How would I group by unique values that are in a list form?
If I wanted to get the mean of the past 2 values based on column id, I would do the following:
df['rolling_mean_2'] = df.groupby('id').apply(lambda x: x.rolling(2, min_periods=2).mean())
>> id value rolling_mean_2
0 b 1 NaN
1 b 3 2
2 d 5 NaN
3 d 7 6
Right, straightforward.
Ok, now let's say my id are in a list form with 4 unique values (a, b, c, d)
x = [{'id': ['a','b','d'], 'value':1},
{'id': ['b','a','d'], 'value':3},
{'id': ['b','a','d'], 'value':5},
{'id': ['a','b','c'], 'value':7}]
df = pd.DataFrame(x)
Now, how would I get the mean from the past 2 values (incl. current row) based on unique value that contains in the list? Thus, my expected output would be as follows:
I'm only going to use variable a and d to keep tidiness and simplicity.
>> id value a_rolling_mean_2 d_rolling_mean_2
0 [a, b, d] 1 NaN NaN
1 [b, a, d] 3 2 2
2 [b, a, d] 5 4 4
3 [a, b, c] 7 6 NaN
python python-3.x pandas lambda pandas-groupby
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
add a comment |
If I wanted to get the mean of the past 2 values based on column id, I would do the following:
df['rolling_mean_2'] = df.groupby('id').apply(lambda x: x.rolling(2, min_periods=2).mean())
>> id value rolling_mean_2
0 b 1 NaN
1 b 3 2
2 d 5 NaN
3 d 7 6
Right, straightforward.
Ok, now let's say my id are in a list form with 4 unique values (a, b, c, d)
x = [{'id': ['a','b','d'], 'value':1},
{'id': ['b','a','d'], 'value':3},
{'id': ['b','a','d'], 'value':5},
{'id': ['a','b','c'], 'value':7}]
df = pd.DataFrame(x)
Now, how would I get the mean from the past 2 values (incl. current row) based on unique value that contains in the list? Thus, my expected output would be as follows:
I'm only going to use variable a and d to keep tidiness and simplicity.
>> id value a_rolling_mean_2 d_rolling_mean_2
0 [a, b, d] 1 NaN NaN
1 [b, a, d] 3 2 2
2 [b, a, d] 5 4 4
3 [a, b, c] 7 6 NaN
python python-3.x pandas lambda pandas-groupby
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
add a comment |
If I wanted to get the mean of the past 2 values based on column id, I would do the following:
df['rolling_mean_2'] = df.groupby('id').apply(lambda x: x.rolling(2, min_periods=2).mean())
>> id value rolling_mean_2
0 b 1 NaN
1 b 3 2
2 d 5 NaN
3 d 7 6
Right, straightforward.
Ok, now let's say my id are in a list form with 4 unique values (a, b, c, d)
x = [{'id': ['a','b','d'], 'value':1},
{'id': ['b','a','d'], 'value':3},
{'id': ['b','a','d'], 'value':5},
{'id': ['a','b','c'], 'value':7}]
df = pd.DataFrame(x)
Now, how would I get the mean from the past 2 values (incl. current row) based on unique value that contains in the list? Thus, my expected output would be as follows:
I'm only going to use variable a and d to keep tidiness and simplicity.
>> id value a_rolling_mean_2 d_rolling_mean_2
0 [a, b, d] 1 NaN NaN
1 [b, a, d] 3 2 2
2 [b, a, d] 5 4 4
3 [a, b, c] 7 6 NaN
python python-3.x pandas lambda pandas-groupby
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
If I wanted to get the mean of the past 2 values based on column id, I would do the following:
df['rolling_mean_2'] = df.groupby('id').apply(lambda x: x.rolling(2, min_periods=2).mean())
>> id value rolling_mean_2
0 b 1 NaN
1 b 3 2
2 d 5 NaN
3 d 7 6
Right, straightforward.
