I have two tables a month table and a product table. The product table will be updated in the future to have new prices (I will insert new 'valid_from' dates)
I would like to join the two tables together to return month, product, price_rate, initial_price, hire_price, other and connection under specific parameters:
I want to return months from the months table that fall within the date range as defined between valid_from and the next value of valid_from for the product and price rate, the following begins to return my required dates and columns:
SELECT
month,
product,
price_rate,
initial_price,
hire_price,
other,
connection
FROM w.products pc
RIGHT JOIN m.month m ON m.month >= pc.valid_from
However; I need to bring back all months from the months table where valid_from is null in the products table, prior to the instance of the next valid from (for the same product and price rate)
I also want to bring back the connection value - how can I do this/what do I join on as I'm currently joining on date, but this doesn't exist within the row where there is a value for connection
id
product
price_rate
valid_from
initial_price
hire_price
other
connection
1
computer
100
154.75
115.5
0.015
2
computer
100
01/01/2021
154.75
115.5
0.015
3
computer
1000
154.75
135
0.015
4
computer
1000
01/01/2021
154.75
135
0.015
5
computer
10000
01/01/2020
453.41
345.5
0.015
6
mouse
100
154.75
142.5
0.015
7
mouse
100
01/01/2021
154.75
142.5
0.015
8
mouse
1000
01/01/2020
154.75
162
0.015
9
mouse
10000
01/01/2020
450.91
415
0.015
10
keyboard
100
163.08
142.5
0.015
11
keyboard
100
01/01/2021
163.08
142.5
0.015
12
keyboard
1000
01/01/2020
163.08
162
0.015
13
121
month
01/01/2019
01/02/2019
01/03/2019
01/04/2019
01/05/2019
01/06/2019
01/07/2019
01/08/2019
01/09/2019
01/10/2019
01/11/2019
01/12/2019
01/01/2020
01/02/2020
01/03/2020
01/04/2020
01/05/2020
01/06/2020
01/07/2020
01/08/2020
01/09/2020
01/10/2020
01/11/2020
01/12/2020
01/01/2021
01/02/2021
01/03/2021
01/04/2021
01/05/2021
01/06/2021
01/07/2021
01/08/2021
01/09/2021
01/10/2021
01/11/2021
01/12/2021
01/01/2022
01/02/2022
I have a table with individual records and another which holds historical information about the individuals in the former.
I want to extract information about the individuals from the second table. Both tables have timestamp. It is very important that the historical information happened before the record in the first table.
Date_Time name
0 2021-09-06 10:46:00 Leg It Liam
1 2021-09-06 10:46:00 Hollyhill Island
2 2021-09-06 10:46:00 Shani El Bolsa
3 2021-09-06 10:46:00 Kilbride Fifi
4 2021-09-06 10:46:00 Go
2100 2021-10-06 11:05:00 Slaneyside Babs
2101 2021-10-06 11:05:00 Hillview Joe
2102 2021-10-06 11:05:00 Fairway Flyer
2103 2021-10-06 11:05:00 Whiteys Surprise
2104 2021-10-06 11:05:00 Astons Lucy
The name is the variable by which you connect the two tables:
Date_Time name cc
13 2021-09-15 12:16:00 Hollyhill Island 6.00
14 2021-09-06 10:46:00 Hollyhill Island 4.50
15 2021-05-30 18:28:00 Hollyhill Island 3.50
16 2021-05-25 10:46:00 Hollyhill Island 2.50
17 2021-05-18 12:46:00 Hollyhill Island 2.38
18 2021-04-05 12:31:00 Hollyhill Island 3.50
19 2021-04-28 12:16:00 Hollyhill Island 3.75
I want to add aggregated data from this table to the first. Such as adding the cc mean and count.
Date_Time name
1 2021-09-06 10:46:00 Hollyhill Island
This line I would add 5 for cc count and 3.126 for the cc mean. Remember the historical records need to be before the date time of the individual records.
I am a bit confused how to do this efficiently. I know I need to groupby the historical data.
Also the individual records are usually in groups of Date_Time, if that makes it any easier.
IIUC:
try:
out=df1.merge(df2,on='name',suffixes=('','_y'))
#merging both df's on name
out=out.mask(out['Date_Time']<=out['Date_Time_y']).dropna()
#filtering results
out=out.groupby(['Date_Time','name'])['cc'].agg(['count','mean']).reset_index()
#aggregrating values
output of out:
Date_Time name count mean
0 2021-09-06 10:46:00 Hollyhill Island 5 3.126
I have this df:
CODE TMAX TMIN PP
DATE
1991-01-01 000130 32.6 23.4 0.0
1991-01-02 000130 31.2 22.4 0.0
1991-01-03 000130 32.0 NaN 0.0
1991-01-04 000130 32.2 23.0 0.0
1991-01-05 000130 30.5 22.0 0.0
... ... ... ...
