我在包含字符串的數據框中有兩列。例如，import pandas as pdimport numpy as npdata = [['Oct-2019', 'Oranges + Grapes + Pears', 'Grapes + Pears'], ['Nov-2019', 'Oranges + Grapes + Pears', 'Oranges + Grapes + Pears']]df = pd.DataFrame(data, columns =['Date', 'Previous shopping list', 'Recent shopping list'])print(df)Fish = ['Salmon', 'Trout']Fruit = ['Oranges', 'Grapes', 'Pears'] Date PSL RSL0 Oct-2019 Oranges + Grapes Grapes + Pears + Pears + Salmon 1 Nov-2019 Oranges + Grapes Oranges + Grapes + Pears + Trout + Pears 我想比較兩列中的字符串，并將文本輸出到一個新列，該列說明兩個列表之間發生了什么變化。例如，創建一個列，該列將檢查與“水果”相關的字符串，并輸出與上一個列表之前的購物列表相比，從最近的購物中刪除了哪些水果。請參閱下面的所需輸出： Date PSL RSL Fruit lost Fish Lost0 Oct-2019 Oranges + Grapes Grapes + Pears Oranges Salmon + Pears + Salmon 1 Nov-2019 Oranges + Grapes Oranges + Grapes Trout + Pears + Trout + Pears 在使用熊貓時我怎么能做到這一點！抱歉，如果這是第一次不清楚！感謝您的任何建議/幫助！
3 回答
嗶嗶one
TA貢獻1854條經驗 獲得超8個贊
檢查“最近的購物清單”中是否存在字符串“Oranges”并根據結果創建一個新列“Oranges Lost”：
df['Oranges Lost'] = np.where(df['Recent shopping list'].str.contains('Oranges'), 'No Change', 'Lost')```
揚帆大魚
TA貢獻1799條經驗 獲得超9個贊
用于處理數據的確切函數取決于每個組合所需的確切輸出。希望下面會給您足夠的信息來為您的問題創建解決方案：
# process data so each row contains a list of elements
df['PSL_processed'] = df['Previous shopping list'].str.split('+')
df['RSL_processed'] = df['Recent shopping list'].str.split('+')
def compare_items(x):
if set(x.PSL_processed) == set(x.RSL_processed):
return 'No change'
elif set(x.PSL_processed) - set(x.CSL_processed) > 0:
return 'Lost'
# add in conditional logic here, to meet specification
df.apply(compare_items, axis=1)
的官方文檔pd.apply()寫得很好。
LEATH
TA貢獻1936條經驗 獲得超7個贊
所以馬克的解決方案很好地抓住了列表之間的差異
# process data so each row contains a list of elements
df['PSL_processed'] = df['Previous shopping list'].str.split()
df['RSL_processed'] = df['Recent shopping list'].str.split()
def compare_items(x):
return set(x.PSL_processed) - set(x.RSL_processed)
# add in conditional logic here, to meet specification
df['Products_lost'] = df.apply(compare_items, axis=1)
print(df)
除此之外，為了找到=水果和產品=魚的產品，我使用了以下內容：
for idx, row in df.iterrows():
for c in Fruit:
if c in row['Products_lost']:
df.ix[idx, 'Fruit lost'] = c
for c in Fish:
if c in row['Products_lost']:
df.ix[idx, 'Fish lost'] = c
似乎運作良好！
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