I have dataframe like this below in pandas,
EMP_ID| Date| Target_GWP
1 | Jan-2017| 100
2 | Jan 2017| 300
1 | Feb-2017| 500
2 | Feb-2017| 200
and I need my output to be printed in below form.
EMP_ID| Date| Target_GWP | past_Target_GWP
1 | Feb-2017| 600 |100
2 | Feb-2017| 500 |300
Basically I have monthly data coming in excel and I want to aggregate this Target_GWP for each EMP_ID against the latest(current month) and have to create a back up column in pandas dataframe for past month Target_GWP. So How will i back the past month target_GWP and add it to current month Target GWP
Any leads on this would be appreciated.
Use:
#convert to datetime
df['Date'] = pd.to_datetime(df['Date'])
#sorting and get last 2 rows
df = df.sort_values(['EMP_ID','Date']).groupby('EMP_ID').tail(2)
#aggregation
df = df.groupby('EMP_ID', as_index=False).agg({'Date':'last', 'Target_GWP':['sum','first']})
df.columns = ['EMP_ID','Date','Target_GWP','past_Target_GWP']
print (df)
EMP_ID Date Target_GWP past_Target_GWP
0 1 2017-02-01 600 100
1 2 2017-02-01 500 300
Or if need top value in Target_GWP instead sum use last:
df = df.groupby('EMP_ID', as_index=False).agg({'Date':'last', 'Target_GWP':['last','first']})
df.columns = ['EMP_ID','Date','Target_GWP','past_Target_GWP']
print (df)
EMP_ID Date Target_GWP past_Target_GWP
0 1 2017-02-01 500 100
1 2 2017-02-01 200 300
Related
I have the following dataframe
date | value | ID
--------------------------------------
2021-12-06 15:00:00 25 1
2021-12-06 15:15:00 35 1
2021-11-30 00:00:00 20 2
2021-11-25 00:00:00 10 2
I want to join this DF with another one like this:
idUser | Name | Gender
-------------------
1 John M
2 Anne F
My expected output is:
ID | Name | Gender | Value
---------------------------
1 John M 35
2 Anne F 20
What I need is: Get only the most recent value of the first dataframe and join only this value with my second dataframe. Although, my spark script is joining both values:
My code:
df = df1.select(
col("date"),
col("value"),
col("ID"),
).OrderBy(
col("ID").asc(),
col("date").desc(),
).groupBy(
col("ID"), col("date").cast(StringType()).substr(0,10).alias("date")
).agg (
max(col("value")).alias("value")
)
final_df = df2.join(
df,
(col("idUser") == col("ID")),
how="left"
)
When i perform this join (formating the columns is abstracted in this post) I have the following output:
ID | Name | Gender | Value
---------------------------
1 John M 35
2 Anne F 20
2 Anne F 10
I use substr to remove hours and minutes to filter only by date. But when I have the same ID in different days my output df has the 2 values instead of the most recently. How can I fix this?
Note: I'm using only pyspark functions to do this (I now want to use spark.sql(...)).
You can use window and row_number function in pysaprk
from pyspark.sql.window import Window
from pyspark.sql.functions import row_number
windowSpec = Window.partitionBy("ID").orderBy("date").desc()
df1_latest_val = df1.withColumn("row_number", row_number().over(windowSpec)).filter(
f.col("row_number") == 1
)
The output of table df1_latest_val will look something like this
date | value | ID | row_number |
-----------------------------------------------------
2021-12-06 15:15:00 35 1 1
2021-11-30 00:00:00 20 2 1
Now you will have df with the latest val, which you can directly join with another table.
I need to create two different columns, one for this year sales and one column for last year sales from a transactional level data?
