所以我正在尝试解决这个 pandas 练习。我从 Kaggle 得到了这个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
所以现在，我必须根据 location 和 type 列找出哪些属性被低估或高估，哪些属性具有真实价格。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
卡在上面有一段时间了。我应该尝试什么逻辑来解决这个问题？
最佳答案
这是我的解决方案。作为内联注释包含的解释。可能有一些方法可以用更少的步骤来做到这一点；我也会有兴趣学习的。
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 - 如何根据其他列查找列值是否被低估或高估？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54469791/