CustID UsageDate EnergyConsumed
0 17111 2018-01-01 00:00:00 1.095
1 17111 2018-01-01 01:00:00 1.129
2 17111 2018-01-01 02:00:00 1.165
3 17111 2018-01-01 03:00:00 1.833
4 17111 2018-01-01 04:00:00 1.697
5 17111 2018-01-01 05:00:00 1.835
missing data point 1
6 17111 2018-01-01 07:00:00 1.835
7 17112 2018-01-01 00:00:00 1.095
8 17112 2018-01-01 01:00:00 1.129
missing data point 1
9 17112 2018-01-01 03:00:00 1.833
10 17112 2018-01-01 04:00:00 1.697
11 17112 2018-01-01 05:00:00 1.835
For every customer, I have hourly data. However, some data points are missing in between. I want to check the Min and Max of Usage Date and fill in the missing Usage Date in that time interval (all values are per hour) and EnergyConsumed as zero. I can later use ffill or backfill to take care of this.
Not every customer's max UsageDate is 2018-01-31 23:00:00. So we only want to extend the series till the max date of every customer.
missing point 1 is replaced by
17111 2018-01-01 06:00:00 0
missing point 2 is replaced by
17112 2018-01-01 02:00:00 0
My main point of trouble is how to find the min and max date of every customer and then generate the gaps of dates.
I have tried indexing by date and resampling but havent helped me reach the solution.
Also, I was wondering if there is a way to directly find customerID's which have missing values in the pattern described above. My data is very large and the solution provided by #Vaishali is computing heavy. Any inputs would be helpful!
You can group the Dataframe by custid and create index with desired date range. Now use this index to reindex the data
df['UsageDate'] = pd.to_datetime(df['UsageDate'])
idx = df.groupby('CustID')['UsageDate'].apply(lambda x: pd.Series(index = pd.date_range(x.min(), x.max(), freq = 'H'))).index
df.set_index(['CustID', 'UsageDate']).reindex(idx).fillna(0).reset_index().rename(columns = {'level_1':'UsageDate'})
CustID UsageDate EnergyConsumed
0 17111 2018-01-01 00:00:00 1.095
1 17111 2018-01-01 01:00:00 1.129
2 17111 2018-01-01 02:00:00 1.165
3 17111 2018-01-01 03:00:00 1.833
4 17111 2018-01-01 04:00:00 1.697
5 17111 2018-01-01 05:00:00 1.835
6 17111 2018-01-01 06:00:00 0.000
7 17111 2018-01-01 07:00:00 1.835
8 17112 2018-01-01 00:00:00 1.095
9 17112 2018-01-01 01:00:00 1.129
10 17112 2018-01-01 02:00:00 0.000
11 17112 2018-01-01 03:00:00 1.833
12 17112 2018-01-01 04:00:00 1.697
13 17112 2018-01-01 05:00:00 1.835
Explanation: Since the Usagedates have to be all the dates in the range of minimum and maximum date for that CustID, we group the data by CustID and create a series of min and max dates using date_range. Set the dates as index of the series rather than value. The result of the groupby will be a multiindex with CUSTID as level 0 and usage date as level 1. We now use this multiindex to reindex the original dataframe. It will use the values where the index matches, assign NaN at the rest. Finally convert the NaN to 0 using fillna.
First create DatetimeIndex and then use asfreq in apply:
df['UsageDate'] = pd.to_datetime(df['UsageDate'])
df = (df.set_index('UsageDate')
.groupby('CustID')['EnergyConsumed']
.apply(lambda x: x.asfreq('H'))
.fillna(0)
.reset_index()
)
print (df)
CustID UsageDate EnergyConsumed
0 17111 2018-01-01 00:00:00 1.095
1 17111 2018-01-01 01:00:00 1.129
2 17111 2018-01-01 02:00:00 1.165
3 17111 2018-01-01 03:00:00 1.833
4 17111 2018-01-01 04:00:00 1.697
5 17111 2018-01-01 05:00:00 1.835
6 17111 2018-01-01 06:00:00 0.000
7 17111 2018-01-01 07:00:00 1.835
8 17112 2018-01-01 00:00:00 1.095
9 17112 2018-01-01 01:00:00 1.129
10 17112 2018-01-01 02:00:00 0.000
11 17112 2018-01-01 03:00:00 1.833
12 17112 2018-01-01 04:00:00 1.697
13 17112 2018-01-01 05:00:00 1.835
Also is possible use parameter ffill or bfill:
df = (df.set_index('UsageDate')
.groupby('CustID')['EnergyConsumed']
.apply(lambda x: x.asfreq('H', method='ffill'))
.reset_index()
)
print (df)
CustID UsageDate EnergyConsumed
0 17111 2018-01-01 00:00:00 1.095
1 17111 2018-01-01 01:00:00 1.129
2 17111 2018-01-01 02:00:00 1.165
3 17111 2018-01-01 03:00:00 1.833
4 17111 2018-01-01 04:00:00 1.697
5 17111 2018-01-01 05:00:00 1.835
6 17111 2018-01-01 06:00:00 1.835
7 17111 2018-01-01 07:00:00 1.835
8 17112 2018-01-01 00:00:00 1.095
9 17112 2018-01-01 01:00:00 1.129
10 17112 2018-01-01 02:00:00 1.129
11 17112 2018-01-01 03:00:00 1.833
12 17112 2018-01-01 04:00:00 1.697
13 17112 2018-01-01 05:00:00 1.835
Related
I have the following data frame with hourly resolution
day_ahead_DK1
Out[27]:
DateStamp DK1
0 2017-01-01 20.96
1 2017-01-01 20.90
2 2017-01-01 18.13
3 2017-01-01 16.03
4 2017-01-01 16.43
... ...
8756 2017-12-31 25.56
8757 2017-12-31 11.02
8758 2017-12-31 7.32
8759 2017-12-31 1.86
type(day_ahead_DK1)
Out[28]: pandas.core.frame.DataFrame
But the current column DateStamp is missing hours. How can I add hours 00:00:00, to 2017-01-01 for Index 0 so it will be 2017-01-01 00:00:00, and then 01:00:00, to 2017-01-01 for Index 1 so it will be 2017-01-01 01:00:00, and so on, so that all my days will have hours from 0 to 23. Thank you!
The expected output:
day_ahead_DK1
Out[27]:
DateStamp DK1
0 2017-01-01 00:00:00 20.96
1 2017-01-01 01:00:00 20.90
2 2017-01-01 02:00:00 18.13
3 2017-01-01 03:00:00 16.03
4 2017-01-01 04:00:00 16.43
... ...
8756 2017-12-31 20:00:00 25.56
8757 2017-12-31 21:00:00 11.02
8758 2017-12-31 22:00:00 7.32
8759 2017-12-31 23:00:00 1.86
Use GroupBy.cumcount for counter with to_timedelta for hours and add to DateStamp column:
df['DateStamp'] = pd.to_datetime(df['DateStamp'])
df['DateStamp'] += pd.to_timedelta(df.groupby('DateStamp').cumcount(), unit='H')
print (df)
DateStamp DK1
0 2017-01-01 00:00:00 20.96
1 2017-01-01 01:00:00 20.90
2 2017-01-01 02:00:00 18.13
3 2017-01-01 03:00:00 16.03
4 2017-01-01 04:00:00 16.43
8756 2017-12-31 00:00:00 25.56
8757 2017-12-31 01:00:00 11.02
8758 2017-12-31 02:00:00 7.32
8759 2017-12-31 03:00:00 1.86
I have a time series dataset that can be created with the following code.
idx = pd.date_range("2018-01-01", periods=100, freq="H")
ts = pd.Series(idx)
dft = pd.DataFrame(ts,columns=["date"])
dft["data"] = ""
dft["data"][0:5]= "a"
dft["data"][5:15]= "b"
dft["data"][15:20]= "c"
dft["data"][20:30]= "d"
dft["data"][30:40]= "a"
dft["data"][40:70]= "c"
dft["data"][70:85]= "b"
dft["data"][85:len(dft)]= "c"
In the data column, the unique values are a,b,c,d. These values are repeating in a sequence in different time windows. I want to capture the first and last value of that time window. How can I do that?
