blocks|key|2407157|text|您必须在代码中更改此行|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2407158|new_points+=+point1+%2B+point2|code-block|syntax|javascript|2407159|到这个|2407160|new_points+=+point1+-+point2|2407161|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

You have to change this line in your code

<pre><code>new_points = point1 + point2
</code></pre>

to this 

<pre><code>new_points = point1 - point2
</code></pre>

blocks|key|2407183|text|以下是几个替代方案的timeit比较。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2407184|%7C+method+++++++++++++%7C+ms+per+loop+%7C
%7C--------------------%2B-------------%7C
%7C+alt2+++++++++++++++%7C++++++++2.36+%7C
%7C+using_concat+++++++%7C++++++++3.26+%7C
%7C+using_double_merge+%7C++++++++22.4+%7C
%7C+orig+++++++++++++++%7C++++++++22.6+%7C
%7C+alt++++++++++++++++%7C++++++++45.8+%7C|code-block|syntax|javascript|2407185|使用IPython生成timeit结果|2407186|In+[138]:+df+=+make_df(20)

In+[143]:+%25timeit+alt2(df)
100+loops,+best+of+3:+2.36+ms+per+loop
In+[140]:+%25timeit+orig(df)
10+loops,+best+of+3:+22.6+ms+per+loop
In+[142]:+%25timeit+alt(df)
10+loops,+best+of+3:+45.8+ms+per+loop
In+[169]:+%25timeit+using_double_merge(df)
10+loops,+best+of+3:+22.4+ms+per+loop
In+[170]:+%25timeit+using_concat(df)
100+loops,+best+of+3:+3.26+ms+per+loop|2407187|2407188|import+numpy+as+np
import+pandas+as+pd


def+alt(df):
++++df['const']+=+1
++++result+=+pd.merge(df,+df,+on='const',+how='outer')
++++result+=+result.loc[(result['colour_x']+!=+result['colour_y'])]

++++result['color']+=+result['colour_x']+%2B+'_'+%2B+result['colour_y']
++++result['points']+=+result['points_x']+-+result['points_y']
++++result+=+result[['color',+'points']]
++++return+result

def+alt2(df):
+++++points+=+np.add.outer(df['points'],+-df['points'])
+++++color+=+pd.MultiIndex.from_product([df['colour'],+df['colour']])
+++++mask+=+color.labels[0]+!=+color.labels[1]
+++++color+=+color.map('_'.join)
+++++result+=+pd.DataFrame({'points':points.ravel(),+'color':color})
+++++result+=+result.loc[mask]
+++++return+result

def+orig(df):
++++combos+=+[]
++++points+=+[]

++++for+i1+in+range(len(df)):
++++++++for+i2+in+range(len(df)):
++++++++++++colour_main+=+df['colour'].iloc[i1]
++++++++++++colour_secondary+=+df['colour'].iloc[i2]
++++++++++++if+colour_main+!=+colour_secondary:
++++++++++++++++combo+=+colour_main+%2B+"_"+%2B+colour_secondary

++++++++++++++++point1+=+df['points'].values[i1]
++++++++++++++++point2+=+df['points'].values[i2]
++++++++++++++++new_points+=+point1+-+point2

++++++++++++++++combos.append(combo)
++++++++++++++++points.append(new_points)

++++return+pd.DataFrame({'color':combos,+'points':points})

def+using_concat(df):
++++"""https://stackoverflow.com/a/51641085/190597+(RafaelC)"""
++++d+=+df.set_index('colour').to_dict()['points']
++++s+=+pd.Series(list(itertools.combinations(df.colour,+2)))
++++s+=+pd.concat([s,+s.transform(lambda+k:+k[::-1])])
++++v+=+s.map(lambda+k:+d[k[0]]+-+d[k[1]])
++++df2+=+pd.DataFrame({'comb':+s.str.get(0)%2B'_'+%2B+s.str.get(1),+'values':+v})
++++return+df2

def+using_double_merge(df):
++++"""https://stackoverflow.com/a/51641007/190597+(sacul)"""++++
++++new+=+(df.reindex(pd.MultiIndex.from_product([df.colour,+df.colour]))
+++++++++++.reset_index()
+++++++++++.drop(['colour',+'points'],+1)
+++++++++++.merge(df.set_index('colour'),+left_on='level_0',+right_index=True)
+++++++++++.merge(df.set_index('colour'),+left_on='level_1',+right_index=True))

