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How to get all data if day is less than or equal to 5th of every month in pandas python


How to get all data if day is less than or equal to 5th of every month in pandas python

By : kavita
Date : November 21 2020, 04:01 AM
Any of those help You can do this: First you need to convert the Date column into Pandas Datetime:
code :
In [2612]: Salary.date = pd.to_datetime(Salary.date, format="%d-%m-%y")
In [2632]: Salary
Out[2632]: 
        Date   Salary
0 2018-07-07  15300.0
1 2018-08-03  14783.0
2 2018-09-04  16249.0
3 2018-10-05  14448.0
4 2018-11-06  15663.0

In [2633]: Salary[Salary['date'].dt.day <= 5].groupby('date')['Salary'].sum()
Out[2633]: 
Date
2018-08-03    14783.0
2018-09-04    16249.0
2018-10-05    14448.0


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python split a pandas data frame by week or month and group the data based on these sp

python split a pandas data frame by week or month and group the data based on these sp


By : Caio Ponce de Leon
Date : March 29 2020, 07:55 AM
should help you out Perhaps group by CostCentre first, then use Series/DataFrame resample()?
code :
In [72]: centers = {}

In [73]: for center, idx in df.groupby("CostCentre").groups.iteritems():
   ....:     timediff = df.ix[idx].set_index("Date")['TimeDifference']
   ....:     centers[center] = timediff.resample("W", how=sum)

In [77]: pd.concat(centers, names=['CostCentre'])
Out[77]: 
CostCentre  Date      
0           2012-09-09         0
            2012-09-16     89522
            2012-09-23         6
            2012-09-30       161
2073        2012-09-09    141208
            2012-09-16    113024
            2012-09-23    169599
            2012-09-30    170780
6078        2012-09-09    171481
            2012-09-16    160871
            2012-09-23    153976
            2012-09-30    122972
In [28]: df = pd.read_clipboard(sep=' +', parse_dates=True, index_col=0,
   ....:                        dayfirst=True)

In [30]: df.head()
Out[30]: 
              CostCentre  TimeDifference
DateOccurred                            
2012-09-03          2073           28138
2012-09-03          6078           34844
2012-09-03          8273           31215
2012-09-03          8367           28160
2012-09-03          8959           32037
In [37]: x = df.groupby("CostCentre").apply(lambda df: 
   ....:         df['TimeDifference'].resample("W", how=sum))

In [38]: x.head(12)
Out[38]: 
CostCentre  DateOccurred
0           2012-09-09           0
            2012-09-16       89522
            2012-09-23           6
            2012-09-30         161
2073        2012-09-09      141208
            2012-09-16      113024
            2012-09-23      169599
            2012-09-30      170780
6078        2012-09-09      171481
            2012-09-16      160871
            2012-09-23      153976
            2012-09-30      122972
Grouping daily data by month in python/pandas and then normalizing

Grouping daily data by month in python/pandas and then normalizing


By : Prim
Date : March 29 2020, 07:55 AM
may help you . I have the table below in a Pandas DataFrame: , If I understand you correctly:
For (1) do this:
code :
In [179]: string = Series(np.random.choice(df.string.values, size=100), name='string')

In [180]: visits = Series(poisson(1000, size=100), name='date')

In [181]: date = Series(np.random.choice([df.date[0], now(), Timestamp('1/1/2001'), Timestamp('11/15/2001'), Timestamp('12/1/01'), Timestamp('5/1/01')], size=100), dtype='datetime64[ns]', name='date')

In [182]: df = DataFrame({'string': string, 'visits': visits, 'date': date})

In [183]: df.head()
Out[183]:
                 date   string  visits
0 2001-11-15 00:00:00  current     997
1 2001-11-15 00:00:00  current     974
2 2012-10-02 00:00:00     stem     982
3 2001-12-01 00:00:00     stem     984
4 2001-01-01 00:00:00  current     989

In [186]: resamp = df.set_index('date').groupby('string').resample('M', how='sum')

In [187]: resamp.head()
Out[187]:
                    visits
string  date
current 2001-01-31    2996
        2001-02-28     NaN
        2001-03-31     NaN
        2001-04-30     NaN
        2001-05-31    3016
In [188]: g = resamp.groupby(level='date').apply(lambda x: x / x.sum())

In [189]: g.head()
Out[189]:
                    visits
string  date
current 2001-01-31   0.177
        2001-02-28     NaN
        2001-03-31     NaN
        2001-04-30     NaN
        2001-05-31   0.188
In [176]: h = g.sortlevel('date').head()

