0       44525        Golden Mile      House   4400000
1       44859           Nagüeles      House   2400000
2       45465           Nagüeles      House   1900000
3       50685           Nagüeles       Plot   4250000
4      130728        Golden Mile      House  32000000
5      130856           Nagüeles       Plot   2900000
6      130857        Golden Mile      House   3900000
7      130897        Golden Mile      House   3148000
8      3484102           Marinha       Plot    478000
9      3484124           Marinha       Plot   2200000
10     3485461           Marinha      House   1980000

因此，现在，我必须根据列location和type找出哪个属性被低估或高估，以及哪个具有真实价格。所需的结果应如下所示：

       id           location       type     price   Over_val   Under_val    Norm_val
0   44525        Golden Mile      House   4400000         0      0             1
1   44859           Nagüeles      House   2400000         0      0             1
2   45465           Nagüeles      House   1900000         0      0             1
3   50685           Nagüeles       Plot   4250000         0      1             0
4  130728        Golden Mile      House  32000000         1      0             0
5  130856           Nagüeles       Plot   2900000         0      1             0
6  130857        Golden Mile      House   3900000         0      0             1
7  130897        Golden Mile      House   3148000         0      0             1
8  3484102           Marinha       Plot    478000         0      0             1
9  3484124           Marinha       Plot   2200000         0      0             1
10 3485461           Marinha      House   1980000         0      1             0

已经被卡住了一段时间了。解决这个问题应该尝试什么逻辑？

最佳答案

这是我的解决方案。说明包括在内嵌注释中。可能有一些方法可以减少步骤数；我也会有兴趣学习。

import pandas as pd

# Replace this with whatever you have to load your data. This is set up for a sample data file I used
df = pd.read_csv('my_sample_data.csv', encoding='latin-1')

# Mean by location - type
mdf = df.set_index('id').groupby(['location','type'])['price'].mean().rename('mean').to_frame().reset_index()
# StdDev by location - type
sdf = df.set_index('id').groupby(['location','type'])['price'].std().rename('sd').to_frame().reset_index()
# Merge back into the original dataframe
df = df.set_index(['location','type']).join(mdf.set_index(['location','type'])).reset_index()
df = df.set_index(['location','type']).join(sdf.set_index(['location','type'])).reset_index()

# Add the indicator columns
df['Over_val'] = 0
df['Under_val'] = 0
df['Normal_val'] = 0

# Update the indicators
df.loc[df['price'] > df['mean'] + 2 * df['sd'], 'Over_val'] = 1
df.loc[df['price'] < df['mean'] - 2 * df['sd'], 'Under_val'] = 1

df['Normal_val'] = df['Over_val'] + df['Under_val']
df['Normal_val'] = df['Normal_val'].apply(lambda x: 1 if x == 0 else 0)

关于python-3.x - 如何查找基于其他列的列值是被低估还是被高估？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54469791/