++++new['points_y']+*=+-1
++++new['sum']+=+new.sum(axis=1)
++++new+=+new[new.level_0+!=+new.level_1].drop(['points_x',+'points_y'],+1)
++++new['colours']+=+new[['level_0',+'level_1']].apply(lambda+x:+'_'.join(x),1)
++++return+new[['colours',+'sum']]

def+make_df(N):
++++df+=+pd.DataFrame({'colour':+np.arange(N),+
+++++++++++++++++++++++'points':+np.random.randint(10,+size=N)})
++++df['colour']+=+df['colour'].astype(str)
++++return+df|2407189|2407190|alt2的主要思想是使用np.add_outer在df['points']之外构造一个加法表|2407191|In+[149]:+points+=+np.add.outer(df['points'],+-df['points'])
In+[151]:+points
Out[151]:+
array([[++0,++-9,+++4],
+++++++[++9,+++0,++13],
+++++++[+-4,+-13,+++0]])|2407192|ravel用于使数组成为一维的：|2407193|In+[152]:+points.ravel()
Out[152]:+array([++0,++-9,+++4,+++9,+++0,++13,++-4,+-13,+++0])|2407194|颜色组合是用pd.MultiIndex.from_product生成的|2407195|In+[153]:+color+=+pd.MultiIndex.from_product([df['colour'],+df['colour']])
In+[155]:+color+=+color.map('_'.join)
In+[156]:+color
Out[156]:+
Index(['red_red',+'red_yellow',+'red_black',+'yellow_red',+'yellow_yellow',
+++++++'yellow_black',+'black_red',+'black_yellow',+'black_black'],
++++++dtype='object')|2407196|生成掩码以删除重复项：|2407197|mask+=+color.labels[0]+!=+color.labels[1]|2407198|然后从这些部分生成result：|2407199|+result+=+pd.DataFrame({'points':points.ravel(),+'color':color})
+result+=+result.loc[mask]|2407200|2407201|在我的original+answer,+here中解释了alt背后的想法。|2407202|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/revisions/8760b788-6370-46ab-bfca-fc6c23ecd15d/view-source^0|A|6|0|0|2|7|B|6|0|0|0|0|0|0|4|C|C|P|C|0|0|0|5|0|0|6|Q|0|0|0|0|9|6|0|0|0|S|3|3|L|0|0^^$0|@$1|2|3|4|5|6|7|1N|8|@$9|1O|A|1P|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1Q|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1R|8|@$9|1S|A|1T|B|C]|$9|1U|A|1V|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|1W|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|1X|8|@]|D|@]|E|$]]|$1|P|3|Q|5|H|7|1Y|8|@]|D|@]|E|$I|J]]|$1|R|3|-4|5|6|7|1Z|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|20|8|@$9|21|A|22|B|C]|$9|23|A|24|B|C]|$9|25|A|26|B|C]]|D|@]|E|$]]|$1|U|3|V|5|H|7|27|8|@]|D|@]|E|$I|J]]|$1|W|3|X|5|6|7|28|8|@$9|29|A|2A|B|C]]|D|@]|E|$]]|$1|Y|3|Z|5|H|7|2B|8|@]|D|@]|E|$I|J]]|$1|10|3|11|5|6|7|2C|8|@$9|2D|A|2E|B|C]]|D|@]|E|$]]|$1|12|3|13|5|H|7|2F|8|@]|D|@]|E|$I|J]]|$1|14|3|15|5|6|7|2G|8|@]|D|@]|E|$]]|$1|16|3|17|5|H|7|2H|8|@]|D|@]|E|$I|J]]|$1|18|3|19|5|6|7|2I|8|@$9|2J|A|2K|B|C]]|D|@]|E|$]]|$1|1A|3|1B|5|H|7|2L|8|@]|D|@]|E|$I|J]]|$1|1C|3|-4|5|6|7|2M|8|@]|D|@]|E|$]]|$1|1D|3|1E|5|6|7|2N|8|@$9|2O|A|2P|B|C]]|D|@$9|2Q|A|2R|1|2S]]|E|$]]|$1|1F|3|-4|5|6|7|2T|8|@]|D|@]|E|$]]]|1G|$1H|$5|1I|1J|1K|E|$1L|1M]]]]

Here is a <code>timeit</code> comparison of a few alternatives.

<pre><code>| method | ms per loop |
|--------------------+-------------|
| alt2 | 2.36 |
| using_concat | 3.26 |
| using_double_merge | 22.4 |
| orig | 22.6 |
| alt | 45.8 |
</code></pre>

The <code>timeit</code> results were generated using <code>IPython</code>:

<pre><code>In [138]: df = make_df(20)

In [143]: %timeit alt2(df)
100 loops, best of 3: 2.36 ms per loop
In [140]: %timeit orig(df)
10 loops, best of 3: 22.6 ms per loop
In [142]: %timeit alt(df)
10 loops, best of 3: 45.8 ms per loop
In [169]: %timeit using_double_merge(df)
10 loops, best of 3: 22.4 ms per loop
In [170]: %timeit using_concat(df)
100 loops, best of 3: 3.26 ms per loop
</code></pre>

<hr>

<pre><code>import numpy as np
import pandas as pd


def alt(df):
 df['const'] = 1
 result = pd.merge(df, df, on='const', how='outer')
 result = result.loc[(result['colour_x'] != result['colour_y'])]

 result['color'] = result['colour_x'] + '_' + result['colour_y']
 result['points'] = result['points_x'] - result['points_y']
 result = result[['color', 'points']]
 return result

def alt2(df):
 points = np.add.outer(df['points'], -df['points'])
 color = pd.MultiIndex.from_product([df['colour'], df['colour']])
 mask = color.labels[0] != color.labels[1]
 color = color.map('_'.join)
 result = pd.DataFrame({'points':points.ravel(), 'color':color})
 result = result.loc[mask]
 return result

def orig(df):
 combos = []
 points = []

 for i1 in range(len(df)):
 for i2 in range(len(df)):
 colour_main = df['colour'].iloc[i1]
 colour_secondary = df['colour'].iloc[i2]
 if colour_main != colour_secondary:
 combo = colour_main + "_" + colour_secondary

 point1 = df['points'].values[i1]
 point2 = df['points'].values[i2]
 new_points = point1 - point2

 combos.append(combo)
 points.append(new_points)

 return pd.DataFrame({'color':combos, 'points':points})

def using_concat(df):
 """https://stackoverflow.com/a/51641085/190597 (RafaelC)"""
 d = df.set_index('colour').to_dict()['points']
 s = pd.Series(list(itertools.combinations(df.colour, 2)))
 s = pd.concat([s, s.transform(lambda k: k[::-1])])
 v = s.map(lambda k: d[k[0]] - d[k[1]])
 df2 = pd.DataFrame({'comb': s.str.get(0)+'_' + s.str.get(1), 'values': v})
 return df2

def using_double_merge(df):
 """https://stackoverflow.com/a/51641007/190597 (sacul)""" 
 new = (df.reindex(pd.MultiIndex.from_product([df.colour, df.colour]))
 .reset_index()
 .drop(['colour', 'points'], 1)
 .merge(df.set_index('colour'), left_on='level_0', right_index=True)
 .merge(df.set_index('colour'), left_on='level_1', right_index=True))

