I would like to calculate mode for each group of resampled rows in pandas dataframe. I try it like so:
import datetime
import pandas as pd
import numpy as np
from statistics import mode
date_times = pd.date_range(datetime.datetime(2012, 4, 5),
datetime.datetime(2013, 4, 5),
freq='D')
a = np.random.sample(date_times.size) * 10.0
frame = pd.DataFrame(data={'a': a},
index=date_times)
frame['b'] = np.random.randint(1, 3, frame.shape[0])
frame.resample("M").apply({'a':'sum', 'b':'mode'})
But it doesnt work.
I also try:
frame.resample("M").apply({'a':'sum', 'b':lambda x: mode(frame['b'])})
But I get wrong results. Any ideas?
Thanks.
In frame.resample("M").apply({'a':'sum', 'b':lambda x: mode(frame['b'])}) the lambda function is called once for each resampling group. x is assigned to a Series whose values are from the b column of the resampling group.
lambda x: mode(frame['b']) ignores x and simply returns the mode of frame['b'] -- the entire column.
Instead, you would want something like
frame.resample("M").apply({'a':'sum', 'b':lambda x: mode(x)})
However, this leads to a StatisticsError
StatisticsError: no unique mode; found 2 equally common values
since there is a resampling group with more than one most common value.
If you use scipy.stats.mode instead, then the smallest such most-common value is returned:
import datetime
import pandas as pd
import numpy as np
import scipy.stats as stats
date_times = pd.date_range(datetime.datetime(2012, 4, 5),
datetime.datetime(2013, 4, 5),
freq='D')
a = np.random.sample(date_times.size) * 10.0
frame = pd.DataFrame(data={'a': a}, index=date_times)
frame['b'] = np.random.randint(1, 3, frame.shape[0])
result = frame.resample("M").apply({'a':'sum', 'b':lambda x: stats.mode(x)[0]})
print(result)
yields
b a
2012-04-30 2 132.708704
2012-05-31 2 149.103439
2012-06-30 2 128.492203
2012-07-31 2 142.167672
2012-08-31 2 126.516689
2012-09-30 1 133.209314
2012-10-31 2 136.684212
2012-11-30 2 165.075150
2012-12-31 2 167.064212
2013-01-31 1 150.293293
2013-02-28 1 125.533830
2013-03-31 2 174.236113
2013-04-30 2 11.254136
If you want the largest most-common value, then, unfortunately, I don't know of any builtin function which does this for you. In this case you might have to compute a value_counts table:
In [89]: counts
Out[89]:
b counts
2012-04-30 3 11
2012-04-30 2 10
2012-04-30 1 5
2012-05-31 2 14
2012-05-31 1 9
2012-05-31 3 8
Then sort it in descending order by both counts and b value, group by the date and take the first value in each group:
import datetime as DT
import numpy as np
import scipy.stats as stats
import pandas as pd
np.random.seed(2018)
date_times = pd.date_range(DT.datetime(2012, 4, 5), DT.datetime(2013, 4, 5), freq='D')
N = date_times.size
a = np.random.sample(N) * 10.0
frame = pd.DataFrame(data={'a': a, 'b': np.random.randint(1, 4, N)}, index=date_times)
resampled = frame.resample("M")
sums = resampled['a'].sum()
counts = resampled['b'].value_counts()
counts.name = 'counts'
counts = counts.reset_index(level=1)
counts = counts.sort_values(by=['counts','b'],
ascending=[False,False])
result = counts.groupby(level=0).first()
yields
b counts
2012-04-30 3 11
2012-05-31 2 14
2012-06-30 3 12
2012-07-31 2 12
2012-08-31 2 11
2012-09-30 3 12
2012-10-31 2 13
2012-11-30 3 13
2012-12-31 2 14
2013-01-31 3 14
2013-02-28 1 10
2013-03-31 3 13
2013-04-30 3 2