Pandas groupby
>>> emp.groupby(['dept', 'gender']).agg({'salary':'mean'}).round(-3)Pandas groupby
>>> emp.groupby(['dept', 'gender']).agg({'salary':'mean'}).round(-3)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)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 5Copyright © 2021 Codeinu
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