datatable.median()

datatable.

median

(

cols

)

sourcetests

Calculate the median value for each column from cols.

Parameters

cols

FExpr

Input columns.

return

Expr

f-expression having one row, and the same names and number of columns as in cols. The column stypes are float32 for float32 columns, and float64 for all the other numeric types.

except

TypeError

The exception is raised when one of the columns from cols has a non-numeric type.

Examples

from datatable import dt, f, by df = dt.Frame({'A': [1, 1, 2, 1, 2], 'B': [None, 2, 3, 4, 5], 'C': [1, 2, 1, 1, 2]}) df

	A	B	C
	int32	int32	int32
0	1	NA	1
1	1	2	2
2	2	3	1
3	1	4	1
4	2	5	2

5 rows × 3 columns

Get the median from column A:

df[:, dt.median(f.A)]

	A
	float64
0	1

1 row × 1 column

Get the median of multiple columns:

df[:, dt.median([f.A, f.B])]

	A	B
	float64	float64
0	1	3.5

1 row × 2 columns

Same as above, but more convenient:

df[:, dt.median(f[:2])]

	A	B
	float64	float64
0	1	3.5

1 row × 2 columns

You can pass in a dictionary with new column names:

df[:, dt.median({"A_median": f.A, "C_mid": f.C})]

	A_median	C_mid
	float64	float64
0	1	1

1 row × 2 columns

In the presence of by(), it returns the median of each column per group:

df[:, dt.median({"A_median": f.A, "B_median": f.B}), by("C")]

	C	A_median	B_median
	int32	float64	float64
0	1	1	3.5
1	2	1.5	3.5

2 rows × 3 columns

See Also

• mean() – function to calculate mean values.

• sd() – function to calculate standard deviation.