2020-12-27 158328 NaN NaN NaN
2020-12-28 158328 NaN NaN NaN
2020-12-29 158328 NaN NaN NaN
2020-12-30 158328 NaN NaN NaN
2020-12-31 158328 NaN NaN NaN
I have data of 30 years (1991-2020) for each CODE, and i want to calculate monthly normals of TMAX, TMIN and PP. So for TMAX and TMIN i should calculate the average for every month, so if January have 31 days i should get the mean of those 31 values and get a value for January 1991, January 1992, etc. So i will have 30 Januarys (January 1991, January 1992, ... ,January 2020), 30 Februarys, etc. After this i should calculate the average of every group of months (Januarys with Januarys, Februarys with Februarys, etc). So i will have 12 values (one value for every month). Example:
(January1991 + January1992 + ..... + January 2020) /30
(February1991 + February1992 + ..... + February 2020) /30
.... same for every group of months.
So i'm using this code but i don't know if it's ok.
from datetime import date
normalstemp=df[['CODE','TMAX','TMIN']].groupby([df.CODE, df.index.month]).mean().round(1)
For PP (precipitation) i should sum the values of every PP value of the month, so if January have 31 days i should sum all of their values and get a value for January 1991, January 1992, etc. So i will have 30 Januarys (January 1991, January 1992, ... ,January 2020) , 30 Februarys (February 1991, February 1992, ... ,February 2020), etc. After this i should calculate the average of every group of months (Januarys with Januarys, Februarys with Februarys, etc). So i will have 12 values (one value for every month, the same as TMAX and TMIN).
Example:
(January1991 + January1992 + ..... + January 2020) /30
(February1991 + February1992 + ..... + February 2020) /30
.... same for every group of months.
So im using this code but i know this code isn't correct because i'm not getting the mean of the januarys, februarys, etc.
normalspp=df[['CODE','PP']].groupby([df.CODE, df.index.month]).sum().round(1)
I only have basic knowledge of python so i will appreciate if you can help me.
Thanks in advance.
Ver 2: Average by Year-Month and by Month
import pandas as pd
import numpy as np
x = pd.date_range(start='1/1/1991', end='12/31/2020',freq='D')
df = pd.DataFrame({'Date':x.tolist()*2,
'Code':['000130']*10958 + ['158328']*10958,
'TMAX': np.random.randint(6,10, size=21916),
'TMIN': np.random.randint(1,5, size=21916)
})
# Create a Month column to get Average by Month for all years
df['Month'] = df.Date.dt.month
# Create a Year-Month column to get Average of each Month within the Year
df['Year_Mon'] = df.Date.dt.strftime('%Y-%m')
# Print the Average of each Month within each Year for each code
print (df.groupby(['Code','Year_Mon'])['TMAX'].mean())
print (df.groupby(['Code','Year_Mon'])['TMIN'].mean())
# Print the Average of each Month irrespective of the year (for each code)
print (df.groupby(['Code','Month'])['TMAX'].mean())
print (df.groupby(['Code','Month'])['TMAX'].mean())
If you want to give a name for the TMAX Average value, you can add the reset_index and rename column. Here's code to do that.
print (df.groupby(['Code','Year_Mon'])['TMAX'].mean().reset_index().rename(columns={'TMAX':'TMAX_Avg'}))
The output of this will be:
Average of TMAX for each Year-Month for each Code
Code Year_Mon
000130 1991-01 7.225806
1991-02 7.678571
1991-03 7.354839
1991-04 7.500000
1991-05 7.516129
...
158328 2020-08 7.387097
2020-09 7.300000
2020-10 7.516129
2020-11 7.500000
2020-12 7.451613
Name: TMAX, Length: 720, dtype: float64
Average of TMIN for each Year-Month for each Code
Code Year_Mon
000130 1991-01 2.419355
1991-02 2.571429
1991-03 2.193548
1991-04 2.366667
1991-05 2.451613
...
