Question

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

所以现在，我必须根据列location和type来找出哪个属性被低估或高估了，哪个具有真实的价格。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

已经停留了一段时间。解决这个问题应该尝试什么逻辑？

Answer 1

这是我的解决方案。说明包括在内嵌注释中。可能可以通过较少的步骤来完成此操作。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)

Answer 2

这是另一种可能的方法。在2个标准偏差下，没有合格属性。一个标准开发者只有一个财产。

import pandas as pd

df = pd.DataFrame(data={}, columns=["id", "location", "type", "price"])

# data is already entered, left out for this example
df["id"] = prop_id
df["location"] = location
df["type"] = prop_type
df["price"] = price

# a function that returns the mean and standard deviation
def mean_std_dev(row):
    mask1 = df["location"] == row["location"]
    mask2 = df["type"] == row["type"]
    df_filt = df[mask1 & mask2]
    mean_price = df_filt["price"].mean()
    std_dev_price = df_filt["price"].std()

    return [mean_price, std_dev_price]


# create two columns and populate with the mean and std dev from function mean_std_dev
df[["mean", "standard deviation"]] = df.apply(
    lambda row: pd.Series(mean_std_dev(row)), axis=1
)

# create final columns
df["Over_val"] = df.apply(
    lambda x: 1 if x["price"] > x["mean"] + x["standard deviation"] else 0, axis=1
)
df["Under_val"] = df.apply(
    lambda x: 1 if x["price"] < x["mean"] - x["standard deviation"] else 0, axis=1
)
df["Norm_val"] = df.apply(
    lambda x: 1 if x["Over_val"] + x["Under_val"] == 0 else 0, axis=1
)

# delete the mean and standard deviation columns
df.drop(["mean", "standard deviation"], axis=1)

如何查找基于其他列的列值是被低估还是被高估？

2 个答案: