Question

因此，我正在寻找一种基于条件和时间范围来更改数据帧内容的简单解决方案。参见下面的代码：

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

我也知道我可以这样处理两个条件，但是我不认为这是一个“优雅”的解决方案，因为我不再使用熊猫的良好时间跨度功能了：

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

用条件和日期范围同时切片熊猫数据框的一种优雅方法是什么？

1 个答案: