Pandas group by

Pandas groupby

data.groupby('month', as_index=False).agg({"duration": "sum"})

Source: shanelynn.ie

Df groupby loop

grouped = df.groupby('A')

for name, group in grouped:
    ...

Source: stackoverflow.com

Pandas groupby

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

Source: medium.com

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)

Source: stackoverflow.com

Pandas group by

data.groupby('amount', as_index=False).agg({"duration": "sum"})

Groupby

df['frequency'] = df['county'].map(df['county'].value_counts())

    county  frequency
1   N       5
2   N       5
3   C       1
4   N       5
5   S       1
6   N       5
7   N       5

-1

Source: stackoverflow.com

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