In [177]: h
Out[177]:
                      visits
string    date
current   2001-01-31   0.077
molecular 2001-01-31   0.228
neuron    2001-01-31   0.073
nucleus   2001-01-31   0.234
stem      2001-01-31   0.388

In [178]: h.sum()
Out[178]:
visits    1
dtype: float64
In [196]: resamp.dropna()
Out[196]:
                      visits
string    date
current   2001-01-31    2996
          2001-05-31    3016
          2001-11-30    5959
          2001-12-31    3998
          2013-09-30    1077
molecular 2001-01-31    3984
          2001-05-31    1911
          2001-11-30    3054
          2001-12-31    1020
          2012-10-31     977
          2013-09-30    1947
neuron    2001-01-31    3961
          2001-05-31    2069
          2001-11-30    5010
          2001-12-31    2065
          2012-10-31    6973
          2013-09-30     994
nucleus   2001-01-31    3060
          2001-05-31    3035
          2001-11-30    2924
          2001-12-31    4144
          2012-10-31    2004
          2013-09-30    7881
stem      2001-01-31    2911
          2001-05-31    5994
          2001-11-30    6072
          2001-12-31    4916
          2012-10-31    1991
          2013-09-30    3977

In [197]: resamp.dropna().reset_index()
Out[197]:
       string                date  visits
0     current 2001-01-31 00:00:00    2996
1     current 2001-05-31 00:00:00    3016
2     current 2001-11-30 00:00:00    5959
3     current 2001-12-31 00:00:00    3998
4     current 2013-09-30 00:00:00    1077
5   molecular 2001-01-31 00:00:00    3984
6   molecular 2001-05-31 00:00:00    1911
7   molecular 2001-11-30 00:00:00    3054
8   molecular 2001-12-31 00:00:00    1020
9   molecular 2012-10-31 00:00:00     977
10  molecular 2013-09-30 00:00:00    1947
11     neuron 2001-01-31 00:00:00    3961
12     neuron 2001-05-31 00:00:00    2069
13     neuron 2001-11-30 00:00:00    5010
14     neuron 2001-12-31 00:00:00    2065
15     neuron 2012-10-31 00:00:00    6973
16     neuron 2013-09-30 00:00:00     994
17    nucleus 2001-01-31 00:00:00    3060
18    nucleus 2001-05-31 00:00:00    3035
19    nucleus 2001-11-30 00:00:00    2924
20    nucleus 2001-12-31 00:00:00    4144
21    nucleus 2012-10-31 00:00:00    2004
22    nucleus 2013-09-30 00:00:00    7881
23       stem 2001-01-31 00:00:00    2911
24       stem 2001-05-31 00:00:00    5994
25       stem 2001-11-30 00:00:00    6072
26       stem 2001-12-31 00:00:00    4916
27       stem 2012-10-31 00:00:00    1991
28       stem 2013-09-30 00:00:00    3977
In [198]: g.dropna()
Out[198]:
                      visits
string    date
current   2001-01-31   0.177
          2001-05-31   0.188
          2001-11-30   0.259
          2001-12-31   0.248
          2013-09-30   0.068
molecular 2001-01-31   0.236
          2001-05-31   0.119
          2001-11-30   0.133
          2001-12-31   0.063
          2012-10-31   0.082
          2013-09-30   0.123
neuron    2001-01-31   0.234
          2001-05-31   0.129
          2001-11-30   0.218
          2001-12-31   0.128
          2012-10-31   0.584
          2013-09-30   0.063
nucleus   2001-01-31   0.181
          2001-05-31   0.189
          2001-11-30   0.127
          2001-12-31   0.257
          2012-10-31   0.168
          2013-09-30   0.496
stem      2001-01-31   0.172
          2001-05-31   0.374
          2001-11-30   0.264
          2001-12-31   0.305
          2012-10-31   0.167
          2013-09-30   0.251

