python - pandas Series (pandas.Series.query()) 是否有查询方法或类似方法？

Question

pandas.DataFrame.query() 方法非常适合在加载或绘图时(预/后)过滤数据。它对于方法链特别方便。

我发现自己经常想将相同的逻辑应用于 pandas.Series，例如在完成诸如返回 pandas.Series 的 df.value_counts 之类的方法之后。

例子

假设有一个巨大的表格，其中包含 Player、Game、Points 列，我想绘制得分超过 14 乘以 3 的球员的直方图。我首先必须对每个玩家的分数求和 (groupby -> agg)，这将返回一个由 ~1000 名玩家组成的系列赛及其总分。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())

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()

方法链和查询方法有助于保持代码清晰，同时子集过滤很快就会变得困惑。

# just to make my point :)
series_bestplayers_under_100[series_prefiltered_under_100 > 0].shape

请帮我走出困境!谢谢

Answer 1

如果我理解正确，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

---

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

---

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