Compute a grouper for your changing values using shift to compare consecutive rows, then use groupby+agg to get the min/max per group:
group = dft.data.ne(dft.data.shift()).cumsum()
dft.groupby(group)['date'].agg(['min', 'max'])
output:
min max
data
1 2018-01-01 00:00:00 2018-01-01 04:00:00
2 2018-01-01 05:00:00 2018-01-01 14:00:00
3 2018-01-01 15:00:00 2018-01-01 19:00:00
4 2018-01-01 20:00:00 2018-01-02 05:00:00
5 2018-01-02 06:00:00 2018-01-02 15:00:00
6 2018-01-02 16:00:00 2018-01-03 21:00:00
7 2018-01-03 22:00:00 2018-01-04 12:00:00
8 2018-01-04 13:00:00 2018-01-05 03:00:00
edit. combining with original data:
dft.groupby(group).agg({'data': 'first', 'date': ['min', 'max']})
output:
data date
first min max
data
1 a 2018-01-01 00:00:00 2018-01-01 04:00:00
2 b 2018-01-01 05:00:00 2018-01-01 14:00:00
3 c 2018-01-01 15:00:00 2018-01-01 19:00:00
4 d 2018-01-01 20:00:00 2018-01-02 05:00:00
5 a 2018-01-02 06:00:00 2018-01-02 15:00:00
6 c 2018-01-02 16:00:00 2018-01-03 21:00:00
7 b 2018-01-03 22:00:00 2018-01-04 12:00:00
8 c 2018-01-04 13:00:00 2018-01-05 03:00:00
I have a time series and I want to group the rows by hour of day (regardless of date) and visualize these as boxplots. So I'd want 24 boxplots starting from hour 1, then hour 2, then hour 3 and so on.
The way I see this working is splitting the dataset up into 24 series (1 for each hour of the day), creating a boxplot for each series and then plotting this on the same axes.
The only way I can think of to do this is to manually select all the values between each hour, is there a faster way?
some sample data:
Date Actual Consumption
2018-01-01 00:00:00 47.05
2018-01-01 00:15:00 46
2018-01-01 00:30:00 44
2018-01-01 00:45:00 45
2018-01-01 01:00:00 43.5
2018-01-01 01:15:00 43.5
2018-01-01 01:30:00 43
2018-01-01 01:45:00 42.5
2018-01-01 02:00:00 43
2018-01-01 02:15:00 42.5
2018-01-01 02:30:00 41
2018-01-01 02:45:00 42.5
2018-01-01 03:00:00 42.04
2018-01-01 03:15:00 41.96
2018-01-01 03:30:00 44
2018-01-01 03:45:00 44
2018-01-01 04:00:00 43.54
2018-01-01 04:15:00 43.46
2018-01-01 04:30:00 43.5
2018-01-01 04:45:00 43
2018-01-01 05:00:00 42.04
This is what i've tried so far:
zero = df.between_time('00:00', '00:59')
one = df.between_time('01:00', '01:59')
two = df.between_time('02:00', '02:59')
and then I would plot a boxplot for each of these on the same axes. However it's very tedious to do this for all 24 hours in a day.