Data format:-
Date | bill amount
2019-07-22 | 500
2019-07-25 | 200
2020-11-15 | 100
2020-11-06 | 900
2020-12-09 | 50
2020-12-21 | 600
Required format:-
Year_month |This month Sales | Prev month sales
2019_07 | 700 | -
2020_11 | 1000 | -
2020_12 | 650 | 1000
The relatively tricky bit is to figure out what the previous month is. We do it by figuring out the beginning of the month for each date and then rolling back by 1 month. Note that this will take care of January -> December of previous year issues
We start by creating a sample dataframe and importing some useful modules
from io import StringIO
from datetime import datetime,timedelta
from dateutil.relativedelta import relativedelta
data = StringIO(
"""
date|amount
2019-07-22|500
2019-07-25|200
2020-11-15|100
2020-11-06|900
2020-12-09|50
2020-12-21|600
""")
df = pd.read_csv(data,sep='|')
df['date'] = pd.to_datetime(df['date'])
df
we get
date amount
0 2019-07-22 500
1 2019-07-25 200
2 2020-11-15 100
3 2020-11-06 900
4 2020-12-09 50
5 2020-12-21 600
Then we figure out the month start and the previous month start using datetime utilities
df['month_start'] = df['date'].apply(lambda d:datetime(year = d.year, month = d.month, day = 1))
df['prev_month_start'] = df['month_start'].apply(lambda d:d+relativedelta(months = -1))
Then we summarize monthly sales using groupby on month start
ms_df = df.drop(columns = 'date').groupby('month_start').agg({'prev_month_start':'first','amount':sum}).reset_index()
ms_df
so we get
month_start prev_month_start amount
0 2019-07-01 2019-06-01 700
1 2020-11-01 2020-10-01 1000
2 2020-12-01 2020-11-01 650
Then we join (merge) ms_df on itself by mapping 'prev_month_start' to 'month_start'
ms_df2 = ms_df.merge(ms_df, left_on='prev_month_start', right_on='month_start', how = 'left', suffixes = ('','_prev'))
We are more or less there but now make it pretty by getting rid of superfluous columns, adding labels, etc
ms_df2['label'] = ms_df2['month_start'].dt.strftime('%Y_%m')
ms_df2 = ms_df2.drop(columns = ['month_start','prev_month_start','month_start_prev','prev_month_start_prev'])
columns = ['label','amount','amount_prev']
ms_df2 = ms_df2[columns]
and we get
| | label | amount | amount_prev |
|---:|--------:|---------:|--------------:|
| 0 | 2019_07 | 700 | nan |
| 1 | 2020_11 | 1000 | nan |
| 2 | 2020_12 | 650 | 1000 |
Using #piterbarg's data, we can use resample, combined with shift and concat to get your desired data:
import pandas as pd
from io import StringIO
data = StringIO(
"""
date|amount
2019-07-22|500
2019-07-25|200
2020-11-15|100
2020-11-06|900
2020-12-09|50
2020-12-21|600
"""
)
df = pd.read_csv(data, sep="|", parse_dates=["date"])
df
date amount
0 2019-07-22 500
1 2019-07-25 200
2 2020-11-15 100
3 2020-11-06 900
4 2020-12-09 50
5 2020-12-21 600
Get the sum for current sales:
data = df.resample(on="date", rule="1M").amount.sum().rename("This_month")
data
date
2019-07-31 700
2019-08-31 0
2019-09-30 0
2019-10-31 0
2019-11-30 0
2019-12-31 0
2020-01-31 0
2020-02-29 0
2020-03-31 0
2020-04-30 0
2020-05-31 0
2020-06-30 0
2020-07-31 0
2020-08-31 0
2020-09-30 0
2020-10-31 0
2020-11-30 1000
2020-12-31 650
Freq: M, Name: This_month, dtype: int64
Now, we can shift the month to get values for previous month, and drop rows that have 0 as total sales to get your final output:
(pd.concat([data, data.shift().rename("previous_month")], axis=1)
.query("This_month!=0")
.fillna(0))
This_month previous_month
date
2019-07-31 700 0.0
2020-11-30 1000 0.0
2020-12-31 650 1000.0
I have a pandas dataframe that looks like this:
ID Date Event_Type
1 01/01/2019 A
1 01/01/2019 B
2 02/01/2019 A
3 02/01/2019 A
I want to be left with:
ID Date
1 01/01/2019
2 02/01/2019
3 02/01/2019
Where my condition is:
If the ID is the same AND the dates are within 2 days of each other then drop one of the rows.
If however the dates are more than 2 days apart then keep both rows.
How do I do this?
I believe you need first convert values to datetimes by to_datetime, then get diff and get first values per groups by isnull() chained with comparing if next values are higher like timedelta treshold:
df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y')
s = df.groupby('ID')['Date'].diff()
df = df[(s.isnull() | (s > pd.Timedelta(2, 'd')))]
print (df)
ID Date Event_Type
0 1 2019-01-01 A
2 2 2019-02-01 A
3 3 2019-02-01 A
Check solution with another data:
print (df)
ID Date Event_Type
0 1 01/01/2019 A
1 1 04/01/2019 B <-difference 3 days
2 2 02/01/2019 A
3 3 02/01/2019 A
df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y')
s = df.groupby('ID')['Date'].diff()
df = df[(s.isnull() | (s > pd.Timedelta(2, 'd')))]
print (df)
ID Date Event_Type
0 1 2019-01-01 A
1 1 2019-01-04 B
2 2 2019-01-02 A
3 3 2019-01-02 A
I have following pandas dataframe:
| id | LocTime |ZPos | XPos
datetime |
2017-01-02 00:14:39 |20421902611| 12531245409231| 0 | -6
2017-01-02 00:14:40 |30453291020| 28332479673070| 0 | -2
I want to convert datetime index to column of the data frame.
I tried df.reset_index(level=['datetime']) but the result does not change.
any idea?
