基于最接近的日期时间
合并两个熊猫数据帧
原文:https://www . geesforgeks . org/merge-two-pandas-data frames-based-on-close-datetime/
在本文中，我们将讨论如何基于最近的日期时间合并熊猫数据帧。要了解如何合并数据帧，首先你必须了解如何创建一个数据帧，你必须参考文章 创建熊猫数据帧 。创建数据帧后，需要合并它们，为了合并数据帧，有一个名为 merge_asof() 的函数，当写这个时，它可以写成:
pandas.merge_asof(左，右，on=无，左 _ on =无，右 _ on =无，左 _ index =假，右 _ index =假，by =无，左 _ by =无，右 _ by =无，后缀=('_x '，' _y ')，容差=无，允许 _ 精确 _ 匹配=真，方向= '向后')
注:
- 要了解更多关于此功能的信息，请参考 Python 中的文章pandas . merge _ asof()函数
- 数据帧必须按关键字排序。
逐步方法
*第一步:*导入熊猫库
为了完成这个任务，我们必须导入名为熊猫的库。
import pandas as pd
*步骤 2:* 创建数据框
在这一步中，我们必须使用函数“pd”创建数据帧。DataFrame()”。在本例中，我们创建了两个数据框，一个命名为左，另一个命名为右，因为我们的最后一个目标是基于最接近的日期时间合并两个数据框。它可以写成:
左= pd。data frame({ 0
"时间":[pd。时间戳(“2020-03-25 13:30:00.023”)，
警局。时间戳(“2020-03-25 13:30:00.023”)，
警局。时间戳(“2020-03-25 13:30:00.030”)，
**警局。时间戳(“2020-03-25 13:30:00.041”)，
警局。时间戳(“2020-03-25 13:30:00.048”)，
警局。时间戳(“2020-03-25 13:30:00.049”)，
警局。时间戳(“2020-03-25 13:30:00.072”)，
警局。时间戳(“2020-03-25 13:30:00.075”)
],
“ticker”:[“GOOG”、“MSFT”、“MSFT”、“MSFT”、“GOOG”、“AAPL”、“GOOG”、“MSFT”]，
“投标”:[720.50、51.95、51.97、51.99、720.50、97.99、720.50、52.01]，
“询问”:[720.93、51.96、51.98、52.00、720.93、98.01、720.88、52.03]
})
右= pd。data frame({ 0
“时间”:[
警局。时间戳(“2020-03-25 13:30:00.023”)，
警局。时间戳(“2020-03-25 13:30:00.038”)，
警局。时间戳(“2020-03-25 13:30:00.048”)，
警局。时间戳(“2020-03-25 13:30:00.048”)，
警局。时间戳(“2020-03-25 13:30:00.048”)
],
股票代码:[“MSFT”、“MSFT”、“谷歌”、“谷歌”、“AAPL”]，
“价格”:[51.95，51.95，720.77，720.92，98.0]，
【数量】:75，155，100，100，100
})**
*步骤 3:* 合并数据帧并打印它们
在这一步中，将使用函数“pd.merge_asof()”合并数据帧。merge_asof()函数的结果存储在一个变量中，然后使用“print()”打印该变量。
Python 3
# Importing the required package
import pandas as pd
# Creating the DataFrame of left side
left = pd.DataFrame({
"time": [pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.030"),
pd.Timestamp("2020-03-25 13:30:00.041"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.049"),
pd.Timestamp("2020-03-25 13:30:00.072"),
pd.Timestamp("2020-03-25 13:30:00.075")
],
"ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG",
"AAPL", "GOOG", "MSFT"],
"bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99,
720.50, 52.01],
"ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01,
720.88, 52.03]
})
# Creating the Dataframe of right side
right = pd.DataFrame({
"time": [
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.038"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048")
],
"ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
"price": [51.95, 51.95, 720.77, 720.92, 98.0],
"quantity": [75, 155, 100, 100, 100]
})
# Applying merge_asof on data and store it
# in a variable
merged_dataframe = pd.merge_asof(right, left, on="time",
by="ticker")
# print the variable
print(merged_dataframe)
*输出:*
*示例 1:* 现在我们在 merge_asof 函数中更改左右数据框的位置。
Python 3
# Importing the required package
import pandas as pd
# Creating the DataFrame of left side
left = pd.DataFrame({
"time": [pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.030"),
pd.Timestamp("2020-03-25 13:30:00.041"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.049"),
pd.Timestamp("2020-03-25 13:30:00.072"),
pd.Timestamp("2020-03-25 13:30:00.075")
],
"ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG",
"AAPL", "GOOG", "MSFT"],
"bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99,
720.50, 52.01],
"ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01,
720.88, 52.03]
})
# Creating the Dataframe of right side
right = pd.DataFrame({
"time": [
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.038"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048")
],
"ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
"price": [51.95, 51.95, 720.77, 720.92, 98.0],
"quantity": [75, 155, 100, 100, 100]
})
# Applying merge_asof on data and store it
# in a variable
merged_dataframe = pd.merge_asof(left, right, on="time",
by="ticker")
# print the variable
print(merged_dataframe)
*输出:*