 new['points_y'] *= -1
 new['sum'] = new.sum(axis=1)
 new = new[new.level_0 != new.level_1].drop(['points_x', 'points_y'], 1)
 new['colours'] = new[['level_0', 'level_1']].apply(lambda x: '_'.join(x),1)
 return new[['colours', 'sum']]

def make_df(N):
 df = pd.DataFrame({'colour': np.arange(N), 
 'points': np.random.randint(10, size=N)})
 df['colour'] = df['colour'].astype(str)
 return df
</code></pre>

<hr>

The main idea in <code>alt2</code> is to use <code>np.add_outer</code> to construct an addition table
out of <code>df['points']</code>:

<pre><code>In [149]: points = np.add.outer(df['points'], -df['points'])
In [151]: points
Out[151]: 
array([[ 0, -9, 4],
 [ 9, 0, 13],
 [ -4, -13, 0]])
</code></pre>

<code>ravel</code> is used to make the array 1-dimensional:

<pre><code>In [152]: points.ravel()
Out[152]: array([ 0, -9, 4, 9, 0, 13, -4, -13, 0])
</code></pre>

and the color combinations are generated with <code>pd.MultiIndex.from_product</code>:

<pre><code>In [153]: color = pd.MultiIndex.from_product([df['colour'], df['colour']])
In [155]: color = color.map('_'.join)
In [156]: color
Out[156]: 
Index(['red_red', 'red_yellow', 'red_black', 'yellow_red', 'yellow_yellow',
 'yellow_black', 'black_red', 'black_yellow', 'black_black'],
 dtype='object')
</code></pre>

A mask is generated to remove duplicates:

<pre><code>mask = color.labels[0] != color.labels[1]
</code></pre>

and then the <code>result</code> is generated from these parts:

<pre><code> result = pd.DataFrame({'points':points.ravel(), 'color':color})
 result = result.loc[mask]
</code></pre>

<hr>

The idea behind <code>alt</code> is explained in my <a href="https://stackoverflow.com/revisions/8760b788-6370-46ab-bfca-fc6c23ecd15d/view-source">original answer, here</a>.

blocks|key|2407207|text|这有点冗长，但可以得到您想要的输出：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2407208|new+=+(df.reindex(pd.MultiIndex.from_product([df.colour,+df.colour]))
+++++++.reset_index()
+++++++.drop(['colour',+'points'],+1)
+++++++.merge(df.set_index('colour'),+left_on='level_0',+right_index=True)
+++++++.merge(df.set_index('colour'),+left_on='level_1',+right_index=True))


new['points_x']+*=+-1

new['sum']+=+new.sum(axis=1)

new+=+new[new.level_0+!=+new.level_1].drop(['points_x',+'points_y'],+1)

new['colours']+=+new[['level_0',+'level_1']].apply(lambda+x:+'_'.join(x),1)


>>>+new
++level_0+level_1++sum+++++++colours
3++yellow+++++red+++-9++++yellow_red
6+++black+++++red++++4+++++black_red
1+++++red++yellow++++9++++red_yellow
7+++black++yellow+++13++black_yellow
2+++++red+++black+++-4+++++red_black
5++yellow+++black++-13++yellow_black|code-block|syntax|javascript|2407209|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This is a bit long-winded, but gets you the output you want:

<pre><code>new = (df.reindex(pd.MultiIndex.from_product([df.colour, df.colour]))
 .reset_index()
 .drop(['colour', 'points'], 1)
 .merge(df.set_index('colour'), left_on='level_0', right_index=True)
 .merge(df.set_index('colour'), left_on='level_1', right_index=True))


new['points_x'] *= -1

new['sum'] = new.sum(axis=1)

new = new[new.level_0 != new.level_1].drop(['points_x', 'points_y'], 1)

new['colours'] = new[['level_0', 'level_1']].apply(lambda x: '_'.join(x),1)


