5
我有數值數據框架柱與蟒蛇大熊貓分級列
df['percentage'].head()
46.5
44.2
100.0
42.12
我想看到的列箱數:
bins = [0, 1, 5, 10, 25, 50, 100]
我如何能得到導致以bin與它的values count
:
[0, 1] bin amount
[1, 5] etc
[5, 10] etc
......
我有數值數據框架柱與蟒蛇大熊貓分級列
df['percentage'].head()
46.5
44.2
100.0
42.12
我想看到的列箱數:
bins = [0, 1, 5, 10, 25, 50, 100]
我如何能得到導致以bin與它的values count
:
[0, 1] bin amount
[1, 5] etc
[5, 10] etc
......
您可以使用:
bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
print (df)
percentage binned
0 46.50 (25, 50]
1 44.20 (25, 50]
2 100.00 (50, 100]
3 42.12 (25, 50]
bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df['binned'] = pd.cut(df['percentage'], bins=bins, labels=labels)
print (df)
percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = np.searchsorted(bins, df['percentage'].values)
print (df)
percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
...然後value_counts
或groupby
和聚集size
:
s = pd.cut(df['percentage'], bins=bins).value_counts()
print (s)
(25, 50] 3
(50, 100] 1
(10, 25] 0
(5, 10] 0
(1, 5] 0
(0, 1] 0
Name: percentage, dtype: int64
s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()
print (s)
percentage
(0, 1] 0
(1, 5] 0
(5, 10] 0
(10, 25] 0
(25, 50] 3
(50, 100] 1
dtype: int64
默認情況下cut
回報categorical
。
Series
像Series.value_counts()
這樣的方法將使用所有類別,即使某些類別不存在於數據中,operations in categorical。