This is the kind of output I want:
https://www.researchgate.net/figure/Boxplot-of-the-NOx-data-by-hour-of-the-day_fig1_24054015
there are 2 steps to achieve this:
convert Actual to date time:
df.Actual = pd.to_datetime(df.Actual)
Group by the hour:
df.groupby([df.Date, df.Actual.dt.hour+1]).Consumption.sum().reset_index()
I assumed you wanted to sum the Consumption (unless you wish to have mean or whatever just change it). One note: hour+1 so it will start from 1 and not 0 (remove it if you wish 0 to be midnight).
desired result:
Date Actual Consumption
0 2018-01-01 1 182.05
1 2018-01-01 2 172.50
2 2018-01-01 3 169.00
3 2018-01-01 4 172.00
4 2018-01-01 5 173.50
5 2018-01-01 6 42.04
I have a dateset as below.
dummy
datetime
2015-10-25 06:00:00 1
2015-04-05 20:00:00 1
2015-11-24 00:00:00 1
2015-08-18 08:00:00 1
2015-10-21 12:00:00 1
I want to change the datetime to the cloest predefined time point, say 00:00:00 and 12:00:00
dummy
datetime
2015-10-25 00:00:00 1
2015-04-05 12:00:00 1
2015-11-24 00:00:00 1
2015-08-18 00:00:00 1
2015-10-21 12:00:00 1
Here is possible use DatetimeIndex.floor:
df.index = df.index.floor('12H')
print (df)
dummy
datetime
2015-10-25 00:00:00 1
2015-04-05 12:00:00 1
2015-11-24 00:00:00 1
2015-08-18 00:00:00 1
2015-10-21 12:00:00 1
i have below dataframe. and i wanna make a hourly mean dataframe
condition that every hour just calculate mean value 00:15:00~00:45:00.
date/time are multi index.
aaa
date time
2017-01-01 00:00:00 146.88
00:15:00 143.28
00:30:00 143.28
00:45:00 141.12
01:00:00 134.64
01:15:00 132.48
01:30:00 136.80
01:45:00 138.24
02:00:00 131.76
02:15:00 131.04
02:30:00 134.64
02:45:00 139.68
03:00:00 136.08
03:15:00 132.48
03:30:00 132.48
03:45:00 139.68
04:00:00 134.64
04:15:00 131.04
04:30:00 160.56
04:45:00 177.12
...
results should be belows.. how can i do it?
aaa
date time
2017-01-01 00:00:00 146.88
01:00:00 134.64
02:00:00 131.76
03:00:00 136.08
04:00:00 134.64
...
It seems need only select rows with 00:00 in the end of times:
df2 = df1[df1.index.get_level_values(1).astype(str).str.endswith('00:00')]
print (df2)
aaa
date time
2017-01-01 00:00:00 146.88
01:00:00 134.64
02:00:00 131.76
03:00:00 136.08
04:00:00 134.64
But if need mean only values 00:15-00:45 it is more complicated:
lvl1 = pd.Series(df1.index.get_level_values(1))
m = ~lvl1.astype(str).str.endswith('00:00')
lvl1new = lvl1.mask(m).ffill()
df1.index = pd.MultiIndex.from_arrays([df1.index.get_level_values(0),
lvl1new.where(m)], names=df1.index.names)
print (df1)
aaa
date time
2017-01-01 NaN 146.88
00:00:00 143.28
00:00:00 143.28
00:00:00 141.12
NaN 134.64
01:00:00 132.48
01:00:00 136.80
01:00:00 138.24
NaN 131.76
02:00:00 131.04
02:00:00 134.64
02:00:00 139.68
NaN 136.08
03:00:00 132.48
03:00:00 132.48
03:00:00 139.68
NaN 134.64
04:00:00 131.04
04:00:00 160.56
04:00:00 177.12
df = df1['aaa'].groupby(level=[0,1]).mean()
print (df)
date time
2017-01-01 00:00:00 142.56
01:00:00 135.84
02:00:00 135.12
03:00:00 134.88
04:00:00 156.24
Name: aaa, dtype: float64