在熊貓中對值進行分組value_counts（）

Question

我想從我的熊貓數據框中創建直方圖。我有1列，我保存百分比值。我使用了value_counts（），但是我有太多的百分比值。 示例：

0.752  1 
0.769  2 
0.800  1 
0.823  1 
      ... 
80.365  1 
84.000  1 
84.615  1 
85.000  10 
85.714  1 

我需要按照相同的比率對這些值進行分組。例如5％。 （0 - 4999，5000 - 9999時，...）我想這個結果：

（例）

0 - 4,999  24 
5 - 9,999  12 
10 - 14,999  30 
... 

Answer 1

您可以將您的數據通過pd.cut()方法的結果：

In [38]: df 
Out[38]: 
    value count 
0 0.752  1 
1 11.769  3 
2 22.800  4 
3 33.823  5 
4 55.365  1 
5 84.000  1 
6 84.615  1 
7 85.000  10 
8 99.714  1 

In [39]: df.groupby(pd.cut(df.value, bins=np.linspace(0, 100, 21)))['count'].sum().fillna(0) 
Out[39]: 
value 
(0, 5]  1.0 
(5, 10]  0.0 
(10, 15]  3.0 
(15, 20]  0.0 
(20, 25]  4.0 
(25, 30]  0.0 
(30, 35]  5.0 
(35, 40]  0.0 
(40, 45]  0.0 
(45, 50]  0.0 
(50, 55]  0.0 
(55, 60]  1.0 
(60, 65]  0.0 
(65, 70]  0.0 
(70, 75]  0.0 
(75, 80]  0.0 
(80, 85]  12.0 
(85, 90]  0.0 
(90, 95]  0.0 
(95, 100]  1.0 
Name: count, dtype: float64 

或者你可以將NaN的：

In [40]: df.groupby(pd.cut(df.value, bins=np.linspace(0, 100, 21)))['count'].sum().dropna() 
Out[40]: 
value 
(0, 5]  1.0 
(10, 15]  3.0 
(20, 25]  4.0 
(30, 35]  5.0 
(55, 60]  1.0 
(80, 85]  12.0 
(95, 100]  1.0 
Name: count, dtype: float64 

說明：

In [41]: pd.cut(df.value, bins=np.linspace(0, 100, 21)) 
Out[41]: 
0  (0, 5] 
1  (10, 15] 
2  (20, 25] 
3  (30, 35] 
4  (55, 60] 
5  (80, 85] 
6  (80, 85] 
7  (80, 85] 
8 (95, 100] 
Name: value, dtype: category 
Categories (20, object): [(0, 5] < (5, 10] < (10, 15] < (15, 20] ... (80, 85] < (85, 90] < (90, 95] < (95, 100]]