I am encountering an errors each time i attempt to compute the difference in readings for a meter in my dataset. The dataset structure is this.
id paymenttermid houseid houseid-meterid quantity month year cleaned_quantity
Datetime
2019-02-01 255 water 215 215M201 23.0 2 2019 23.0
2019-02-01 286 water 193 193M181 24.0 2 2019 24.0
2019-02-01 322 water 172 172M162 22.0 2 2019 22.0
2019-02-01 323 water 176 176M166 61.0 2 2019 61.0
2019-02-01 332 water 158 158M148 15.0 2 2019 15.0
I am attempting to generate a new column called consumption that computes the difference in quantities consumed for each house(identified by houseid-meterid) after every month of the year.
The code i am using to implement this is:
water_df["consumption"] = water_df.groupby(["year", "month", "houseid-meterid"])["cleaned_quantity"].diff(-1)
After executing this code, the consumption column is filled with NaN values. How can I correctly implement this logic.
The end result looks like this:
id paymenttermid houseid houseid-meterid quantity month year cleaned_quantity consumption
Datetime
2019-02-01 255 water 215 215M201 23.0 2 2019 23.0 NaN
2019-02-01 286 water 193 193M181 24.0 2 2019 24.0 NaN
2019-02-01 322 water 172 172M162 22.0 2 2019 22.0 NaN
2019-02-01 323 water 176 176M166 61.0 2 2019 61.0 NaN
2019-02-01 332 water 158 158M148 15.0 2 2019 15.0 NaN
Many thank in advance.
I have attempted to use
water_df["consumption"] = water_df.groupby(["year", "month", "houseid-meterid"])["cleaned_quantity"].diff(-1)
and
water_df["consumption"] = water_df.groupby(["year", "month", "houseid-meterid"])["cleaned_quantity"].diff(0)
and
water_df["consumption"] = water_df.groupby(["year", "month", "houseid-meterid"])["cleaned_quantity"].diff()
all this commands result in the same behaviour as stated above.
Expected output should be:
Datetime houseid-meterid cleaned_quantity consumption
2019-02-01 215M201 23.0 20
2019-03-02 215M201 43.0 9
2019-04-01 215M201 52.0 12
2019-05-01 215M201 64.0 36
2019-06-01 215M201 100.0 20
what steps should i take?
Sort values by Datetime (if needed) then group by houseid-meterid before compute the diff for cleaned_quantity values then shift row to align with the right data:
df['consumption'] = (df.sort_values('Datetime')
.groupby('houseid-meterid')['cleaned_quantity']
.transform(lambda x: x.diff().shift(-1)))
print(df)
# Output
Datetime houseid-meterid cleaned_quantity consumption
0 2019-02-01 215M201 23.0 20.0
1 2019-03-02 215M201 43.0 9.0
2 2019-04-01 215M201 52.0 12.0
3 2019-05-01 215M201 64.0 36.0
4 2019-06-01 215M201 100.0 NaN
I have a Df with column date, I tried to search in this column where the years is 2020 and add to the date in the same point 31 days for example the df.date[1] = 10.12.2020 so this rows will be with the new data 10.1.2021
I think this is what you're looking for, if I'm understanding correctly:
import datetime
import pandas as pd
# Create a dummy dataframe
date_range = pd.date_range(datetime.datetime(2020, 1, 1), datetime.datetime(2022, 1, 1))
test = pd.DataFrame({"date": date_range})
test
date
0 2020-01-01
1 2020-01-02
2 2020-01-03
3 2020-01-04
4 2020-01-05
.. ...
727 2021-12-28
728 2021-12-29
729 2021-12-30
730 2021-12-31
731 2022-01-01
For each value, find if the year is 2020, and if so then add 31 days to the date:
test['norm_date'] = test['date'].where(test['date'].dt.year != 2020, test['date'].dt.date.add(datetime.timedelta(days=31)))
test
date norm_date
0 2020-01-01 2020-02-01
1 2020-01-02 2020-02-02
2 2020-01-03 2020-02-03
3 2020-01-04 2020-02-04
4 2020-01-05 2020-02-05
.. ... ...
