首頁猿問 Pandas：按復(fù)雜條件合并組內(nèi)的兩行

Pandas：按復(fù)雜條件合并組內(nèi)的兩行

Python

幕布斯7119047 2023-07-05 11:06:52

我有一個 df 如下；將 pandas 導(dǎo)入為 pddf = pd.DataFrame({ "ID": ['company A', 'company A', 'company A', 'company B','company B', 'company B', 'company C', 'company C','company C','company C', 'company D', 'company D','company D'], 'Sender': [28, 'delete', 'flag_source', 56, 28, 312, 'delete', 'flag_source', 78, 102, 26, 101, 96], 'Receiver': [129, 28, 'delete', 172, 56, 28, 61, 'delete', 12, 78, 98, 26, 101], 'Date': ['2020-04-12', '2020-03-20', '2020-03-20', '2019-02-11', '2019-01-31', '2018-04-02', '2020-06-29', '2020-06-29', '2019-11-29', '2019-10-01', '2020-04-03', '2020-01-30', '2019-10-18'], 'Sender_type': ['house', 'temp', 'house', 'house', 'house', 'house', 'temp', 'house', 'house','house','house', 'temp', 'house'], 'Receiver_type': ['house', 'house', 'temp', 'house','house','house','house', 'temp', 'house','house','house','house','temp'], 'Price': [32, 50, 47, 21, 23, 19, 52, 39, 12, 22, 61, 53, 19]})它是這樣的： ID Sender Receiver Date Sender_type Receiver_type Price 0 company A 28 129 2020-04-12 house house 32 1 company A delete 28 2020-03-20 temp house 50 # combine this row with below2 company A flag_source delete 2020-03-20 house temp 47 # combine this row with above3 company B 56 172 2019-02-11 house house 21 4 company B 28 56 2019-01-31 house house 23 5 company B 312 28 2018-04-02 house house 19 我希望通過以下規(guī)則合并/合并每個組“ID”（公司 x）的兩行：將“Sender”中包含“flag_source”的行及其上面的行合并為一個新行。在這個新行中：Sender 是 flag_source，'Revceiver' 是其上面的值（刪除兩個 'delete' 值），Date 是上面的日期，Sender_type 和 Receiver_type 是 'house'，'Price' 是上面的上一個值價值。然后刪除兩行。例如，對于 A 公司，它將合并第 1 行和第 2 行以生成以下新行：ID Sender Receiver Date Sender_type Receiver_type Pricecompany A flag_source 28 2020-03-20 house house 50

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3 回答

躍然一笑

TA貢獻1826條經(jīng)驗獲得超6個贊

import pandas as pd

df = pd.DataFrame({

"ID": ['company A', 'company A', 'company A', 'company B','company B', 'company B', 'company C', 'company C','company C','company C', 'company D', 'company D','company D'],

'Sender': [28, 'delete', 'flag_source', 56, 28, 312, 'delete', 'flag_source', 78, 102, 26, 101, 96],

'Receiver': [129, 28, 'delete', 172, 56, 28, 61, 'delete', 12, 78, 98, 26, 101],

'Date': ['2020-04-12', '2020-03-20', '2020-03-20', '2019-02-11', '2019-01-31', '2018-04-02', '2020-06-29', '2020-06-29', '2019-11-29', '2019-10-01', '2020-04-03', '2020-01-30', '2019-10-18'],

'Sender_type': ['house', 'temp', 'house', 'house', 'house', 'house', 'temp', 'house', 'house','house','house', 'temp', 'house'],

'Receiver_type': ['house', 'house', 'temp', 'house','house','house','house', 'temp', 'house','house','house','house','temp'],

'Price': [32, 50, 47, 21, 23, 19, 52, 39, 12, 22, 61, 53, 19]

})

flaggedData = (df[df["Sender"] == "flag_source"])

for i,row in flaggedData.iterrows(): # Row variable contains row having sender as flag_source

deleteRow = df[df.index == i-1].values[0] # delete variable contains row having sender as delete

combined = [row[0], # ID

row[1], # Sender

deleteRow[2], # Receiver

deleteRow[3], # Date

row[4], # Sender_type

deleteRow[5], # Receiver_type

deleteRow[6]] # Price

df.loc[i-1] = combined # replace with new values

df = df.drop(index=i) # drop old values

df = df.reset_index() # resent index for better access on future.

print(df.loc[1])

我假設(shè)每個“刪除”行都位于“flag_source”行上方。如果你還是不明白，請閱讀評論，評論你的疑問。

反對回復(fù) 2023-07-05

精慕HU

TA貢獻1845條經(jīng)驗獲得超8個贊

如果delete/flag_source始終位于同一日期，并且該日期+ ID上沒有其他行，則可以對ID和日期使用groupby聚合函數(shù)以避免使用長循環(huán)。如果您的數(shù)據(jù)順序不正確，您始終可以sort_values提前進行更改。

cols = df.columns

new_df = df.groupby(['ID', 'Date']).aggregate({

'Sender': 'last',

'Receiver': 'first',

'Sender_type': 'last',

'Receiver_type': 'first',

'Price': 'first'

}).reset_index()

# Reorder as per original data

new_df[cols].sort_values(['ID', 'Date'], ascending=[1, 0])

反對回復(fù) 2023-07-05

慕容3067478

TA貢獻1773條經(jīng)驗獲得超3個贊

看來您只需要刪除每對的第二行并替換其余行中的一些值。

df = df[dd.Receiver == 'delete']

df.Sender = df.Sender.str.replace('delete', 'flag_source')

df.Sender_type = df.Sender_type.str.replace('temp', 'house')

反對回復(fù) 2023-07-05

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Pandas：按復(fù)雜條件合并組內(nèi)的兩行

Pandas：按復(fù)雜條件合并組內(nèi)的兩行

3 回答

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