我有兩個熊貓數據框。我想將所有行保留在df2等于Typein TypeANDdf1之間Date（Date- df11 天或 + 1 天）。我怎樣才能做到這一點？df1 IBSN Type Date0 1 X 2014-08-171 1 Y 2019-09-22df2 IBSN Type Date0 2 X 2014-08-161 2 D 2019-09-222 9 X 2014-08-183 3 H 2019-09-224 3 Y 2019-09-235 5 G 2019-09-22資源 IBSN Type Date0 2 X 2014-08-16 <-- keep because Type = df1[0]['Type'] AND Date = df1[0]['Date'] - 11 9 X 2014-08-18 <-- keep because Type = df1[0]['Type'] AND Date = df1[0]['Date'] + 12 3 Y 2019-09-23 <-- keep because Type = df1[1]['Type'] AND Date = df1[1]['Date'] + 1
2 回答
月關寶盒
TA貢獻1772條經驗 獲得超5個贊
這應該這樣做：
import pandas as pd
from datetime import timedelta
# create dummy data
df1 = pd.DataFrame([[1, 'X', '2014-08-17'], [1, 'Y', '2019-09-22']], columns=['IBSN', 'Type', 'Date'])
df1['Date'] = pd.to_datetime(df1['Date']) # might not be necessary if your Date column already contain datetime objects
df2 = pd.DataFrame([[2, 'X', '2014-08-16'], [2, 'D', '2019-09-22'], [9, 'X', '2014-08-18'], [3, 'H', '2019-09-22'], [3, 'Y', '2014-09-23'], [5, 'G', '2019-09-22']], columns=['IBSN', 'Type', 'Date'])
df2['Date'] = pd.to_datetime(df2['Date']) # might not be necessary if your Date column already contain datetime objects
# add date boundaries to the first dataframe
df1['Date_from'] = df1['Date'].apply(lambda x: x - timedelta(days=1))
df1['Date_to'] = df1['Date'].apply(lambda x: x + timedelta(days=1))
# merge the date boundaries to df2 on 'Type'. Filter rows where date is between
# data_from and date_to (inclusive). Drop 'date_from' and 'date_to' columns
df2 = df2.merge(df1.loc[:, ['Type', 'Date_from', 'Date_to']], on='Type', how='left')
df2[(df2['Date'] >= df2['Date_from']) & (df2['Date'] <= df2['Date_to'])].\
drop(['Date_from', 'Date_to'], axis=1)
請注意，根據您的邏輯，df2（3 Y 2014-09-23）中的第 4 行不應保留，因為其日期（2014）不在 df1（2019 年）的給定日期之間。
白豬掌柜的
TA貢獻1893條經驗 獲得超10個贊
假設Date兩個數據框中的列已經在 dtype 中datetime。我會構造IntervalIndex分配給df1. to的Map列Type。最后檢查相等性以創建要切片的掩碼df1df2
iix = pd.IntervalIndex.from_arrays(df1.Date + pd.Timedelta(days=-1),
df1.Date + pd.Timedelta(days=1), closed='both')
df1 = df1.set_index(iix)
s = df2['Date'].map(df1.Type)
df_final = df2[df2.Type == s]
Out[1131]:
IBSN Type Date
0 2 X 2014-08-16
2 9 X 2014-08-18
4 3 Y 2019-09-23
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