158328 2020-08 2.451613
2020-09 2.566667
2020-10 2.612903
2020-11 2.666667
2020-12 2.580645
Name: TMIN, Length: 720, dtype: float64
Average of TMAX for each Month for each Code (all years combined)
Code Month
000130 1 7.540860
2 7.536557
3 7.482796
4 7.486667
5 7.444086
6 7.570000
7 7.507527
8 7.529032
9 7.501111
10 7.401075
11 7.482222
12 7.517204
158328 1 7.532258
2 7.563679
3 7.490323
4 7.555556
5 7.500000
6 7.497778
7 7.545161
8 7.483871
9 7.526667
10 7.529032
11 7.547778
12 7.524731
Name: TMAX, dtype: float64
Average of TMIN for each Month for each Code (all years combined)
Code Month
000130 1 7.540860
2 7.536557
3 7.482796
4 7.486667
5 7.444086
6 7.570000
7 7.507527
8 7.529032
9 7.501111
10 7.401075
11 7.482222
12 7.517204
158328 1 7.532258
2 7.563679
3 7.490323
4 7.555556
5 7.500000
6 7.497778
7 7.545161
8 7.483871
9 7.526667
10 7.529032
11 7.547778
12 7.524731
Name: TMAX, dtype: float64
Ver 1: Average by Year and Month for each Code
Here is one way to do this.
You can create two columns - Year and Month. Then get the average of TMAX, TMIN, and PP for each month within the year by doing a groupby ('Code','Year_Mon')
See code for more details.
import pandas as pd
import numpy as np
# create a range of dates from 1/1/2018 thru 12/31/2020 for each day
x = pd.date_range(start='1/1/2018', end='12/31/2020',freq='D')
# create a dataframe with the date ranges x 2 for two codes
# TMIN is a random value from 1 thru 5 - you can put your actual data here
# TMAX is a random value from 6 thru 10 - you can put your actual data here
df = pd.DataFrame({'Date':x.tolist()*2,
'Code':['000130']*1096 + ['158328']*1096,
'TMAX': np.random.randint(6,10, size=2192),
'TMIN': np.random.randint(1,5, size=2192)
})
# Create a Year-Month column using df.Date.dt.strftime
df['Year_Mon'] = df.Date.dt.strftime('%Y-%m')
# Calculate the Average of TMAX and TMIN using groupby Code and Year_Mon
df['TMAX_Avg'] = df.groupby(['Code','Year_Mon'])['TMAX'].transform('mean')
df['TMIN_Avg'] = df.groupby(['Code','Year_Mon'])['TMIN'].transform('mean')
The output of this will be:
Date Code TMAX TMIN Year_Mon TMAX_Avg TMIN_Avg
0 2018-01-01 000130 8 2 2018-01 7.451613 2.129032
1 2018-01-02 000130 7 4 2018-01 7.451613 2.129032
2 2018-01-03 000130 9 2 2018-01 7.451613 2.129032
3 2018-01-04 000130 6 1 2018-01 7.451613 2.129032
4 2018-01-05 000130 9 4 2018-01 7.451613 2.129032
5 2018-01-06 000130 6 1 2018-01 7.451613 2.129032
6 2018-01-07 000130 9 2 2018-01 7.451613 2.129032
7 2018-01-08 000130 9 2 2018-01 7.451613 2.129032
8 2018-01-09 000130 7 2 2018-01 7.451613 2.129032
9 2018-01-10 000130 8 2 2018-01 7.451613 2.129032
10 2018-01-11 000130 8 3 2018-01 7.451613 2.129032
11 2018-01-12 000130 7 2 2018-01 7.451613 2.129032
12 2018-01-13 000130 7 1 2018-01 7.451613 2.129032
13 2018-01-14 000130 8 1 2018-01 7.451613 2.129032
14 2018-01-15 000130 7 3 2018-01 7.451613 2.129032
15 2018-01-16 000130 6 1 2018-01 7.451613 2.129032
16 2018-01-17 000130 6 3 2018-01 7.451613 2.129032
17 2018-01-18 000130 9 3 2018-01 7.451613 2.129032
18 2018-01-19 000130 7 2 2018-01 7.451613 2.129032
19 2018-01-20 000130 8 1 2018-01 7.451613 2.129032
20 2018-01-21 000130 9 4 2018-01 7.451613 2.129032
21 2018-01-22 000130 6 2 2018-01 7.451613 2.129032
22 2018-01-23 000130 9 4 2018-01 7.451613 2.129032
23 2018-01-24 000130 6 2 2018-01 7.451613 2.129032
24 2018-01-25 000130 8 3 2018-01 7.451613 2.129032
25 2018-01-26 000130 6 2 2018-01 7.451613 2.129032
26 2018-01-27 000130 8 1 2018-01 7.451613 2.129032
27 2018-01-28 000130 8 3 2018-01 7.451613 2.129032
