如何根据 Python 中的条件将另一个数据帧的值分配给当前数据帧？
How to assign values from another dataframe to current dataframe based on a condition in Python?
我想根据 DatetimeIndex 条件将一个数据帧的值分配给另一个数据帧。
我有这个数据框：（第一个）
date importance
2006-12-05 10:35:00 HIGH
2006-12-13 02:40:00 LOW
这个数据框：（第二个）
index value
2006-12-05 08:03:01.985 6
2006-12-05 08:11:34.130 7
2006-12-05 08:20:05.959 6
2006-12-05 08:28:38.104 6
2006-12-05 08:37:02.995 6
2006-12-05 08:45:35.140 5
2006-12-05 08:54:06.969 6
2006-12-05 09:02:59.928 6
2006-12-05 09:11:32.072 6
2006-12-05 09:20:03.901 6
2006-12-05 09:28:36.046 5
2006-12-05 09:37:00.937 5
2006-12-05 09:45:33.082 6
2006-12-05 09:54:04.911 6
2006-12-05 10:02:04.889 6
2006-12-05 10:10:37.034 5
2006-12-05 10:19:08.863 6
2006-12-05 10:27:41.008 5
2006-12-05 10:36:04.953 5
2006-12-05 10:44:37.098 5
.
.
.
2006-12-13 02:06:00.898 1
2006-12-13 02:14:33.043 1
2006-12-13 02:23:04.872 1
2006-12-13 02:31:03.904 1
2006-12-13 02:39:36.048 1
2006-12-13 02:48:07.878 2
2006-12-13 02:56:40.022 5
2006-12-13 03:05:04.914 2
2006-12-13 03:13:37.058 3
2006-12-13 03:22:08.888 6
2006-12-13 03:31:03.108 1
2006-12-13 03:39:34.937 1
2006-12-13 03:48:07.081 1
2006-12-13 03:56:38.911 2
2006-12-13 04:05:04.117 3
最终结果应该是这样的：
index value new_value
2006-12-05 08:03:01.985 6
2006-12-05 08:11:34.130 7
2006-12-05 08:20:05.959 6
2006-12-05 08:28:38.104 6
2006-12-05 08:37:02.995 6
2006-12-05 08:45:35.140 5
2006-12-05 08:54:06.969 6
2006-12-05 09:02:59.928 6
2006-12-05 09:11:32.072 6
2006-12-05 09:20:03.901 6
2006-12-05 09:28:36.046 5
2006-12-05 09:37:00.937 5
2006-12-05 09:45:33.082 6
2006-12-05 09:54:04.911 6
2006-12-05 10:02:04.889 6
2006-12-05 10:10:37.034 5
2006-12-05 10:19:08.863 6
2006-12-05 10:27:41.008 5
2006-12-05 10:36:04.953 5 HIGH
2006-12-05 10:44:37.098 5
.
.
.
2006-12-13 02:06:00.898 1
2006-12-13 02:14:33.043 1
2006-12-13 02:23:04.872 1
2006-12-13 02:31:03.904 1
2006-12-13 02:39:36.048 1 LOW
2006-12-13 02:48:07.878 2
2006-12-13 02:56:40.022 5
2006-12-13 03:05:04.914 2
2006-12-13 03:13:37.058 3
2006-12-13 03:22:08.888 6
2006-12-13 03:31:03.108 1
2006-12-13 03:39:34.937 1
2006-12-13 03:48:07.081 1
2006-12-13 03:56:38.911 2
2006-12-13 04:05:04.117 3
我试过这个：
def getNearestDate(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
items = second_df.index
for pivot in first_df.date:
d = getNearestDate(items, pivot)
print(d)
second_df.loc[second_df.index == d, 'new_value'] = first_df.importance
它打印最近的这些日期：
2006-12-05 10:36:04.953000
2006-12-13 02:39:36.048000
所以在这些日子里，它应该把值放在“重要性”上。
此外，在 new_value 列上，所有内容都是 NAN。
你能帮我解决这个问题吗？
你已经使用了loc中的条件
second_df.index == d
并且它 return 在满足条件的索引处为真，而不是索引。
改为使用
second_df[second_df.index == d].index.values
您已经有了 second_df.index == d 想要的面具。这会产生一个 pandas.Series，其中值为 True，值为真，值为 False，值为假。您可以 |= 多个掩码一起获取任何掩码中 True 的所有行。只需将该系列作为 'new_value' 列附加到您的第二个数据框。
mask = False
for pivot in first_df.date:
mask |= second_df.index == getNearestDate(second_df.index, pivot)
second_df['new_value'] = mask
如果你真的想让 'X' 和 '' 成为 True 和 False 的别名，你也可以在添加之前应用一个简单的 lambda 来转换它们系列到数据框。
mask = False
for pivot in first_df.date:
mask |= second_df.index == getNearestDate(second_df.index, pivot)
second_df['new_value'] = mask.apply(lambda x: 'X' if bool(x) else '')
编辑：
如果要获取第一个数据帧的 importance 值，只需使用 getNearestDate 函数确定哪些行需要该值，然后将它们与第二个数据帧合并。
first_df['index'] = first_df.apply(
lambda x: getNearestDate(second_df.index, x.date),
axis = 1,
result_type = 'reduce'
)
second_df = second_df.merge(first_df, how='left', on='index')
您应该可以使用 reindex 和 merge
# note the method and the tolerance. Change them to whatever works best for your actual data
new_df = df2.merge(df.reindex(df2.index, method='nearest', limit=1, tolerance='2T'),
left_index=True, right_index=True)
value importance
index
2006-12-05 08:03:01.985 6 NaN
2006-12-05 08:11:34.130 7 NaN
2006-12-05 08:20:05.959 6 NaN
2006-12-05 08:28:38.104 6 NaN
2006-12-05 08:37:02.995 6 NaN
2006-12-05 08:45:35.140 5 NaN
2006-12-05 08:54:06.969 6 NaN
2006-12-05 09:02:59.928 6 NaN
2006-12-05 09:11:32.072 6 NaN
2006-12-05 09:20:03.901 6 NaN
2006-12-05 09:28:36.046 5 NaN
2006-12-05 09:37:00.937 5 NaN
2006-12-05 09:45:33.082 6 NaN
2006-12-05 09:54:04.911 6 NaN
2006-12-05 10:02:04.889 6 NaN
2006-12-05 10:10:37.034 5 NaN
2006-12-05 10:19:08.863 6 NaN
2006-12-05 10:27:41.008 5 NaN
2006-12-05 10:36:04.953 5 HIGH
2006-12-05 10:44:37.098 5 NaN
2006-12-13 02:06:00.898 1 NaN
2006-12-13 02:14:33.043 1 NaN
2006-12-13 02:23:04.872 1 NaN
2006-12-13 02:31:03.904 1 NaN
2006-12-13 02:39:36.048 1 LOW
2006-12-13 02:48:07.878 2 NaN
2006-12-13 02:56:40.022 5 NaN
2006-12-13 03:05:04.914 2 NaN
2006-12-13 03:13:37.058 3 NaN
2006-12-13 03:22:08.888 6 NaN
2006-12-13 03:31:03.108 1 NaN
2006-12-13 03:39:34.937 1 NaN
2006-12-13 03:48:07.081 1 NaN
2006-12-13 03:56:38.911 2 NaN
2006-12-13 04:05:04.117 3 NaN
只需进行这些小改动，希望它会起作用
loc=[]
def getNearestDate(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
items = second_df.index
for pivot in first_df.date:
d = getNearestDate(items, pivot)
loc.append(second_df.set_index('index').index.get_loc(d))
## Adding Data to your second df
second_df['importance']=[]
for index,locations in enumerate(loc):
df['importance'][int(location)]=first_df['importance'][index]
首先我们必须保留与原始数据框中的日期相对应的日期：
items = second_df.index
dates = []
for pivot in first_df.date:
dates.append(getNearestDate(items, pivot))
first_df['new_date'] = dates
因为我们不再需要它们，我们可以删除整列：
first_df = first_df.drop(columns="date")
为了使合并工作，我们需要在两个数据帧上声明索引。
first_df.set_index("new_date", inplace =True)
合并完成如下：
second_df = second_df.merge(first_df, how = "left",left_index=True, right_index=True)
此外，永远不要让 NaN 出现在数据框中很重要：
second_df.importance = second_df.importance.fillna(0)
我想根据 DatetimeIndex 条件将一个数据帧的值分配给另一个数据帧。
我有这个数据框：（第一个）
date importance
2006-12-05 10:35:00 HIGH
2006-12-13 02:40:00 LOW
这个数据框：（第二个）
index value
2006-12-05 08:03:01.985 6
2006-12-05 08:11:34.130 7
2006-12-05 08:20:05.959 6
2006-12-05 08:28:38.104 6
2006-12-05 08:37:02.995 6
2006-12-05 08:45:35.140 5
2006-12-05 08:54:06.969 6
2006-12-05 09:02:59.928 6
2006-12-05 09:11:32.072 6
2006-12-05 09:20:03.901 6
2006-12-05 09:28:36.046 5
2006-12-05 09:37:00.937 5
2006-12-05 09:45:33.082 6
2006-12-05 09:54:04.911 6
2006-12-05 10:02:04.889 6
2006-12-05 10:10:37.034 5
2006-12-05 10:19:08.863 6
2006-12-05 10:27:41.008 5
2006-12-05 10:36:04.953 5
2006-12-05 10:44:37.098 5
.
