熊猫系列（pandas.Series.query（））是否有查询方法或类似方法？
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Is there a query method or similar for pandas Series (pandas.Series.query())?
提问by dmeu
The pandas.DataFrame.query()method is of great usage for (pre/post)-filtering data when loading or plotting. It comes particularly handy for method chaining.
该pandas.DataFrame.query()方法对于加载或绘图时的（前/后）过滤数据非常有用。它对于方法链特别方便。
I find myself often wanting to apply the same logic to a pandas.Series, e.g. after having done a method such as df.value_countswhich returns a pandas.Series.
我发现自己经常想对 a 应用相同的逻辑pandas.Series，例如在完成诸如df.value_countswhich 返回 a 之类的方法之后pandas.Series。
Example
例子
Lets assume there is a huge table with the columns Player, Game, Pointsand I want to plot a histogram of the players with more than 14 times 3 points. I first have to sum the points of each player (groupby -> agg) which will return a Series of ~1000 players and their overall points. Applying the .querylogic it would look something like this:
让我们假设有一个带有列的巨大表格，Player, Game, Points我想绘制超过 14 倍 3 分的玩家的直方图。我首先必须对每个玩家 ( groupby -> agg)的积分求和，这将返回一系列 ~1000 名玩家及其总分。应用.query逻辑它看起来像这样：
df = pd.DataFrame({
'Points': [random.choice([1,3]) for x in range(100)],
'Player': [random.choice(["A","B","C"]) for x in range(100)]})
(df
.query("Points == 3")
.Player.values_count()
.query("> 14")
.hist())
The only solutions I find force me to do an unnecessary assignment and break the method chaining:
我找到的唯一解决方案迫使我做一个不必要的分配并打破方法链：
(points_series = df
.query("Points == 3")
.groupby("Player").size()
points_series[points_series > 100].hist()
Method chaining as well as the query method help to keep the code legible meanwhile the subsetting-filtering can get messy quite quickly.
方法链和查询方法有助于保持代码清晰，同时子集过滤会很快变得混乱。
# just to make my point :)
series_bestplayers_under_100[series_prefiltered_under_100 > 0].shape
Please help me out of my dilemma! Thanks
请帮助我摆脱困境！谢谢
采纳答案by jezrael
IIUC you can add query("Points > 100"):
IIUC 你可以添加query("Points > 100")：
df = pd.DataFrame({'Points':[50,20,38,90,0, np.Inf],
'Player':['a','a','a','s','s','s']})
print (df)
Player Points
0 a 50.000000
1 a 20.000000
2 a 38.000000
3 s 90.000000
4 s 0.000000
5 s inf
points_series = df.query("Points < inf").groupby("Player").agg({"Points": "sum"})['Points']
print (points_series)
a = points_series[points_series > 100]
print (a)
Player
a 108.0
Name: Points, dtype: float64
points_series = df.query("Points < inf")
.groupby("Player")
.agg({"Points": "sum"})
.query("Points > 100")
print (points_series)
Points
Player
a 108.0
Another solution is Selection By Callable:
另一种解决方案是Selection By Callable：
points_series = df.query("Points < inf")
.groupby("Player")
.agg({"Points": "sum"})['Points']
.loc[lambda x: x > 100]
print (points_series)
Player
a 108.0
Name: Points, dtype: float64
Edited answer by edited question:
编辑问题的编辑答案：
np.random.seed(1234)
df = pd.DataFrame({
'Points': [np.random.choice([1,3]) for x in range(100)],
'Player': [np.random.choice(["A","B","C"]) for x in range(100)]})
print (df.query("Points == 3").Player.value_counts().loc[lambda x: x > 15])
C 19
B 16
Name: Player, dtype: int64
print (df.query("Points == 3").groupby("Player").size().loc[lambda x: x > 15])
Player
B 16
C 19
dtype: int64
回答by Martin
Why not convert from Series to DataFrame, do the querying, and then convert back.
为什么不从 Series 转换为 DataFrame，进行查询，然后再转换回来。
df["Points"] = df["Points"].to_frame().query('Points > 100')["Points"]
Here, .to_frame()converts to DataFrame, while the trailing ["Points"]converts to Series.
在这里，.to_frame()转换为 DataFrame，而尾随["Points"]转换为 Series。
The method .query()can then be used consistently whether or not the Pandas object has 1 or more columns.
.query()无论 Pandas 对象是否有 1 列或更多列，该方法都可以一致地使用。
回答by Ilya Prokin
Instead of query you can use pipe:
您可以使用而不是查询pipe：
s.pipe(lambda x: x[x>0]).pipe(lambda x: x[x<10])