&gt;&gt;&gt; new
 level_0 level_1 sum colours
3 yellow red -9 yellow_red
6 black red 4 black_red
1 red yellow 9 red_yellow
7 black yellow 13 black_yellow
2 red black -4 red_black
5 yellow black -13 yellow_black
</code></pre>

blocks|key|2407229|text|d+=+df.set_index('colour').to_dict()['points']
s+=+pd.Series(list(itertools.combinations(df.colour,+2)))
s+=+pd.concat([s,+s.transform(lambda+k:+k[::-1])])
v+=+s.map(lambda+k:+d[k[0]]+-+d[k[1]])
df2=+pd.DataFrame({'comb':+s.str.get(0)%2B'_'+%2B+s.str.get(1),+'values':+v})

++++comb+++++++++++++values
0+++red_yellow+++++++-9
1+++red_black++++++++4
2+++yellow_black+++++13
0+++yellow_red+++++++9
1+++black_red++++++++-4
2+++black_yellow++++-13|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|2407230|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>d = df.set_index('colour').to_dict()['points']
s = pd.Series(list(itertools.combinations(df.colour, 2)))
s = pd.concat([s, s.transform(lambda k: k[::-1])])
v = s.map(lambda k: d[k[0]] - d[k[1]])
df2= pd.DataFrame({'comb': s.str.get(0)+'_' + s.str.get(1), 'values': v})

 comb values
0 red_yellow -9
1 red_black 4
2 yellow_black 13
0 yellow_red 9
1 black_red -4
2 black_yellow -13
</code></pre>

I have a pandas dataframe which looks like this:

<pre><code> colour points
0 red 1
1 yellow 10
2 black -3
</code></pre>

Then I'm trying to do the following algorithm:

<pre><code>combos = []
points = []

for i1 in range(len(df)):
 for i2 in range(len(df)):

 colour_main = df['colour'].values[i1]
 colour_secondary = df['colour'].values[i2]
 combo = colour_main + "_" + colour_secondary

 point1 = df['points'].values[i1]
 point2 = df['points'].values[i2]
 new_points = point1 + point2

 combos.append(combo)
 points.append(new_points)

df_new = pd.DataFrame({'colours': combos,
 'points': points})

print(df_new)
</code></pre>

I want to get all combinations and sum points:

<ul>
<li>if the colour is used as main I want to sum his value</li>
<li>if the colour is used as a secondary I want to sum opposite value</li>
</ul>

Example:

<pre><code>red_yellow = 1 + (-10) = -9
red_black = 1 + ( +3) = 4
black_red = -3 + ( -1) = -4
</code></pre>

The output I currently get:

<pre><code> colours points
0 red_red 2
1 red_yellow 11
2 red_black -2
3 yellow_red 11
4 yellow_yellow 20
5 yellow_black 7
6 black_red -2
7 black_yellow 7
8 blac_kblack -6
</code></pre>

The output I'm looking for:

<pre><code>red_yellow -9
red_black 4
yellow_red 9
yellow_black 13
black_red -4
black_yellow -13
</code></pre>

I don't know how to apply my logic to this code, also I bet there is a more simplest way to get all combinations without doing two loops, but currently, that's the only thing that comes to my mind.

I would like to:

<ul>
<li>get deserved output</li>
<li>improve the performance in cases when we get like 20 input colours</li>
<li>remove duplicates like red_red</li>
</ul>

Python pandas dataframe get all combinations of column values?

我有一个熊猫数据帧，看起来像这样：        colour   points0         red         11      yellow        102       black        -3然后，我尝试执行以下算法：combos = []points = []for i1 in range(...

问Python pandas dataframe获取列值的所有组合？
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问Python pandas dataframe获取列值的所有组合？EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问Python pandas dataframe获取列值的所有组合？
EN