.
.
2006-12-13 02:06:00.898 1
2006-12-13 02:14:33.043 1
2006-12-13 02:23:04.872 1
2006-12-13 02:31:03.904 1
2006-12-13 02:39:36.048 1
2006-12-13 02:48:07.878 2
2006-12-13 02:56:40.022 5
2006-12-13 03:05:04.914 2
2006-12-13 03:13:37.058 3
2006-12-13 03:22:08.888 6
2006-12-13 03:31:03.108 1
2006-12-13 03:39:34.937 1
2006-12-13 03:48:07.081 1
2006-12-13 03:56:38.911 2
2006-12-13 04:05:04.117 3
最终结果应该是这样的：
index value new_value
2006-12-05 08:03:01.985 6
2006-12-05 08:11:34.130 7
2006-12-05 08:20:05.959 6
2006-12-05 08:28:38.104 6
2006-12-05 08:37:02.995 6
2006-12-05 08:45:35.140 5
2006-12-05 08:54:06.969 6
2006-12-05 09:02:59.928 6
2006-12-05 09:11:32.072 6
2006-12-05 09:20:03.901 6
2006-12-05 09:28:36.046 5
2006-12-05 09:37:00.937 5
2006-12-05 09:45:33.082 6
2006-12-05 09:54:04.911 6
2006-12-05 10:02:04.889 6
2006-12-05 10:10:37.034 5
2006-12-05 10:19:08.863 6
2006-12-05 10:27:41.008 5
2006-12-05 10:36:04.953 5 HIGH
2006-12-05 10:44:37.098 5
.
.
.
2006-12-13 02:06:00.898 1
2006-12-13 02:14:33.043 1
2006-12-13 02:23:04.872 1
2006-12-13 02:31:03.904 1
2006-12-13 02:39:36.048 1 LOW
2006-12-13 02:48:07.878 2
2006-12-13 02:56:40.022 5
2006-12-13 03:05:04.914 2
2006-12-13 03:13:37.058 3
2006-12-13 03:22:08.888 6
2006-12-13 03:31:03.108 1
2006-12-13 03:39:34.937 1
2006-12-13 03:48:07.081 1
2006-12-13 03:56:38.911 2
2006-12-13 04:05:04.117 3
我试过这个：
def getNearestDate(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
items = second_df.index
for pivot in first_df.date:
d = getNearestDate(items, pivot)
print(d)
second_df.loc[second_df.index == d, 'new_value'] = first_df.importance
它打印最近的这些日期：
2006-12-05 10:36:04.953000
2006-12-13 02:39:36.048000
所以在这些日子里，它应该把值放在“重要性”上。
此外，在 new_value 列上，所有内容都是 NAN。
你能帮我解决这个问题吗？
你已经使用了loc中的条件
second_df.index == d
并且它 return 在满足条件的索引处为真，而不是索引。
改为使用
second_df[second_df.index == d].index.values
您已经有了 second_df.index == d 想要的面具。这会产生一个 pandas.Series，其中值为 True，值为真，值为 False，值为假。您可以 |= 多个掩码一起获取任何掩码中 True 的所有行。只需将该系列作为 'new_value' 列附加到您的第二个数据框。
mask = False
for pivot in first_df.date:
mask |= second_df.index == getNearestDate(second_df.index, pivot)
second_df['new_value'] = mask
如果你真的想让 'X' 和 '' 成为 True 和 False 的别名，你也可以在添加之前应用一个简单的 lambda 来转换它们系列到数据框。
mask = False
for pivot in first_df.date:
mask |= second_df.index == getNearestDate(second_df.index, pivot)
second_df['new_value'] = mask.apply(lambda x: 'X' if bool(x) else '')
编辑：
