CSS

groupby where only

>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
...                           'foo', 'bar'],
...                    'B' : [1, 2, 3, 4, 5, 6],
...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> grouped.filter(lambda x: x['B'].mean() > 3.)
     A  B    C
1  bar  2  5.0
3  bar  4  1.0
5  bar  6  9.0

df groupby

df.groupby(by="a", dropna=False).sum()

groupby and list

In [1]: df = pd.DataFrame( {'a':['A','A','B','B','B','C'], 'b':[1,2,5,5,4,6]})
        df

Out[1]: 
   a  b
0  A  1
1  A  2
2  B  5
3  B  5
4  B  4
5  C  6

In [2]: df.groupby('a')['b'].apply(list)
Out[2]: 
a
A       [1, 2]
B    [5, 5, 4]
C          [6]
Name: b, dtype: object

In [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')
        df1
Out[3]: 
   a        new
0  A     [1, 2]
1  B  [5, 5, 4]
2  C        [6]

group by pandas

#calculate sum of sales grouped by month
df.groupby(df.date.dt.month)['sales'].sum()

date
1    34
2    44
3    31
Name: sales, dtype: int64

pandas groupby

# usage example
gb = df.groupby(["col1", "col2"])
counts = gb.size().to_frame(name="counts")
count
(
    counts.join(gb.agg({"col3": "mean"}).rename(columns={"col3": "col3_mean"}))
    .join(gb.agg({"col4": "median"}).rename(columns={"col4": "col4_median"}))
    .join(gb.agg({"col4": "min"}).rename(columns={"col4": "col4_min"}))
    .reset_index()
)

# to create dataframe
keys = np.array(
    [
        ["A", "B"],
        ["A", "B"],
        ["A", "B"],
        ["A", "B"],
        ["C", "D"],
        ["C", "D"],
        ["C", "D"],
        ["E", "F"],
        ["E", "F"],
        ["G", "H"],
    ]
)



df = pd.DataFrame(
    np.hstack([keys, np.random.randn(10, 4).round(2)]), columns=["col1", "col2", "col3", "col4", "col5", "col6"]
)
df[["col3", "col4", "col5", "col6"]] = df[["col3", "col4", "col5", "col6"]].astype(float)

group by dataframe

df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})

groupby in python

# car_sales:> it is dataframe
# "Make" is column, or feature name
# operation is mean
car_sales.groupby(["Make"]).mean()

Pandas groupby

>>> emp.groupby(['dept', 'gender']).agg({'salary':'mean'}).round(-3)

groupbycolumn

# import pandas
import pandas as pd
  
# create dataframe
df = pd.DataFrame({'Name': ['Mukul', 'Rohan', 'Mukul', 'Manoj',
                            'Kamal', 'Rohan', 'Robin'],
                     
                   'age': [22, 22, 21, 20, 21, 24, 20]})
  
# print dataframe
print(df)
  
# use count() and sort()
df = df.groupby(['Name'])['age'].count().reset_index(
  name='Count').sort_values(['Count'], ascending=False)
  
# print dataframe
print(df)

pandas group by to dataframe

In [21]: g1.add_suffix('_Count').reset_index()
Out[21]: 
      Name      City  City_Count  Name_Count
0    Alice   Seattle           1           1
1      Bob   Seattle           2           2
2  Mallory  Portland           2           2
3  Mallory   Seattle           1           1

groupby

spUtil.get('pps-list-modal', {title: c.data.editAllocations, 
  table: 'resource_allocation', 
  queryString: 'GROUPBYuser^resource_plan=' + c.data.sysId, 
  view: 'resource_portal_allocations' }).then(function(response) {
    var formModal = response;
    c.allocationListModal = response;
  });

groupby

$users = User::select('name')->groupBy('name')->get()->toArray() ;

groupby

function groupBy(array, keyFn) {
  return array.reduce((accumulator, value) => {
    const key = keyFn(value);
    if (!accumulator[key]) {
      accumulator[key] = [value];
    } else {
      accumulator[key] = [value];
    }
    return accumulator;
  }, {});
}