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

Question

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.

I find myself often wanting to apply the same logic to a pandas.Series , eg after having done a method such as df.value_counts which returns a pandas.Series .

Example

Lets assume there is a huge table with the columns Player, Game, Points and 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 .query logic it would look something like this:

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:

(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.

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

Please help me out of my dilemma! Thanks

Answer 1

IIUC you can add 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 :

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:

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

Answer 2

Why not convert from Series to DataFrame, do the querying, and then convert back.

df["Points"] = df["Points"].to_frame().query('Points > 100')["Points"]

Here, .to_frame() converts to DataFrame, while the trailing ["Points"] converts to Series.

The method .query() can then be used consistently whether or not the Pandas object has 1 or more columns.

Answer 3

而不是查询，您可以使用pipe ：

s.pipe(lambda x: x[x>0]).pipe(lambda x: x[x<10])