我是Python的新手（使用Anaconda w / Python v3.4.3）并且无法在任何地方找到这个答案，但它似乎非常重要，我必须以错误的方式解决它。
import pandas as pd
url = 'https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv'
tips = pd.read_csv(url)
tips.head(5)
Out[1]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
我想选择day组至少有50条记录的记录。
sel_days = tips.groupby("day").size() > 50
sel_days
Out[2]:
day
Fri False
Sat True
Sun True
Thur True
dtype: bool
我看到这是一个系列，但似乎无法弄清楚如何生成一个布尔序列来从原始集tips中选择行。
type(sel_days)
Out[3]: pandas.core.series.Series
print(x in sel_days for x in tips["day"])
<generator object <genexpr> at 0x0000000007DBDFC0>
我该怎么做？
答案 0 :(得分：4)
您想要filter：
In [22]:
tips.groupby('day').filter(lambda x: len(x) > 50)
Out[22]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
5 25.29 4.71 Male No Sun Dinner 4
6 8.77 2.00 Male No Sun Dinner 2
7 26.88 3.12 Male No Sun Dinner 4
8 15.04 1.96 Male No Sun Dinner 2
9 14.78 3.23 Male No Sun Dinner 2
10 10.27 1.71 Male No Sun Dinner 2
11 35.26 5.00 Female No Sun Dinner 4
12 15.42 1.57 Male No Sun Dinner 2
13 18.43 3.00 Male No Sun Dinner 4
14 14.83 3.02 Female No Sun Dinner 2
15 21.58 3.92 Male No Sun Dinner 2
16 10.33 1.67 Female No Sun Dinner 3
17 16.29 3.71 Male No Sun Dinner 3
18 16.97 3.50 Female No Sun Dinner 3
19 20.65 3.35 Male No Sat Dinner 3
20 17.92 4.08 Male No Sat Dinner 2
21 20.29 2.75 Female No Sat Dinner 2
22 15.77 2.23 Female No Sat Dinner 2
23 39.42 7.58 Male No Sat Dinner 4
24 19.82 3.18 Male No Sat Dinner 2
25 17.81 2.34 Male No Sat Dinner 4
26 13.37 2.00 Male No Sat Dinner 2
27 12.69 2.00 Male No Sat Dinner 2
28 21.70 4.30 Male No Sat Dinner 2
29 19.65 3.00 Female No Sat Dinner 2
.. ... ... ... ... ... ... ...
207 38.73 3.00 Male Yes Sat Dinner 4
208 24.27 2.03 Male Yes Sat Dinner 2
209 12.76 2.23 Female Yes Sat Dinner 2
210 30.06 2.00 Male Yes Sat Dinner 3
211 25.89 5.16 Male Yes Sat Dinner 4
212 48.33 9.00 Male No Sat Dinner 4
213 13.27 2.50 Female Yes Sat Dinner 2
214 28.17 6.50 Female Yes Sat Dinner 3
215 12.90 1.10 Female Yes Sat Dinner 2
216 28.15 3.00 Male Yes Sat Dinner 5
217 11.59 1.50 Male Yes Sat Dinner 2
218 7.74 1.44 Male Yes Sat Dinner 2
219 30.14 3.09 Female Yes Sat Dinner 4
227 20.45 3.00 Male No Sat Dinner 4
228 13.28 2.72 Male No Sat Dinner 2
229 22.12 2.88 Female Yes Sat Dinner 2
230 24.01 2.00 Male Yes Sat Dinner 4
231 15.69 3.00 Male Yes Sat Dinner 3
232 11.61 3.39 Male No Sat Dinner 2
233 10.77 1.47 Male No Sat Dinner 2
234 15.53 3.00 Male Yes Sat Dinner 2
235 10.07 1.25 Male No Sat Dinner 2
236 12.60 1.00 Male Yes Sat Dinner 2
237 32.83 1.17 Male Yes Sat Dinner 2
238 35.83 4.67 Female No Sat Dinner 3
239 29.03 5.92 Male No Sat Dinner 3
240 27.18 2.00 Female Yes Sat Dinner 2
241 22.67 2.00 Male Yes Sat Dinner 2
242 17.82 1.75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2
[225 rows x 7 columns]
答案 1 :(得分：0)
我会在映射布尔掩码的tips数据帧中添加一个新列：
tips['mask'] = tips['day'].map(sel_days)
然后只选择真值：
tips = tips[tips['mask']]