Need assign output back or inplace=True parameter:
df = df.reset_index()
df.reset_index(inplace=True)
print (df)
datetime id LocalTime ZPosition XPosition
0 2017-01-02 00:14:39 10453190861 1483312478909238 0 -9
1 2017-01-02 00:14:40 10453191020 1483312479673076 0 -8
I have a dataframe that looks like:
import pandas as pd
import datetime as dt
df= pd.DataFrame({'date':['2017-12-31','2017-12-31'],'type':['Asset','Liab'],'Amount':[100,-100],'Maturity Date':['2019-01-02','2018-01-01']})
df
I am trying to build a roll-off profile by checking if the 'Maturity Date' is greater than a 'date' in the future. I am trying to achieve something like:
#First Month
df1=df[df['Maturity Date']>'2018-01-31']
df1['date']='2018-01-31'
#Second Month
df2=df[df['Maturity Date']>'2018-02-28']
df2['date']='2018-02-28'
#third Month
df3=df[df['Maturity Date']>'2018-03-31']
df3['date']='2018-02-31'
#first quarter
qf1=df[df['Maturity Date']>'2018-06-30']
qf1['date']='2018-06-30'
#concatenate
df=pd.concat([df,df1,df2,df3,qf1])
df
I was wondering if there is a way to :
Allow an arbitrary long number of dates without repeating code
I think you need numpy.tile for repeat indices and assign to new column, last filter by boolean indexing and sorting by sort_values:
d = '2017-12-31'
df['Maturity Date'] = pd.to_datetime(df['Maturity Date'])
#generate first month and next quarters
c1 = pd.date_range(d, periods=4, freq='M')
c2 = pd.date_range(c1[-1], periods=2, freq='Q')
#join together
c = c1.union(c2[1:])
#repeat rows be indexing repeated index
df1 = df.loc[np.tile(df.index, len(c))].copy()
#assign column by datetimes
df1['date'] = np.repeat(c, len(df))
#filter by boolean indexing
df1 = df1[df1['Maturity Date'] > df1['date']]
print (df1)
Amount Maturity Date date type
0 100 2019-01-02 2017-12-31 Asset
1 -100 2018-01-01 2017-12-31 Liab
0 100 2019-01-02 2018-01-31 Asset
0 100 2019-01-02 2018-02-28 Asset
0 100 2019-01-02 2018-03-31 Asset
0 100 2019-01-02 2018-06-30 Asset
You could use a nifty tool in the Pandas arsenal called
pd.merge_asof. It
works similarly to pd.merge, except that it matches on "nearest" keys rather
than equal keys. Furthermore, you can tell pd.merge_asof to look for nearest
keys in only the backward or forward direction.
To make things interesting (and help check that things are working properly), let's add another row to df:
df = pd.DataFrame({'date':['2017-12-31', '2017-12-31'],'type':['Asset', 'Asset'],'Amount':[100,200],'Maturity Date':['2019-01-02', '2018-03-15']})
for col in ['date', 'Maturity Date']:
df[col] = pd.to_datetime(df[col])
df = df.sort_values(by='Maturity Date')
print(df)
# Amount Maturity Date date type
# 1 200 2018-03-15 2017-12-31 Asset
# 0 100 2019-01-02 2017-12-31 Asset
Now define some new dates:
dates = (pd.date_range('2018-01-31', periods=3, freq='M')
.union(pd.date_range('2018-01-1', periods=2, freq='Q')))
result = pd.DataFrame({'date': dates})
# date
# 0 2018-01-31
# 1 2018-02-28
# 2 2018-03-31
# 3 2018-06-30
Now we can merge rows, matching nearest dates from result with Maturity Dates from df:
result = pd.merge_asof(result, df.drop('date', axis=1),
left_on='date', right_on='Maturity Date', direction='forward')
In this case we want to "match" dates with Maturity Dates which are greater
so we use direction='forward'.
Putting it all together:
import pandas as pd
df = pd.DataFrame({'date':['2017-12-31', '2017-12-31'],'type':['Asset', 'Asset'],'Amount':[100,200],'Maturity Date':['2019-01-02', '2018-03-15']})
for col in ['date', 'Maturity Date']:
df[col] = pd.to_datetime(df[col])
df = df.sort_values(by='Maturity Date')
dates = (pd.date_range('2018-01-31', periods=3, freq='M')
.union(pd.date_range('2018-01-1', periods=2, freq='Q')))
result = pd.DataFrame({'date': dates})
result = pd.merge_asof(result, df.drop('date', axis=1),
left_on='date', right_on='Maturity Date', direction='forward')
result = pd.concat([df, result], axis=0)
result = result.sort_values(by=['Maturity Date', 'date'])
print(result)
yields
Amount Maturity Date date type
1 200 2018-03-15 2017-12-31 Asset
0 200 2018-03-15 2018-01-31 Asset
1 200 2018-03-15 2018-02-28 Asset
0 100 2019-01-02 2017-12-31 Asset
2 100 2019-01-02 2018-03-31 Asset
3 100 2019-01-02 2018-06-30 Asset