28 2018-01-29 000130 6 1 2018-01 7.451613 2.129032
29 2018-01-30 000130 6 1 2018-01 7.451613 2.129032
30 2018-01-31 000130 8 1 2018-01 7.451613 2.129032
31 2018-02-01 000130 7 1 2018-02 7.250000 2.428571
32 2018-02-02 000130 6 2 2018-02 7.250000 2.428571
33 2018-02-03 000130 6 4 2018-02 7.250000 2.428571
34 2018-02-04 000130 8 3 2018-02 7.250000 2.428571
35 2018-02-05 000130 8 2 2018-02 7.250000 2.428571
36 2018-02-06 000130 6 3 2018-02 7.250000 2.428571
37 2018-02-07 000130 6 3 2018-02 7.250000 2.428571
38 2018-02-08 000130 7 1 2018-02 7.250000 2.428571
39 2018-02-09 000130 9 4 2018-02 7.250000 2.428571
40 2018-02-10 000130 8 2 2018-02 7.250000 2.428571
41 2018-02-11 000130 7 4 2018-02 7.250000 2.428571
42 2018-02-12 000130 8 1 2018-02 7.250000 2.428571
43 2018-02-13 000130 6 4 2018-02 7.250000 2.428571
44 2018-02-14 000130 6 1 2018-02 7.250000 2.428571
45 2018-02-15 000130 6 4 2018-02 7.250000 2.428571
46 2018-02-16 000130 8 2 2018-02 7.250000 2.428571
47 2018-02-17 000130 7 3 2018-02 7.250000 2.428571
48 2018-02-18 000130 9 3 2018-02 7.250000 2.428571
49 2018-02-19 000130 8 2 2018-02 7.250000 2.428571
If you want only the Code, Year-Month, and TMIN and TMAX values, you can do:
TMAX average for each month within the year:
print (df.groupby(['Code','Year_Mon'])['TMAX'].mean())
Output will be:
Code Year_Mon
000130 2018-01 7.451613
2018-02 7.250000
2018-03 7.774194
2018-04 7.366667
2018-05 7.451613
...
158328 2020-08 7.935484
2020-09 7.666667
2020-10 7.548387
2020-11 7.333333
2020-12 7.580645
TMIN average for each month within the year:
print (df.groupby(['Code','Year_Mon'])['TMIN'].mean())
Output will be:
Code Year_Mon
000130 2018-01 2.129032
2018-02 2.428571
2018-03 2.451613
2018-04 2.500000
2018-05 2.677419
...
158328 2020-08 2.709677
2020-09 2.166667
2020-10 2.161290
2020-11 2.366667
2020-12 2.548387
I have a huge data that needs to be grouped based on its 'IDs' and only the last value of each ID needs to be exported into a SINGLE csv/excel file.
incl = ['A', 'B', 'C']
for k, g in df[df['ID'].isin(incl)].groupby('ID'):
g.tail(1).to_csv(f'{k}.csv')
I have tried this but it makes different csv files for each ID instead of a one big file containing the last value of each group.
Sample data:
ID Date Open High Low
30 UNITY 2020-06-18 11.50 11.75 11.41
31 UNITY 2020-06-21 11.44 11.50 10.88
32 UNITY 2020-06-22 11.26 11.78 11.26
33 UNITY 2020-06-23 11.72 12.08 11.53
34 UNITY 2020-06-24 11.51 11.59 11.40
35 UNITY 2020-06-25 11.85 11.85 11.11
36 SSOM 2020-05-03 27.50 27.95 27.00
37 SSOM 2020-05-05 27.50 27.50 27.50
38 SSOM 2020-05-06 29.20 29.56 29.20
39 SSOM 2020-05-07 31.77 31.77 31.77
I'm trying to do a weekly forecast in FBProphet for just 5 weeks ahead. The make_future_dataframe method doesn't seem to be working right....makes the correct one week intervals except for one week between jul 3 and Jul 5....every other interval is correct at 7 days or a week. Code and output below:
INPUT DATAFRAME
ds y
548 2010-01-01 3117
547 2010-01-08 2850
546 2010-01-15 2607
545 2010-01-22 2521
544 2010-01-29 2406
... ... ...
4 2020-06-05 2807
3 2020-06-12 2892
2 2020-06-19 3012
1 2020-06-26 3077
0 2020-07-03 3133
CODE
future = m.make_future_dataframe(periods=5, freq='W')
future.tail(9)
OUTPUT
ds
545 2020-06-12
546 2020-06-19
547 2020-06-26
548 2020-07-03
549 2020-07-05
550 2020-07-12
551 2020-07-19
552 2020-07-26
553 2020-08-02
All you need to do is create a dataframe with the dates you need for predict method. utilizing the make_future_dataframe method is not necessary.