我有兩個不同的 csv，我在兩個數據幀中讀取。我想將列 df1['building_type] 與 df2['model'] 匹配，并將相應的行附加到 df1。數據框 1：data = [{'length': '34', 'width': '58.5', 'height': '60.2', 'building_type': ['concrete','wood','steel','laminate']}, {'length': '42', 'width': '33', 'height': '23', 'building_type': ['concrete_double','wood_double','steel_double']}]df1 = pd.DataFrame(data)print(df1)數據框 2：data2 = [{'type': 'A1', 'floor': '2', 'model': ['wood','laminate','concrete','steel']}, {'type': 'B3', 'floor': '4', 'model': ['wood_double','concrete_double','steel_double']}]df2=pd.DataFrame(data2)print(df2)最終數據框： length width height building_type type floor0 34 58.5 60.2 [concrete, wood, steel, laminate] A1 21 42 33 23 [concrete_double, wood_double, steel_double] B3 4
1 回答
交互式愛情
TA貢獻1712條經驗 獲得超3個贊
pd.merge似乎是這里必要的工具，但我們需要一個不可變的 dtype。list是可變的，不能加入。我們可以將list(mutable) 轉換為tupleor frozenset，這兩者都是不可變的，可以用來加入。由于示例輸出顯示順序無關緊要，我選擇了frozenset.
這是代碼：
import pandas as pd
data = [{'length': '34', 'width': '58.5', 'height': '60.2', 'building_type': ['concrete','wood','steel','laminate']},
{'length': '42', 'width': '33', 'height': '23', 'building_type': ['concrete_double','wood_double','steel_double']}]
df1 = pd.DataFrame(data)
print(df1)
data2 = [{'type': 'A1', 'floor': '2', 'model': ['wood','laminate','concrete','steel']},
{'type': 'B3', 'floor': '4', 'model': ['wood_double','concrete_double','steel_double']}]
df2=pd.DataFrame(data2)
print(df2)
# Note: Merge fails on mutable dtype
# pd.merge(df1, df2, left_on='building_type', right_on='model')
# Produces `TypeError: unhashable type: 'list'`
# Convert mutable type to immutable type and merge.
# `tuple` is best if order matters for you. I am assuming that the
# order doesn't matter based on the sample output, so `frozenset` is more
# appropriate.
df1['building_type'] = df1['building_type'].apply(frozenset)
df2['model'] = df2['model'].apply(frozenset)
# Now, merge. Note that since column names are different both
# 'building_type' and 'model' would be retained. You can remove one of them.
final_df = pd.merge(df1, df2, left_on='building_type', right_on='model')
final_df = final_df.drop(['model'], axis=1)
print(final_df)
我機器上的輸出：
length width height building_type
0 34 58.5 60.2 [concrete, wood, steel, laminate]
1 42 33 23 [concrete_double, wood_double, steel_double]
type floor model
0 A1 2 [wood, laminate, concrete, steel]
1 B3 4 [wood_double, concrete_double, steel_double]
length width height building_type type floor
0 34 58.5 60.2 (laminate, wood, steel, concrete) A1 2
1 42 33 23 (concrete_double, steel_double, wood_double) B3 4
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