如果要获取第一个数据帧的 importance 值，只需使用 getNearestDate 函数确定哪些行需要该值，然后将它们与第二个数据帧合并。
first_df['index'] = first_df.apply(
lambda x: getNearestDate(second_df.index, x.date),
axis = 1,
result_type = 'reduce'
)
second_df = second_df.merge(first_df, how='left', on='index')
您应该可以使用 reindex 和 merge
# note the method and the tolerance. Change them to whatever works best for your actual data
new_df = df2.merge(df.reindex(df2.index, method='nearest', limit=1, tolerance='2T'),
left_index=True, right_index=True)
value importance
index
2006-12-05 08:03:01.985 6 NaN
2006-12-05 08:11:34.130 7 NaN
2006-12-05 08:20:05.959 6 NaN
2006-12-05 08:28:38.104 6 NaN
2006-12-05 08:37:02.995 6 NaN
2006-12-05 08:45:35.140 5 NaN
2006-12-05 08:54:06.969 6 NaN
2006-12-05 09:02:59.928 6 NaN
2006-12-05 09:11:32.072 6 NaN
2006-12-05 09:20:03.901 6 NaN
2006-12-05 09:28:36.046 5 NaN
2006-12-05 09:37:00.937 5 NaN
2006-12-05 09:45:33.082 6 NaN
2006-12-05 09:54:04.911 6 NaN
2006-12-05 10:02:04.889 6 NaN
2006-12-05 10:10:37.034 5 NaN
2006-12-05 10:19:08.863 6 NaN
2006-12-05 10:27:41.008 5 NaN
2006-12-05 10:36:04.953 5 HIGH
2006-12-05 10:44:37.098 5 NaN
2006-12-13 02:06:00.898 1 NaN
2006-12-13 02:14:33.043 1 NaN
2006-12-13 02:23:04.872 1 NaN
2006-12-13 02:31:03.904 1 NaN
2006-12-13 02:39:36.048 1 LOW
2006-12-13 02:48:07.878 2 NaN
2006-12-13 02:56:40.022 5 NaN
2006-12-13 03:05:04.914 2 NaN
2006-12-13 03:13:37.058 3 NaN
2006-12-13 03:22:08.888 6 NaN
2006-12-13 03:31:03.108 1 NaN
2006-12-13 03:39:34.937 1 NaN
2006-12-13 03:48:07.081 1 NaN
2006-12-13 03:56:38.911 2 NaN
2006-12-13 04:05:04.117 3 NaN
只需进行这些小改动，希望它会起作用
loc=[]
def getNearestDate(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
items = second_df.index
for pivot in first_df.date:
d = getNearestDate(items, pivot)
loc.append(second_df.set_index('index').index.get_loc(d))
## Adding Data to your second df
second_df['importance']=[]
for index,locations in enumerate(loc):
df['importance'][int(location)]=first_df['importance'][index]
首先我们必须保留与原始数据框中的日期相对应的日期：
items = second_df.index
dates = []
for pivot in first_df.date:
dates.append(getNearestDate(items, pivot))
first_df['new_date'] = dates
因为我们不再需要它们，我们可以删除整列：
first_df = first_df.drop(columns="date")
为了使合并工作，我们需要在两个数据帧上声明索引。
first_df.set_index("new_date", inplace =True)
合并完成如下：
second_df = second_df.merge(first_df, how = "left",left_index=True, right_index=True)
此外，永远不要让 NaN 出现在数据框中很重要：
second_df.importance = second_df.importance.fillna(0)