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Python Pandas Counts using rolling time window


Python Pandas Counts using rolling time window

By : user2172436
Date : October 23 2020, 08:10 PM
Any of those help There is two check , one check the days diff, and another is check the amount always more than 100 which using all , then both situation satisfied we select the ID.
code :
s=df.groupby('customerId').agg({'Date':lambda x : (x.iloc[0]-x.iloc[-1]).days==-1,'Amount_Spent':lambda x : (x>100).all()}).all(1)
newdf=df.loc[df.customerId.isin(s.index),]
newdf
Out[1242]:
   customerId       Date  Amount_Spent
0         123 2018-01-01           500
2         123 2018-01-02           300
newdf.groupby('customerId').agg({'Date':['first','last'],'Amount_Spent':'sum'})
Out[1244]: 
                 Date            Amount_Spent
                first       last          sum
customerId                                   
123        2018-01-01 2018-01-02          800


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pandas rolling computation with window based on values instead of counts

pandas rolling computation with window based on values instead of counts


By : Kristoffer D
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I'm looking for a way to do something like the various rolling_* functions of pandas, but I want the window of the rolling computation to be defined by a range of values (say, a range of values of a column of the DataFrame), not by the number of rows in the window. , I think this does what you want:
code :
In [1]: df
Out[1]:
   RollBasis  ToRoll
0          1       1
1          1       4
2          1      -5
3          2       2
4          3      -4
5          5      -2
6          8       0
7         10     -13
8         12      -2
9         13      -5

In [2]: def f(x):
   ...:     ser = df.ToRoll[(df.RollBasis >= x) & (df.RollBasis < x+5)]
   ...:     return ser.sum()
In [3]: df['Rolled'] = df.RollBasis.apply(f)

In [4]: df
Out[4]:
   RollBasis  ToRoll  Rolled
0          1       1      -4
1          1       4      -4
2          1      -5      -4
3          2       2      -4
4          3      -4      -6
5          5      -2      -2
6          8       0     -15
7         10     -13     -20
8         12      -2      -7
9         13      -5      -5
In [1]: from pandas import *

In [2]: import io

In [3]: text = """\
   ...:    RollBasis  ToRoll
   ...: 0          1       1
   ...: 1          1       4
   ...: 2          1      -5
   ...: 3          2       2
   ...: 4          3      -4
   ...: 5          5      -2
   ...: 6          8       0
   ...: 7         10     -13
   ...: 8         12      -2
   ...: 9         13      -5
   ...: """

In [4]: df = read_csv(io.BytesIO(text), header=0, index_col=0, sep='\s+')
Rolling Product in PANDAS over 30-day time window

Rolling Product in PANDAS over 30-day time window


By : Alex Slack
Date : March 29 2020, 07:55 AM
To fix this issue Edited so that final values of BHAR are included in the main DataFrame.
code :
BHAR = pd.Series()

def bhar(arr):
    return np.cumprod(arr+1)[-1]

grouped = df.groupby('event_id')
    for name, group in grouped:
        BHAR = BHAR.append(pd.rolling_apply(group['ret'],10,bhar) -
                           pd.rolling_apply(group['Exp_Ret'],10,bhar))

df['BHAR'] = BHAR
pandas rolling how to retain the first time index of each time window

pandas rolling how to retain the first time index of each time window


By : Srinidhi
Date : March 29 2020, 07:55 AM
hop of those help? Sorry for all the confusion I have made. shift method works perfectly fine. It turns out that rolling actually keeps all indices and all we have to do is to shift back, no matter if the indices are regular or not. It seems that the pandas rolling method always keeps the last index of each time window. Example: , Idea 1
Hack by reversing the dataframe first, then back again...
code :
(lambda d: d.a.rank().rolling(3).corr(d.b.rank()).iloc[::-1])(df.iloc[::-1])

2017-01-01    0.891042
2017-01-02    0.838628
2017-01-03    0.960769
2017-01-04   -0.897918
2017-01-05   -0.996616
2017-01-06    0.327327
2017-01-07    0.443533
2017-01-08   -0.178538
2017-01-09         NaN
2017-01-10         NaN
Freq: D, dtype: float64
rolling_spearmanr.shift(-2)

2017-01-01    0.891042
2017-01-02    0.838628
2017-01-03    0.960769
2017-01-04   -0.897918
2017-01-05   -0.996616
2017-01-06    0.327327
2017-01-07    0.443533
2017-01-08   -0.178538
2017-01-09         NaN
2017-01-10         NaN
Freq: D, dtype: float64
unable to perform time based rolling operations window pandas? Always gives window must be an integer

unable to perform time based rolling operations window pandas? Always gives window must be an integer


By : user3699153
Date : March 29 2020, 07:55 AM
wish helps you I think you can create list of dictionaries first and pass to DataFrame constructor, convert column time to datetimes and then use rolling:
code :
out = [json.loads(line)["transaction"] for line in sys.stdin]
df = pd.DataFrame(out)
df['time'] = pd.to_datetime(df['time'])
df['new'] = df.rolling('2min', on='time')['amount'].count()
print (df)
    merchant  amount                      time  new
0  merchantA      90 2019-02-13 11:00:00+00:00  1.0
1  merchantA      90 2019-02-13 11:00:00+00:00  2.0
out = []
for line in sys.stdin:
    data = json.loads(line)
    out.append(data["transaction"])

df = pd.DataFrame(out)
df['time'] = pd.to_datetime(df['time'])

df['new'] = df.rolling('2min', on='time')['amount'].count()
Pandas finding max value in rolling window of time

Pandas finding max value in rolling window of time


By : Shoaib Kureshi
Date : March 29 2020, 07:55 AM
seems to work fine Here's a way with resample/rolling. I get a weird warning using pandas version 0.18.0 and python 3.5. I don't think it's a concern but not sure why it is generated.
This assumes index is 'timestamp', if not, precede the following with df = df.set_index('timestamp'):
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