727 2021-12-28 2021-12-28
728 2021-12-29 2021-12-29
729 2021-12-30 2021-12-30
730 2021-12-31 2021-12-31
731 2022-01-01 2022-01-01
I would like to know that how would I get a data like, Date in first column and all stations ID in second column and all respective values of Average Wind speed, Sunshine duration and all and below that, Next date in next row and all stations in column beside this. I have data as below,
Stations_ID Date Average windspeed (Beaufort) sunshine duration average cloud cover
0 102 2016-01-01 6.8 5.733 NaN
1 164 2016-01-01 1.6 0.000 8.0
2 232 2016-01-01 2.0 0.000 7.8
3 282 2016-01-01 1.2 0.000 7.8
4 183 2016-01-01 2.9 0.000 7.8
... ... ... ... ... ...
164035 4271 2021-12-31 6.7 0.000 7.6
164036 4625 2021-12-31 7.1 0.000 7.3
164037 4177 2021-12-31 2.6 4.167 3.9
164038 4887 2021-12-31 4.7 5.333 3.8
164039 4336 2021-12-31 3.4 0.000 7.4
I have used below line of code,
df_data_3111 = pd.DataFrame(df_data_311.groupby(['Date','Stations_ID'])['Minimum Temperature'])
But this does not work.
My data has the following structure:
np.random.seed(25)
tdf = pd.DataFrame({'person_id' :[1,1,1,1,
2,2,
3,3,3,3,3,
4,4,4,
5,5,5,5,5,5,5,
6,
7,7,
8,8,8,8,8,8,8,
9,9,
10,10
],
'Date': ['2021-01-02','2021-01-05','2021-01-07','2021-01-09',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11',
'2021-01-02','2021-01-05','2021-01-07',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11','2021-01-13','2021-01-15',
'2021-01-02',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05','2021-01-07','2021-01-09','2021-01-11','2021-01-13','2021-01-15',
'2021-01-02','2021-01-05',
'2021-01-02','2021-01-05'
],
'Quantity': np.floor(np.random.random(size=35)*100)
})
And I want to calculate moving average (2 periods) over Date. So, the final output looks like the following. For first MA, we are taking 2021-01-02 & 2021-01-05 across all observations & calculate the MA (50). Similarly for other dates. The output need not be in the structure I'm showing the report. I just need date & MA column in the final data.
Thanks!
IIUC, you can aggregate the similar dates first, getting the sum and count.
Then take the sum per rolling 2 dates (here it doesn't look like you want to take care of a defined period but rather raw successive values, so I am assuming here prior sorting).
Finally, perform the ratio of sum and count to get the mean:
g = tdf.groupby('Date')['Quantity']
out = g.sum().rolling(2).sum()/g.count().rolling(2).sum()
output:
Date
2021-01-02 NaN
2021-01-05 50.210526
2021-01-07 45.071429
2021-01-09 41.000000
2021-01-11 44.571429
2021-01-13 48.800000
2021-01-15 50.500000
Name: Quantity, dtype: float64
joining the original data:
g = tdf.groupby('Date')['Quantity']
s = g.sum().rolling(2).sum()/g.count().rolling(2).sum()
tdf.merge(s.rename('Quantity_MA(2)'), left_on='Date', right_index=True)
output:
person_id Date Quantity Quantity_MA(2)
0 1 2021-01-02 87.0 NaN
4 2 2021-01-02 41.0 NaN
6 3 2021-01-02 68.0 NaN
11 4 2021-01-02 11.0 NaN
14 5 2021-01-02 16.0 NaN
21 6 2021-01-02 51.0 NaN
22 7 2021-01-02 38.0 NaN
