我有一個資料框，其中有一列包含字串串列。
id sentence category
0 "I love basketball and dunk to the basket" ['basketball']
1 "I am playing football and basketball tomorrow " ['football', 'basketball']
我想做兩件事：
-
- 轉換類別列，其中前一個串列中的每個元素都變成一個字串，每個字串都有一行，并且具有相同的 id 和句子
-
- 按類別擁有一個資料框
步驟 1) 的預期輸出：
id sentence category
0 "I love basketball and dunk to the basket" 'basketball'
1 "I am playing football and tomorrow basketball" 'football'
1 "I am playing football and tomorrow basketball" 'basketball'
步驟 2) 的預期輸出：
DF_1
id sentence category
0 "I love basketball and dunk to the basket" 'basketball'
1 "I am playing football and tomorrow basketball" 'basketball'
DF_2
id sentence category
1 "I am playing football and tomorrow basketball" 'football'
我怎樣才能做到這一點 ？對于每個串列并檢查每個串列的 len 都可以作業，但是有沒有更快/更優雅的方法？
uj5u.com熱心網友回復：
你可以explode“分類”；然后groupby：
out = [g for _, g in df.explode('category').groupby('category')]
然后，如果您列印以下專案out：
for i in out:
print(i, end='\n\n')
你會看到的：
id sentence category
0 0 I love basketball and dunk to the basket basketball
1 1 I am playing football and basketball tomorrow basketball
id sentence category
1 1 I am playing football and basketball tomorrow football
uj5u.com熱心網友回復：
您將需要兩個工具：explode和groupby。
首先讓我們準備我們的資料，并確保 explode 將與literal_eval一起使用：
import pandas as pd
from io import StringIO
from ast import literal_eval
csvfile = StringIO(
"""id\tsentence\tcategory
0\t"I love basketball and dunk to the basket"\t["basketball"]
1\t"I am playing football and basketball tomorrow "\t["football", "basketball"]""")
df = pd.read_csv(csvfile, sep = '\t', engine='python')
df.loc[:, 'category'] = df.loc[:, 'category'].apply(literal_eval)
然后爆炸關于您的類別列：
df = df.explode('category')
最后，您可以將groupby其用作字典并將子資料幀存盤在其他地方：
dg = df.groupby('category')
list_dg = []
for n, g in dg:
list_dg.append(g)
dgImo，如果可能的話，我會堅持下去
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標籤：Python python-3.x 熊猫 数据框
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