我有一个包含四个数据框的列表，并希望根据公共列('id'(将它们全部合并到一个表中，然后计算这些合并的行。我想我可以通过使用reduce()和lambda表达式来做到这一点，如下所示：
number_of_rows = reduce(lambda a, b: a['id'].isin(b['id']).value_counts(), list_of_dataframes)
但这当然不起作用。我的问题是我不知道如何编写代码，将第一对 value_counts(( 函数的公共行数添加到下一对。提前感谢！
数据帧列表：
list_of_df = [df_hops, df_method_mash_temp, df_ingredients_malt, whole_table]
每个数据帧：
df_hops：
| | hops.name | hops.add | hops.attribute | hops.amount.value | hops.amount.unit | id | name |
+----+-----------------+-----------+-----------------+--------------------+-------------------+-----+-------------------------------------+
| 0 | Fuggles | start | bitter | 25.00 | grams | 1 | Buzz |
| 1 | First Gold | start | bitter | 25.00 | grams | 1 | Buzz |
| 2 | Fuggles | middle | flavour | 37.50 | grams | 1 | Buzz |
| 3 | First Gold | middle | flavour | 37.50 | grams | 1 | Buzz |
| 4 | Cascade | end | flavour | 37.50 | grams | 1 | Buzz |
| 5 | Amarillo | start | bitter | 13.80 | grams | 2 | Trashy Blonde |
| 6 | Simcoe | start | bitter | 13.80 | grams | 2 | Trashy Blonde |
| 7 | Amarillo | end | flavour | 26.30 | grams | 2 | Trashy Blonde |
| 8 | Motueka | end | flavour | 18.80 | grams | 2 | Trashy Blonde |
| 9 | Bramling Cross | middle | bitter | 10.00 | grams | 3 | Berliner Weisse With Yuzu - B-Sides |
+----+-----------------+-----------+-----------------+--------------------+-------------------+-----+-------------------------------------+
df_method_mash_temp：
| | method.mash_temp.duration | method.mash_temp.temp.value | method.mash_temp.temp.unit | id | name |
+----+----------------------------+------------------------------+-----------------------------+-----+-------------------------------------+
| 0 | 75.0 | 64.0 | celsius | 1 | Buzz |
| 1 | NaN | 69.0 | celsius | 2 | Trashy Blonde |
| 2 | 10.0 | 60.0 | celsius | 3 | Berliner Weisse With Yuzu - B-Sides |
| 3 | 30.0 | 65.0 | celsius | 3 | Berliner Weisse With Yuzu - B-Sides |
+----+----------------------------+------------------------------+-----------------------------+-----+-------------------------------------+
df_ingredients_malt：
| | ingredients.malt.name | ingredients.malt.amount.value | ingredients.malt.amount.unit | id | name |
+----+-------------------------+--------------------------------+-------------------------------+-----+-------------------------------------+
| 0 | Maris Otter Extra Pale | 3.30000 | kilograms | 1 | Buzz |
| 1 | Caramalt | 0.20000 | kilograms | 1 | Buzz |
| 2 | Munich | 0.40000 | kilograms | 1 | Buzz |
| 3 | Maris Otter Extra Pale | 3.25000 | kilograms | 2 | Trashy Blonde |
| 4 | Caramalt | 0.20000 | kilograms | 2 | Trashy Blonde |
| 5 | Munich | 0.40000 | kilograms | 2 | Trashy Blonde |
| 6 | Propino Pale Malt | 1.63000 | kilograms | 3 | Berliner Weisse With Yuzu - B-Sides |
+----+-------------------------+--------------------------------+-------------------------------+-----+-------------------------------------+
whole_table(这是上述其他三个来自的主表(：
| | id | name | tagline | first_brewed | description | image_url | abv | ibu | target_fg | target_og | ebc | srm | ph | attenuation_level | food_pairing | brewers_tips | contributed_by | volume.value | volume.unit | boil_volume.value | boil_volume.unit | method.mash_temp | method.fermentation.temp.value | method.fermentation.temp.unit | method.twist | ingredients.malt | ingredients.hops | ingredients.yeast |