In [199]: g.dropna().reset_index()
Out[199]:
       string                date  visits
0     current 2001-01-31 00:00:00   0.177
1     current 2001-05-31 00:00:00   0.188
2     current 2001-11-30 00:00:00   0.259
3     current 2001-12-31 00:00:00   0.248
4     current 2013-09-30 00:00:00   0.068
5   molecular 2001-01-31 00:00:00   0.236
6   molecular 2001-05-31 00:00:00   0.119
7   molecular 2001-11-30 00:00:00   0.133
8   molecular 2001-12-31 00:00:00   0.063
9   molecular 2012-10-31 00:00:00   0.082
10  molecular 2013-09-30 00:00:00   0.123
11     neuron 2001-01-31 00:00:00   0.234
12     neuron 2001-05-31 00:00:00   0.129
13     neuron 2001-11-30 00:00:00   0.218
14     neuron 2001-12-31 00:00:00   0.128
15     neuron 2012-10-31 00:00:00   0.584
16     neuron 2013-09-30 00:00:00   0.063
17    nucleus 2001-01-31 00:00:00   0.181
18    nucleus 2001-05-31 00:00:00   0.189
19    nucleus 2001-11-30 00:00:00   0.127
20    nucleus 2001-12-31 00:00:00   0.257
21    nucleus 2012-10-31 00:00:00   0.168
22    nucleus 2013-09-30 00:00:00   0.496
23       stem 2001-01-31 00:00:00   0.172
24       stem 2001-05-31 00:00:00   0.374
25       stem 2001-11-30 00:00:00   0.264
26       stem 2001-12-31 00:00:00   0.305
27       stem 2012-10-31 00:00:00   0.167
28       stem 2013-09-30 00:00:00   0.251
In [210]: g.dropna().reset_index().reindex(columns=['visits', 'string', 'date'])
Out[210]:
    visits     string                date
0    0.177    current 2001-01-31 00:00:00
1    0.188    current 2001-05-31 00:00:00
2    0.259    current 2001-11-30 00:00:00
3    0.248    current 2001-12-31 00:00:00
4    0.068    current 2013-09-30 00:00:00
5    0.236  molecular 2001-01-31 00:00:00
6    0.119  molecular 2001-05-31 00:00:00
7    0.133  molecular 2001-11-30 00:00:00
8    0.063  molecular 2001-12-31 00:00:00
9    0.082  molecular 2012-10-31 00:00:00
10   0.123  molecular 2013-09-30 00:00:00
11   0.234     neuron 2001-01-31 00:00:00
12   0.129     neuron 2001-05-31 00:00:00
13   0.218     neuron 2001-11-30 00:00:00
14   0.128     neuron 2001-12-31 00:00:00
15   0.584     neuron 2012-10-31 00:00:00
16   0.063     neuron 2013-09-30 00:00:00
17   0.181    nucleus 2001-01-31 00:00:00
18   0.189    nucleus 2001-05-31 00:00:00
19   0.127    nucleus 2001-11-30 00:00:00
20   0.257    nucleus 2001-12-31 00:00:00
21   0.168    nucleus 2012-10-31 00:00:00
22   0.496    nucleus 2013-09-30 00:00:00
23   0.172       stem 2001-01-31 00:00:00
24   0.374       stem 2001-05-31 00:00:00
25   0.264       stem 2001-11-30 00:00:00
26   0.305       stem 2001-12-31 00:00:00
27   0.167       stem 2012-10-31 00:00:00
28   0.251       stem 2013-09-30 00:00:00
python pandas: trying to reference data from another column's previous month end

python pandas: trying to reference data from another column's previous month end


By : sulaiman
Date : March 29 2020, 07:55 AM
help you fix your problem After you set the values for is_month_end==True with df['SD'], you can full NAs with ffill methiod - which forward fills the values.
code :
In [10]: df.ix[df.index.is_month_end==True, 'SD.prevmo'] = df['SD']
In [11]: df['SD.prevmo'].fillna(method='ffill')
Out[11]:
Date
2000-02-29    0.0312
2000-03-01    0.0312
2000-03-02    0.0312
2000-03-03    0.0312
2000-03-28    0.0312
2000-03-29    0.0312
2000-03-30    0.0312
2000-03-31    0.0556
2000-04-03    0.0556
2000-04-04    0.0556
2000-04-05    0.0556
Name: SD.prevmo, dtype: float64
Extract data from starting month and year to end month and year not working python pandas

Extract data from starting month and year to end month and year not working python pandas


By : user3547597
Date : March 29 2020, 07:55 AM
To fix the issue you can do df , Change your loops to
code :
for y in range(2010, 2015):
    for x in range(1, 13):
        df2 = df[df["order_date"].dt.month.eq(x) & df["order_date"].dt.year.eq(y)]
    print("Data for period", x, y, "is\n", df2)

Sorting data by day and month (ignoring year) python pandas

Sorting data by day and month (ignoring year) python pandas


By : Bernie
Date : October 13 2020, 08:00 PM
fixed the issue. Will look into that further I found many questions similar to mine, but none of them answer it exactly (this one comes closest, but it focusses on ruby). , On way from argsort
code :
yourdf=df.loc[df.Date.dt.strftime('%m%d').astype(int).argsort()]
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