如何根据熊猫的不同价值进行分组？
如何根据熊猫的不同价值进行分组？
提问于 2016-03-20 18:58:57
我有一个dataframe，我想根据分类变量和一系列值对其进行分组。您可能会把它看作是类似值的行(集群？)。例如：
df = pd.DataFrame({'symbol' : ['IP', 'IP', 'IP', 'IP', 'IP', 'IP', 'IP'],
'serie' : ['A', 'B', 'A', 'B', 'A', 'B', 'B'],
'strike' : [10, 10, 12, 13, 12, 13, 14],
'last' : [1, 2, 2.5, 3, 4.5, 5, 6],
'price' : [11, 11, 11, 11, 11, 11, 11],
'type' : ['call', 'put', 'put', 'put', 'call', 'put', 'call']})如果我用
grouped = df.groupby(['symbol', 'serie', 'strike'])我的部分问题已经得到解决，但我想结合更接近的罢工值，例如10和11、12和13等等。最好在%范围内。
回答 2
Stack Overflow用户
回答已采纳
发布于 2016-03-20 19:49:06
我猜OP想要按分类变量分组，然后是以间隔为单位的数值变量。在这种情况下，您可以使用np.digitize()。
smallest = np.min(df['strike'])
largest = np.max(df['strike'])
num_edges = 3
# np.digitize(input_array, bin_edges)
ind = np.digitize(df['strike'], np.linspace(smallest, largest, num_edges))那么ind应该是
array([1, 1, 2, 2, 2, 2, 3], dtype=int64)与绑定对应的
[10, 10, 12, 13, 12, 13, 14]带桶边
array([ 10., 12., 14.]) # == np.linspace(smallest, largest, num_edges)最后，按所需的所有列分组，但使用此额外的bin列。
df['binned_strike'] = ind
for grp in df.groupby(['symbol', 'serie', 'binned_strike']):
print "group key"
print grp[0]
print "group content"
print grp[1]
print "============="这个应该打印出来
group key
('IP', 'A', 1)
group content
last price serie strike symbol type binned_strike
0 1.0 11 A 10 IP call 1
=============
group key
('IP', 'A', 2)
group content
last price serie strike symbol type binned_strike
2 2.5 11 A 12 IP put 2
4 4.5 11 A 12 IP call 2
=============
group key
('IP', 'B', 1)
group content
last price serie strike symbol type binned_strike
1 2.0 11 B 10 IP put 1
=============
group key
('IP', 'B', 2)
group content
last price serie strike symbol type binned_strike
3 3.0 11 B 13 IP put 2
5 5.0 11 B 13 IP put 2
=============
group key
('IP', 'B', 3)
group content
last price serie strike symbol type binned_strike
6 6.0 11 B 14 IP call 3
=============Stack Overflow用户
发布于 2016-03-20 20:00:00
在groupy()的垃圾箱上做strike
使用pd.cut创建大量罢工数据，然后按该信息分组：
# Create DataFrame
df = pd.DataFrame({
'symbol' : ['IP', 'IP', 'IP', 'IP', 'IP', 'IP', 'IP'],
'serie' : ['A', 'B', 'A', 'B', 'A', 'B', 'B'],
'strike' : [10, 10, 12, 13, 12, 13, 14],
'last' : [1, 2, 2.5, 3, 4.5, 5, 6],
'price' : [11, 11, 11, 11, 11, 11, 11],
'type' : ['call', 'put', 'put', 'put', 'call', 'put', 'call']
})
# Create Bins (example three bins across data)
df['strikebins'] = pd.cut(df['strike'], bins=3)
print 'Binned DataFrame:'
print df
print
# Group these DataFrame
grouped = df.groupby(['symbol', 'serie', 'strikebins'])
# Do something with groups for example
gp_sum = grouped.sum()
print 'Grouped Sum (for example):'
print gp_sum
printBinned DataFrame:
last price serie strike symbol type strikebins
0 1.0 11 A 10 IP call (9.996, 11.333]
1 2.0 11 B 10 IP put (9.996, 11.333]
2 2.5 11 A 12 IP put (11.333, 12.667]
3 3.0 11 B 13 IP put (12.667, 14]
4 4.5 11 A 12 IP call (11.333, 12.667]
5 5.0 11 B 13 IP put (12.667, 14]
6 6.0 11 B 14 IP call (12.667, 14]
Grouped Sum (for example):
last price strike
symbol serie strikebins
IP A (9.996, 11.333] 1 11 10
(11.333, 12.667] 7 22 24
(12.667, 14] NaN NaN NaN
B (9.996, 11.333] 2 11 10
(11.333, 12.667] NaN NaN NaN
(12.667, 14] 14 33 40如果你想的话你可以用drop() strike，或者用范围的平均值代替strikebins .
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原文链接：
https://stackoverflow.com/questions/36118122
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