24 8 2021-01-02 51.0 NaN
31 9 2021-01-02 90.0 NaN
33 10 2021-01-02 45.0 NaN
1 1 2021-01-05 58.0 50.210526
5 2 2021-01-05 11.0 50.210526
7 3 2021-01-05 43.0 50.210526
12 4 2021-01-05 44.0 50.210526
15 5 2021-01-05 52.0 50.210526
23 7 2021-01-05 99.0 50.210526
25 8 2021-01-05 55.0 50.210526
32 9 2021-01-05 66.0 50.210526
34 10 2021-01-05 28.0 50.210526
2 1 2021-01-07 27.0 45.071429
8 3 2021-01-07 55.0 45.071429
13 4 2021-01-07 58.0 45.071429
16 5 2021-01-07 32.0 45.071429
26 8 2021-01-07 3.0 45.071429
3 1 2021-01-09 18.0 41.000000
9 3 2021-01-09 36.0 41.000000
17 5 2021-01-09 69.0 41.000000
27 8 2021-01-09 71.0 41.000000
10 3 2021-01-11 40.0 44.571429
18 5 2021-01-11 36.0 44.571429
28 8 2021-01-11 42.0 44.571429
19 5 2021-01-13 83.0 48.800000
29 8 2021-01-13 43.0 48.800000
20 5 2021-01-15 48.0 50.500000
30 8 2021-01-15 28.0 50.500000
I'm trying to resample some tick data I have into 1 minute blocks. The code appears to work fine but when I look into the resulting dataframe it is changing the order of the dates incorrectly. Below is what it looks like pre resample:
Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10
2020-06-30 17:00:00 41.68 2 tptBid tctRegular NaN 255 NaN 0 msNormal
2020-06-30 17:00:00 41.71 3 tptAsk tctRegular NaN 255 NaN 0 msNormal
2020-06-30 17:00:00 41.68 1 tptTradetctRegular NaN 255 NaN 0 msNormal
2020-06-30 17:00:00 41.71 5 tptAsk tctRegular NaN 255 NaN 0 msNormal
2020-06-30 17:00:00 41.71 8 tptAsk tctRegular NaN 255 NaN 0 msNormal
... ... ... ... ... ... ... ... ... ...
2020-01-07 17:00:21 41.94 5 tptBid tctRegular NaN 255 NaN 0 msNormal
2020-01-07 17:00:27 41.94 4 tptBid tctRegular NaN 255 NaN 0 msNormal
2020-01-07 17:00:40 41.94 3 tptBid tctRegular NaN 255 NaN 0 msNormal
2020-01-07 17:00:46 41.94 4 tptBid tctRegular NaN 255 NaN 0 msNormal
2020-01-07 17:00:50 41.94 3 tptBid tctRegular NaN 255 NaN 0 msNormal
As you can see the date starts at 5pm on the 30th of June. Then I use this code:
one_minute_dataframe['Price'] = df.Var2.resample('1min').last()
one_minute_dataframe['Volume'] = df.Var3.resample('1min').sum()
one_minute_dataframe.index = pd.to_datetime(one_minute_dataframe.index)
one_minute_dataframe.sort_index(inplace = True)
And I get the following:
Price Volume
2020-01-07 00:00:00 41.73 416
2020-01-07 00:01:00 41.74 198
2020-01-07 00:02:00 41.76 40
2020-01-07 00:03:00 41.74 166
2020-01-07 00:04:00 41.77 143
... ... ...
2020-06-30 23:55:00 41.75 127
2020-06-30 23:56:00 41.74 234
2020-06-30 23:57:00 41.76 344
2020-06-30 23:58:00 41.72 354
2020-06-30 23:59:00 41.74 451
It seems to want to start from midnight on the 1st of July. But I've tried sorting the index and it still is not changing.
Also, the datetime index seems to add lots more dates outside the ones that were originally in the dataframe and plonks them in the middle of the resampled one.
Any help would be great. Apologies if I've set this out poorly
I see what's happened. Somewhere in the data download the month and day have been switched around. That's why its putting July at the top, because it thinks it's January.