Question

import pandas as pd 
import numpy as np 
import datetime as dt

# Create Column names
col_names = ['930', '931', '932', '933', '934', '935']
# Create Index datetimes 
idx_names = pd.date_range(start = dt.datetime(2011, 1, 1), periods = 10, freq= 'D')

# Create dataframe with previously created column names and index datetimes
df1 = pd.DataFrame(np.random.randn(10, 6), columns=col_names, index=idx_names)

# Change the column names from strings to datetimes.time() object
df1.columns = [dt.datetime.strptime(x, '%H%M').time() for x in df1.columns]

# This step and the next step changes the dataframe into a chronological timeseries

df2 = df1.T.unstack()
df2.index = [dt.datetime.combine(x[0], x[1]) for x in df2.index.tolist()]

# Show the series
df2

问题：创建特定列表的最pythonic / pandas-thonic方法是什么？这个列表会说“每次9:32和9:34之间的差异在0到.50之间时，9：34和第二天9:34之间的区别是什么。

我用数据帧格式的数字（沿x轴的日期和沿y轴的时间）这样做，我想说的是（下面是伪代码，上面不是伪代码）：

# Create a column with wrong answers and right answers
df['Today 934 minus yesterday 934'] = df[934] - df[934].shift(1)

# Boolean mask were condition 1 (diff > 0) and condition 2 (diff < .5) are true
mask = (df[934].shift(1) - df[932].shift(1) > 0) & (df[934].shift(1) - df[932].shift(1) < .5)
# Apply the boolean mask to the dataframe. This is will remove all the answers
# I dont want from the df['Today 934 minus yesterday 934'] column
df2 = df[mask]

# Only the answers I want:
answers = df['Today 934 minus yesterday 934']

Answer 1

我的尝试，基本上是你的伪代码的填写版本。其他人可能会采取更清洁的方法。

mask1 = (df2.index.hour == 9) & (df2.index.minute == 34)
mask2 = (df2.index.hour == 9) & (df2.index.minute == 32)

diff_934 = df2[mask1] - df2[mask1].shift(-1)
diff_934 = diff_934[diff_934.index.minute == 34]

diff_932 = df2[mask1|mask2]  - df2[mask1|mask2].shift(-1)
diff_932 = diff_932[diff_932.index.minute == 34]
diff_932 = diff_932[(diff_932 > 0) & (diff_932 < .5)]

answer = diff_934.reindex(diff_932.index)


In [116]: answer
Out[116]: 
2011-01-02 09:34:00   -0.874153
2011-01-08 09:34:00    0.186254
dtype: float64

熊猫系列重新取样：如何根据之前的某些变化获取动作？

1 个答案: