PYTHON

df groupby

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

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)

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;
  }, {});
}

python group by

df.groupby('group').assign(mean_var1 = lambda x: np.mean(x.var1)