+----+-----+--------------------------------------+-----------------------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------+-------+-------+------------+------------+-------+--------+------+--------------------+----------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+---------------+--------------+--------------------+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+--------------------------------+----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------+
| 0 | 1 | Buzz | A Real Bitter Experience. | 09/2007 | A light, crisp and bitter IPA brewed with English and American hops. A small batch brewed only once. | https://images.punkapi.com/v2/keg.png | 4.50 | 60.0 | 1010.0 | 1044.0 | 20.0 | 10.00 | 4.4 | 75.00 | [Spicy chicken tikka masala, Grilled chicken quesadilla, Caramel toffee cake] | The earthy and floral aromas from the hops can be overpowering. Drop a little Cascade in at the end of the boil to lift the profile with a bit of citrus. | Sam Mason <samjbmason> | 20 | litres | 25 | litres | [{'temp': {'value': 64, 'unit': 'celsius'}, 'duration': 75}] | 19.0 | celsius | None | [{'name': 'Maris Otter Extra Pale', 'amount': {'value': 3.3, 'unit': 'kilograms'}}, {'name': 'Caramalt', 'amount': {'value': 0.2, 'unit': 'kilograms'}}, {'name': 'Munich', 'amount': {'value': 0.4, 'unit': 'kilograms'}}] | [{'name': 'Fuggles', 'amount': {'value': 25, 'unit': 'grams'}, 'add': 'start', 'attribute': 'bitter'}, {'name': 'First Gold', 'amount': {'value': 25, 'unit': 'grams'}, 'add': 'start', 'attribute': 'bitter'}, {'name': 'Fuggles', 'amount': {'value': 37.5, 'unit': 'grams'}, 'add': 'middle', 'attribute': 'flavour'}, {'name': 'First Gold', 'amount': {'value': 37.5, 'unit': 'grams'}, 'add': 'middle', 'attribute': 'flavour'}, {'name': 'Cascade', 'amount': {'value': 37.5, 'unit': 'grams'}, 'add': 'end', 'attribute': 'flavour'}] | Wyeast 1056 - American Ale™ |
| 1 | 2 | Trashy Blonde | You Know You Shouldn't | 04/2008 | A titillating, neurotic, peroxide punk of a Pale Ale. Combining attitude, style, substance, and a little bit of low self esteem for good measure; what would your mother say? The seductive lure of the sassy passion fruit hop proves too much to resist. All that is even before we get onto the fact that there are no additives, preservatives, pasteurization or strings attached. All wrapped up with the customary BrewDog bite and imaginative twist. | https://images.punkapi.com/v2/2.png | 4.10 | 41.5 | 1010.0 | 1041.7 | 15.0 | 15.00 | 4.4 | 76.00 | [Fresh crab with lemon, Garlic butter dipping sauce, Goats cheese salad, Creamy lemon bar doused in powdered sugar] | Be careful not to collect too much wort from the mash. Once the sugars are all washed out there are some very unpleasant grainy tasting compounds that can be extracted into the wort. | Sam Mason <samjbmason> | 20 | litres | 25 | litres | [{'temp': {'value': 69, 'unit': 'celsius'}, 'duration': None}] | 18.0 | celsius | None | [{'name': 'Maris Otter Extra Pale', 'amount': {'value': 3.25, 'unit': 'kilograms'}}, {'name': 'Caramalt', 'amount': {'value': 0.2, 'unit': 'kilograms'}}, {'name': 'Munich', 'amount': {'value': 0.4, 'unit': 'kilograms'}}] | [{'name': 'Amarillo', 'amount': {'value': 13.8, 'unit': 'grams'}, 'add': 'start', 'attribute': 'bitter'}, {'name': 'Simcoe', 'amount': {'value': 13.8, 'unit': 'grams'}, 'add': 'start', 'attribute': 'bitter'}, {'name': 'Amarillo', 'amount': {'value': 26.3, 'unit': 'grams'}, 'add': 'end', 'attribute': 'flavour'}, {'name': 'Motueka', 'amount': {'value': 18.8, 'unit': 'grams'}, 'add': 'end', 'attribute': 'flavour'}] | Wyeast 1056 - American Ale™ |
