1
2

select top 5 *
from df

1

df.head()

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2
3

select top 5 *
from df
where day = 'MON'

1
2
3

df[df.days=="MON"]
df.query("day=='MON'")
df.loc[df.day=="MON"]

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2
3

select top 5 *
from df
where day in ('MON', 'TUE')

1
2

df.query("day in (['MON','TUE'])")
df[df.day.isin(["MON", "TUE"])]

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2
3

select top 5 *
from df
where day like 'M%'

1
2

df.query("day.str.startswith('M)")
df[df.day.str.match(r'[Mm].')]

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2
3

select top 5 *
from df
where num between 5 and 6

1
2

df[df.num.between(5,6)]
df.query("5<=num<=6")

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2
3

select top 5 *
from df
where num is null

1
2

df[df.num.isna()]
df.query("num.isna()")

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2
3

select top 5 *
from df
where num >5 and size <6

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2
3

df[(df.num>5)&(df.size<6>)]
df.query("num>5 and size<6")
df.query("num>5 & size<6")

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2
3

select *
from df
order by no

1

df.sort_values('no')

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2
3

select *
from df
order by no desc, size

1
2

df.sort_values(['no', 'size'], ascending=[0,1])
df.sort_values(['no', 'size'], ascending=[False,True])

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2
3

select avg(no) as avg_no,
        stdev(no) as std_no
from df

1
2

df[["no"]].agg(["mean", "std"])
df.agg({'no':["mean", "std"]})

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2
3
4

select day,
       avg(no) as avg_no
from df
group by day

1

df.groupby("day")["no"].agg("mean")