*注意:*因此，从我们的两个输出中可以清楚地看到，当我们将右侧数据框放在第一个位置时，输出中的行数等于右侧数据框中的行数，当左侧数据框放在第一个位置时，输出中的行数等于左侧数据框中的行数。如果我们查看两个输出并比较它们，那么我们可以很容易地说 merge_asof()类似于左连接，只是我们匹配最近的键而不是相等的键。
*例 2:* 我们只在报价时间和交易时间之间的 2 毫秒内报价。
Python 3
# Importing the required package
import pandas as pd
# Creating the DataFrame of left side
left = pd.DataFrame({
"time": [pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.030"),
pd.Timestamp("2020-03-25 13:30:00.041"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.049"),
pd.Timestamp("2020-03-25 13:30:00.072"),
pd.Timestamp("2020-03-25 13:30:00.075")
],
"ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG",
"AAPL", "GOOG", "MSFT"],
"bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99,
720.50, 52.01],
"ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01,
720.88, 52.03]
})
# Creating the Dataframe of right side
right = pd.DataFrame({
"time": [
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.038"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048")
],
"ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
"price": [51.95, 51.95, 720.77, 720.92, 98.0],
"quantity": [75, 155, 100, 100, 100]
})
# Applying merge_asof on data and store it
# in a variable
merged_dataframe = pd.merge_asof(left, right, on="time", by="ticker",
tolerance=pd.Timedelta("2ms"))
# print the variable
print(merged_dataframe)
*输出:*
*例 3:* 我们只计算报价时间和交易时间之间 10 毫秒内的时间，不包括精确匹配的时间。然而，先前的数据将向前传播。
Python 3
# Importing the required package
import pandas as pd
# Creating the DataFrame of left side
left = pd.DataFrame({
"time": [pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.030"),
pd.Timestamp("2020-03-25 13:30:00.041"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.049"),
pd.Timestamp("2020-03-25 13:30:00.072"),
pd.Timestamp("2020-03-25 13:30:00.075")
],
"ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG",
"AAPL", "GOOG", "MSFT"],
"bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99,
720.50, 52.01],
"ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01,
720.88, 52.03]
})
# Creating the Dataframe of right side
right = pd.DataFrame({
"time": [
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.038"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048")
],
"ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
"price": [51.95, 51.95, 720.77, 720.92, 98.0],
"quantity": [75, 155, 100, 100, 100]
})
# Applying merge_asof on data and store it
# in a variable
merged_dataframe = pd.merge_asof(left, right, on="time", by="ticker",
tolerance=pd.Timedelta("2ms"),
allow_exact_matches=False)
# print the variable
print(merged_dataframe)
*输出:*
*示例 4:* 当在两个地方使用相同的数据帧时。在这个左侧数据框中，两边都使用。
Python 3
# Importing the required package
import pandas as pd
# Creating the DataFrame of left side
left = pd.DataFrame({
"time": [pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.030"),
pd.Timestamp("2020-03-25 13:30:00.041"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.049"),
pd.Timestamp("2020-03-25 13:30:00.072"),
pd.Timestamp("2020-03-25 13:30:00.075")
],
"ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG",
"AAPL", "GOOG", "MSFT"],
"bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99,
720.50, 52.01],
"ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01,
720.88, 52.03]
})
# Creating the Dataframe of right side
right = pd.DataFrame({
"time": [
pd.Timestamp("2020-03-25 13:30:00.023"),
pd.Timestamp("2020-03-25 13:30:00.038"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048"),
pd.Timestamp("2020-03-25 13:30:00.048")
],
"ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
"price": [51.95, 51.95, 720.77, 720.92, 98.0],
"quantity": [75, 155, 100, 100, 100]
})
# Applying merge_asof on data and store it
# in a variable
merged_dataframe = pd.merge_asof(left, left, on="time",
by="ticker")
# print the variable
print(merged_dataframe)
*输出:*
它将相同的数据帧创建为两个帧，一个表示为 x，另一个表示为 y，即 bid_x、bid_y、ask_x、ask_y。
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