df = DataFrame(
[[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
columns=["A", "B", "C"],
)
gb = df.groupby("A", as_index=False)
print(gb.describe())
# A ... B
# count mean std min 25% 50% 75% ... mean std min 25% 50% 75% max
# 0 2.0 1.0 0.0 1.0 1.0 1.0 1.0 ... 2.0 NaN 2.0 2.0 2.0 2.0 2.0
# 1 1.0 3.0 NaN 3.0 3.0 3.0 3.0 ... NaN NaN NaN NaN NaN NaN NaN
Instead, column A should just have the groupers
A B
count mean std min 25% 50% 75% max
0 1 1.0 2.0 NaN 2.0 2.0 2.0 2.0 2.0
1 3 0.0 NaN NaN NaN NaN NaN NaN NaN
We get different, but also incorrect, output for SeriesGroupBy:
df = DataFrame(
{
"foo1": ["one", "two", "two", "three", "one", "two"],
"foo2": [1, 2, 4, 4, 5, 6],
}
)
gb = df.groupby("foo1", as_index=False)['foo2']
print(gb.describe())
# foo1 0 one
# 1 three
# 2 two
# count 0 2.0
# 1 1.0
# 2 3.0
# mean 0 3.0
# 1 4.0
# 2 4.0
# std 0 2.828427
# 1 NaN
# 2 2.0
# min 0 1.0
# 1 4.0
# 2 2.0
# 25% 0 2.0
# 1 4.0
# 2 3.0
# 50% 0 3.0
# 1 4.0
# 2 4.0
# 75% 0 4.0
# 1 4.0
# 2 5.0
# max 0 5.0
# 1 4.0
# 2 6.0
# dtype: object
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