同时按条件和日期范围对 pandas 数据帧进行切片的优雅方法是什么？

Question

所以我正在寻找一种简单的解决方案，用于根据条件和时间范围更改数据框的内容。请参阅下面的代码：

import numpy as np
import pandas as pd

data = pd.DataFrame(data=np.random.rand(15,2), index = pd.DatetimeIndex(start = "2018-01-01 00:00", end = "2018-01-01 00:14", freq="1min"), columns = ["A", "B"])


data.loc[data["A"].between(0.2,0.3), :].loc[:"2018-01-01 00:02", "A"] = 4

# /Users/ap/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning:
# A value is trying to be set on a copy of a slice from a DataFrame

# See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
#   self._setitem_with_indexer(indexer, value)
# __main__:1: SettingWithCopyWarning:
# A value is trying to be set on a copy of a slice from a DataFrame

# See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

print(data)

#                             A         B
# 2018-01-01 00:00:00  0.146793  0.198634
# 2018-01-01 00:01:00  0.284354  0.422438
# 2018-01-01 00:02:00  0.359768  0.199127
# 2018-01-01 00:03:00  0.306145  0.538669
# 2018-01-01 00:04:00  0.839377  0.299983
# 2018-01-01 00:05:00  0.236554  0.127450
# 2018-01-01 00:06:00  0.262167  0.304692
# 2018-01-01 00:07:00  0.341273  0.099983
# 2018-01-01 00:08:00  0.721702  0.763717
# 2018-01-01 00:09:00  0.196948  0.541878
# 2018-01-01 00:10:00  0.673248  0.421809
# 2018-01-01 00:11:00  0.892244  0.070801
# 2018-01-01 00:12:00  0.354958  0.184147
# 2018-01-01 00:13:00  0.062060  0.840900
# 2018-01-01 00:14:00  0.139046  0.742875

# ==> Nothing happened as indicated by the warning


# non-elegant way to solve the issue:
x = data.loc[data["A"].between(0.2,0.3), :]
x.loc[:"2018-01-01 00:02", "A"] = 4
data.loc[x.index,:] = x

print(data)

#                             A         B
# 2018-01-01 00:00:00  0.146793  0.198634
# 2018-01-01 00:01:00  4.000000  0.422438
# 2018-01-01 00:02:00  0.359768  0.199127
# 2018-01-01 00:03:00  0.306145  0.538669
# 2018-01-01 00:04:00  0.839377  0.299983
# 2018-01-01 00:05:00  0.236554  0.127450
# 2018-01-01 00:06:00  0.262167  0.304692
# 2018-01-01 00:07:00  0.341273  0.099983
# 2018-01-01 00:08:00  0.721702  0.763717
# 2018-01-01 00:09:00  0.196948  0.541878
# 2018-01-01 00:10:00  0.673248  0.421809
# 2018-01-01 00:11:00  0.892244  0.070801
# 2018-01-01 00:12:00  0.354958  0.184147
# 2018-01-01 00:13:00  0.062060  0.840900
# 2018-01-01 00:14:00  0.139046  0.742875

我也知道我可以像这样从中得出两个条件，但我不认为这是一个 "elegant" 解决方案，因为我没有使用 pandas 再:

from datetime import datetime
data.loc[(data["A"].between(0.2,0.3)) & (data.index < datetime.strptime("2018-01-01 00:02", "%Y-%m-%d %H:%M")), "A"] = 4

Answer 1

这将完成工作：

data.loc[:"2018-01-01 00:02","A"][data.loc[:"2018-01-01 00:02", "A"].between(0.2,0.3)]=4

同时按条件和日期范围对 pandas 数据帧进行切片的优雅方法是什么？

What is an elegant way to slice a pandas dataframe by a condition AND a date range at the same time?

python

pandas

numpy

indexing

numpy-slicing