填补数据帧中其他行中父级的空白

Question

假设我有一个显示速度限制的数据集。其理念是，每个地区或城市都可以应用自己的规则，或“继承”其父实体的规则

+-------------+---------------------------+---------------------+-----------+
| country     | region                    | city                | max_speed |
+-------------+---------------------------+---------------------+-----------+
| France      |                           |                     | 50        |
+-------------+---------------------------+---------------------+-----------+
| France      | Bretagne                  |                     | 70        |
+-------------+---------------------------+---------------------+-----------+
| France      | Bretagne                  | Saint-Grégoire      |           |
+-------------+---------------------------+---------------------+-----------+
| France      | Bretagne                  | Saint-Malo          | 30        |
+-------------+---------------------------+---------------------+-----------+
| France      | Île-de-France             |                     |           |
+-------------+---------------------------+---------------------+-----------+
| France      | Île-de-France             | Saint-Cloud         |           |
+-------------+---------------------------+---------------------+-----------+
| France      | Île-de-France             | Vélizy-Villacoublay | 50        |
+-------------+---------------------------+---------------------+-----------+
| Germany     |                           |                     | 70        |
+-------------+---------------------------+---------------------+-----------+
| Germany     | Bayern                    |                     |           |
+-------------+---------------------------+---------------------+-----------+
| Germany     | Bayern                    | Nürnberg            |           |
+-------------+---------------------------+---------------------+-----------+
| Netherlands |                           |                     | 90        |
+-------------+---------------------------+---------------------+-----------+
| Netherlands | Provincie Gelderland      |                     |           |
+-------------+---------------------------+---------------------+-----------+
| Netherlands | Provincie   Gelderland    | Harderwijk          |           |
+-------------+---------------------------+---------------------+-----------+
| Netherlands | Provincie Noord-Holland   |                     | 70        |
+-------------+---------------------------+---------------------+-----------+
| Netherlands | Provincie Noord-Holland   | Haarlem             |           |
+-------------+---------------------------+---------------------+-----------+
| Netherlands | Provincie Noord-Holland   | Hoorn               | 30        |
+-------------+---------------------------+---------------------+-----------+

每当max_speed值丢失时，应将其推断为父级的值。例如，圣格雷戈瓦的限速是布列塔涅的限速，而哈德维克和纽伦堡则适用该国的规则（分别为90和70）

因此，考虑到这个DataFrame：

data = {'country': ['France', 'France', 'France', 'France', 'France', 'France', 'France', 'Germany', 'Germany', 'Germany', 'Netherlands', 'Netherlands', 'Netherlands', 'Netherlands', 'Netherlands', 'Netherlands'],
'region': [None, 'Bretagne', 'Bretagne', 'Bretagne', 'Île-de-France', 'Île-de-France', 'Île-de-France', None, 'Bayern', 'Bayern', None, 'Provincie Gelderland', 'Provincie Gelderland', 'Provincie Noord-Holland', 'Provincie Noord-Holland', 'Provincie Noord-Holland'],
'city': [None, None, 'Saint-Grégoire', 'Saint-Malo', None, 'Saint-Cloud', 'Vélizy-Villacoublay', None, None, 'Nürnberg', None, None, 'Harderwijk', None, 'Haarlem', 'Hoorn'],
'max_speed': [50, 70, None, 30, None, None, 50, 70, None, None, 90, None, None, 70, None, 30]}

speed_limits = pd.DataFrame(data)

如何填写max_speed中缺少的值以获得：

+-------------+-------------------------+---------------------+-----------+
| country     | region                  | city                | max_speed |
+-------------+-------------------------+---------------------+-----------+
| France      |                         |                     |        50 |
+-------------+-------------------------+---------------------+-----------+
| France      | Bretagne                |                     |        70 |
+-------------+-------------------------+---------------------+-----------+
| France      | Bretagne                | Saint-Grégoire      |        70 |
+-------------+-------------------------+---------------------+-----------+
| France      | Bretagne                | Saint-Malo          |        30 |
+-------------+-------------------------+---------------------+-----------+
| France      | Île-de-France           |                     |        50 |
+-------------+-------------------------+---------------------+-----------+
| France      | Île-de-France           | Saint-Cloud         |        50 |
+-------------+-------------------------+---------------------+-----------+
| France      | Île-de-France           | Vélizy-Villacoublay |        50 |
+-------------+-------------------------+---------------------+-----------+
| Germany     |                         |                     |        70 |
+-------------+-------------------------+---------------------+-----------+
| Germany     | Bayern                  |                     |        70 |
+-------------+-------------------------+---------------------+-----------+
| Germany     | Bayern                  | Nürnberg            |        70 |
+-------------+-------------------------+---------------------+-----------+
| Netherlands |                         |                     |        90 |
+-------------+-------------------------+---------------------+-----------+
| Netherlands | Provincie Gelderland    |                     |        90 |
+-------------+-------------------------+---------------------+-----------+
| Netherlands | Provincie Gelderland    | Harderwijk          |        90 |
+-------------+-------------------------+---------------------+-----------+
| Netherlands | Provincie Noord-Holland |                     |        70 |
+-------------+-------------------------+---------------------+-----------+
| Netherlands | Provincie Noord-Holland | Haarlem             |        70 |
+-------------+-------------------------+---------------------+-----------+
| Netherlands | Provincie Noord-Holland | Hoorn               |        30 |
+-------------+-------------------------+---------------------+-----------+

我一直在尝试创建一个函数来应用于max_speed==np.NaN的每一行，检索它的父行（在确定缺少的值是否适用于某个地区或城市之后），然后返回它的max_speed值，但是，除了在这方面不是很成功之外，我甚至不确定这是最明智的方法

有什么想法吗

Answer 1

这是对我的审判。 有文档记录和一些print（）用于调试。 query（）语句基于pandas/NumPy使用np.nan！=np.nan，并且对待任何人都不像np.nan。 请参阅本页上的注释/警告之一https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html

import pandas  as pd
data = {'country': ['France', 'France', 'France', 'France', 'France', 'France', 'France', 'Germany', 'Germany', 'Germany', 'Netherlands', 'Netherlands', 'Netherlands', 'Netherlands', 'Netherlands', 'Netherlands'],
'region': [None, 'Bretagne', 'Bretagne', 'Bretagne', 'Île-de-France', 'Île-de-France', 'Île-de-France', None, 'Bayern', 'Bayern', None, 'Provincie Gelderland', 'Provincie Gelderland', 'Provincie Noord-Holland', 'Provincie Noord-Holland', 'Provincie Noord-Holland'],
'city': [None, None, 'Saint-Grégoire', 'Saint-Malo', None, 'Saint-Cloud', 'Vélizy-Villacoublay', None, None, 'Nürnberg', None, None, 'Harderwijk', None, 'Haarlem', 'Hoorn'],
'max_speed': [50, 70, None, 30, None, None, 50, 70, None, None, 90, None, None, 70, None, 30]}

df = pd.DataFrame(data)

#1)split the initial df in multiple dfs, using df.query():
# - countries - we assume that all of them have max_speed
# - regions - two categories
#   - max speed set
#   - max speed unset
# - cities - to categories 
#   - max speed set
#   - max speed unset
#
#2) use merge/join to update the max speed for the categories with max speed unset
#
#3) use append to cncatenate all sets, this is final result 
# replaced None/nan wit empty string for nice printing

# those will have speed set
# city compare is superflue, but for consistency
df_countries_only = df.query("(region != region) and (city != city) ")
print(df_countries_only)

# fix the regions
df_regions_to_fix = df.query("(city != city) and (max_speed != max_speed) and (region == region)")
df_regions_ok = df.query("(city != city) and (max_speed == max_speed) and (region == region)")

df_regions_speed = pd.merge(df_countries_only.drop(['region', 'city'], axis=1), 
        df_regions_to_fix.drop(['max_speed'], axis=1), how="inner", on=["country"])
df_regions_speed = df_regions_speed.append(df_regions_ok)
print(df_regions_speed)

df_cities_to_fix = df.query("(city == city) and (max_speed != max_speed)")
df_cities_ok = df.query("(city == city) and (max_speed == max_speed)")

df_cities_speed = pd.merge(df_regions_speed.drop(['city'], axis=1), 
        df_cities_to_fix.drop(['max_speed'], axis=1), how="inner", on=["country", "region"])

print(df_cities_speed)

# now rebuild final df
df_all_data = df_cities_speed.append(df_cities_ok).append(df_regions_speed).append(df_countries_only)
print("\n\n")
print(df_all_data.sort_values(by=['country', 'region', 'city']).fillna("")[['country', 'region', 'city', 'max_speed']])