Python & Pandas – Group by day and count for each day

by

You can use dt.floor for convert to dates and then value_counts or groupby with size:

df = (pd.to_datetime(df['date & time of connection'])
       .dt.floor('d')
       .value_counts()
       .rename_axis('date')
       .reset_index(name="count"))
print (df)
        date  count
0 2017-06-23      6
1 2017-06-21      5
2 2017-06-19      3
3 2017-06-22      3
4 2017-06-20      2

Or:

s = pd.to_datetime(df['date & time of connection'])
df = s.groupby(s.dt.floor('d')).size().reset_index(name="count")
print (df)
  date & time of connection  count
0                2017-06-19      3
1                2017-06-20      2
2                2017-06-21      5
3                2017-06-22      3
4                2017-06-23      6

Timings:

np.random.seed(1542)

N = 220000
a = np.unique(np.random.randint(N, size=int(N/2)))
df = pd.DataFrame(pd.date_range('2000-01-01', freq='37T', periods=N)).drop(a)
df.columns = ['date & time of connection']
df['date & time of connection'] = df['date & time of connection'].dt.strftime('%d/%m/%Y %H:%M:%S')
print (df.head()) 

In [193]: %%timeit
     ...: df['date & time of connection']=pd.to_datetime(df['date & time of connection'])
     ...: df1 = df.groupby(by=df['date & time of connection'].dt.date).count()
     ...: 
539 ms ± 45.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [194]: %%timeit
     ...: df1 = (pd.to_datetime(df['date & time of connection'])
     ...:        .dt.floor('d')
     ...:        .value_counts()
     ...:        .rename_axis('date')
     ...:        .reset_index(name="count"))
     ...: 
12.4 ms ± 350 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [195]: %%timeit
     ...: s = pd.to_datetime(df['date & time of connection'])
     ...: df2 = s.groupby(s.dt.floor('d')).size().reset_index(name="count")
     ...: 
17.7 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

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