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我有一个数据框,这是从阅读csv的结果。它包含一个日期时间列和与事件相关的数据。我需要用每20分钟的统计数据来计算平均一天,在下面的代码中,我使用'mean'作为示例。熊猫groupby得到一个平均的日子
编辑: 我的数据是观察结果。这意味着并非所有箱都有数据。但是,在计算平均值时,必须考虑这个零计数:mean = count /#days
此代码有效,但这是要走的路吗?它看起来对我来说很复杂,我不知道我是否真的需要在一天中的某个时间给我们一个BinID和不可能的组。
import pandas as pd
# Create dataframe
data = {'date': pd.date_range('2017-01-01 00:30:00', freq='10min', periods=282),
'i/o': ['in', 'out'] * 141}
df = pd.DataFrame(data)
# Add ones
df['move'] = 1
# I did try:
# 1)
# df['time'] = df['date'].dt.time
# df.groupby(['i/o', pd.Grouper(key='time', freq='20min')])
# This failed with groupby, so should I use my own bins then???
# 2)
# Create 20 minutes bins
# df['binID'] = df['date'].dt.hour*3 + df['date'].dt.minute//20
# averageDay = df.groupby(['i/o', 'binID']).agg(['count', 'sum', 'mean'])
#
# Well, bins with zero moves aren't their.
# So 'mean' can't be used as well as other functions that
# need the number of observations. Resample and reindex then???
# Resample
df2 = df.groupby(['i/o', pd.Grouper(key='date', freq='20min')]).agg('sum')
# Reindex and reset (for binID and groupby)
levels = [['in', 'out'],
pd.date_range('2017-01-01 00:00:00', freq='20min', periods=144)]
newIndex = pd.MultiIndex.from_product(levels, names=['i/o', 'date'])
df2 = df2.reindex(newIndex, fill_value=0).reset_index()
# Create 20 minutes bins
df2['binID'] = df2['date'].dt.hour*3 + df2['date'].dt.minute//20
# Average day
averageDay2 = df2.groupby(['i/o', 'binID']).agg(['count', 'sum', 'mean'])
print(averageDay2)
我想,我是不太清楚。我的数据是观察结果。这意味着并非所有箱都有数据。但是在计算平均值时必须考虑这个零计数:mean = count/#days – EdGO