| 2 | 3 | Berliner Weisse With Yuzu - B-Sides | Japanese Citrus Berliner Weisse. | 11/2015 | Japanese citrus fruit intensifies the sour nature of this German classic. | https://images.punkapi.com/v2/keg.png | 4.20 | 8.0 | 1007.0 | 1040.0 | 8.0 | 4.00 | 3.2 | 83.00 | [Smoked chicken wings, Miso ramen, Yuzu cheesecake] | Clean everything twice. All you want is the clean sourness of lactobacillus. | Sam Mason <samjbmason> | 20 | litres | 25 | litres | [{'temp': {'value': 60, 'unit': 'celsius'}, 'duration': 10}, {'temp': {'value': 65, 'unit': 'celsius'}, 'duration': 30}, {'temp': {'value': 72, 'unit': 'celsius'}, 'duration': 10}, {'temp': {'value': 78, 'unit': 'celsius'}, 'duration': 5}] | 21.0 | celsius | Soured naturally using the kettle souring technique, Yuzu fruit: 50g at middle, Yuzu juice: 200ml at FV | [{'name': 'Propino Pale Malt', 'amount': {'value': 1.63, 'unit': 'kilograms'}}, {'name': 'Wheat Malt', 'amount': {'value': 1.63, 'unit': 'kilograms'}}, {'name': 'Propino Pale Malt for kettle souring', 'amount': {'value': 0.03, 'unit': 'kilograms'}}, {'name': 'Acidulated Malt for kettle souring', 'amount': {'value': 0.03, 'unit': 'kilograms'}}] | [{'name': 'Bramling Cross', 'amount': {'value': 10, 'unit': 'grams'}, 'add': 'middle', 'attribute': 'bitter'}] | Wyeast 1056 - American Ale™ |
+----+-----+--------------------------------------+-----------------------------------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------+-------+-------+------------+------------+-------+--------+------+--------------------+----------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+---------------+--------------+--------------------+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------+--------------------------------+----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------+
@Anshul：
list_of_df = [df_hops,df_method_mash_temp,df_ingredients_malt,whole_table]
merged_count = 0
def merge_dfs(df_a, df_b):
df = df_a.merge(df_b, on=['id', 'name'], how='outer')
global merged_count
merged_count += df_a.shape[0] + df_b.shape[0] - df.shape[0]
return df
# use reduce function to get the final merged dataframe
df_final = reduce(merge_dfs, list_of_df)
print(merged_count)
我的其余代码是来自 API 的请求和简单的规范化，以便创建您在上面看到的表。我认为没有必要在这个问题的上下文中粘贴到这里，但如果你想让我在这里发布它，只需写，我会这样做。
我很好奇，想试试这个。我创建了自己的一组示例数据帧，使用"reduce"实现了合并数据帧，使用"全局"变量获取合并的行总数。这就是我所做的，它对我有用：
import pandas as pd
from functools import reduce
data1 = {
"Id": [1,2,3,4,5],
"Data": ['a', 'b', 'c', 'd', 'e']
}
data2 = {
"Id": [2,3,4,5,6],
"Data": ['b', 'c', 'd', 'e', 'f']
}
data3 = {
"Id": [3,4,5,6,7],
"Data": ['c', 'd', 'e', 'f', 'g']
}
data4 = {
"Id": [4,5,6,7,8],
"Data": ['d', 'e', 'f', 'g', 'h']
}
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
df3 = pd.DataFrame(data3)
df4 = pd.DataFrame(data4)
数据帧：
DF1：
Data Id
0 a 1
1 b 2
2 c 3
3 d 4
4 e 5
DF2：
Data Id
0 b 2
1 c 3
2 d 4
3 e 5
4 f 6
DF3：
Data Id
0 c 3
1 d 4
2 e 5
3 f 6
4 g 7
DF4：
Data Id
0 d 4
1 e 5
2 f 6
3 g 7
4 h 8
.
# define a counter variable to be used as a global
merged_count = 0
# define the function to be called via reduce
def merge_dfs(df_a, df_b):
df = df_a.merge(df_b, on=['Id', 'Data'], how='outer')
global merged_count
merged_count += df_a.shape[0] + df_b.shape[0] - df.shape[0]
return df
# use reduce function to get the final merged dataframe
df_final = reduce(merge_dfs, [df1, df2, df3, df4])
print(df_final)
print(merged_count)
df_final：
Data Id
0 a 1
1 b 2
2 c 3
3 d 4
4 e 5
5 f 6
6 g 7
7 h 8
合并的行数：
12
如果这对你有帮助，你可以尝试在你的方案中实现它。 如果有更好的方法来实现这一点，如果需要，我真的很想知道并改进此解决方案。