Ok, now let's say my id are in a list form with 4 unique values (a, b, c, d)
x = [{'id': ['a','b','d'], 'value':1},
{'id': ['b','a','d'], 'value':3},
{'id': ['b','a','d'], 'value':5},
{'id': ['a','b','c'], 'value':7}]
df = pd.DataFrame(x)
Now, how would I get the mean from the past 2 values (incl. current row) based on unique value that contains in the list? Thus, my expected output would be as follows:
I'm only going to use variable a and d to keep tidiness and simplicity.
>> id value a_rolling_mean_2 d_rolling_mean_2
0 [a, b, d] 1 NaN NaN
1 [b, a, d] 3 2 2
2 [b, a, d] 5 4 4
3 [a, b, c] 7 6 NaN
python python-3.x pandas lambda pandas-groupby
python python-3.x pandas lambda pandas-groupby
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
asked Nov 27 '18 at 2:00
ChipmunkafyChipmunkafy
13511
13511
add a comment |
add a comment |
1 Answer
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Using concat with dataframe constructor recreate the dataframe
df=df.rename(columns={'value':'V'})
newdf=pd.concat([df.V,pd.DataFrame(df.id.tolist(),index=df.index)],axis=1)
Then , Using melt with groupby rolling mean and stack to get the out put
newdf.reset_index().melt(['index','V']).set_index('index').sort_index().groupby('value').V.rolling(2, min_periods=2).mean().unstack(0)
Out[260]:
value a b c d
index
0 NaN NaN NaN NaN
1 2.0 2.0 NaN 2.0
2 4.0 4.0 NaN 4.0
3 6.0 6.0 NaN NaN
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
add a comment |
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1 Answer
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1 Answer
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active
oldest
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Using concat with dataframe constructor recreate the dataframe
df=df.rename(columns={'value':'V'})
newdf=pd.concat([df.V,pd.DataFrame(df.id.tolist(),index=df.index)],axis=1)
Then , Using melt with groupby rolling mean and stack to get the out put
newdf.reset_index().melt(['index','V']).set_index('index').sort_index().groupby('value').V.rolling(2, min_periods=2).mean().unstack(0)
Out[260]:
value a b c d
index
0 NaN NaN NaN NaN
1 2.0 2.0 NaN 2.0
2 4.0 4.0 NaN 4.0
3 6.0 6.0 NaN NaN
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
add a comment |
Using concat with dataframe constructor recreate the dataframe
df=df.rename(columns={'value':'V'})
newdf=pd.concat([df.V,pd.DataFrame(df.id.tolist(),index=df.index)],axis=1)
Then , Using melt with groupby rolling mean and stack to get the out put
newdf.reset_index().melt(['index','V']).set_index('index').sort_index().groupby('value').V.rolling(2, min_periods=2).mean().unstack(0)
Out[260]:
value a b c d
index
0 NaN NaN NaN NaN
1 2.0 2.0 NaN 2.0
2 4.0 4.0 NaN 4.0
3 6.0 6.0 NaN NaN
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
add a comment |
Using concat with dataframe constructor recreate the dataframe
df=df.rename(columns={'value':'V'})
newdf=pd.concat([df.V,pd.DataFrame(df.id.tolist(),index=df.index)],axis=1)
Then , Using melt with groupby rolling mean and stack to get the out put
newdf.reset_index().melt(['index','V']).set_index('index').sort_index().groupby('value').V.rolling(2, min_periods=2).mean().unstack(0)
Out[260]:
value a b c d
index
0 NaN NaN NaN NaN
1 2.0 2.0 NaN 2.0
2 4.0 4.0 NaN 4.0
3 6.0 6.0 NaN NaN
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
Using concat with dataframe constructor recreate the dataframe
df=df.rename(columns={'value':'V'})
newdf=pd.concat([df.V,pd.DataFrame(df.id.tolist(),index=df.index)],axis=1)
Then , Using melt with groupby rolling mean and stack to get the out put
newdf.reset_index().melt(['index','V']).set_index('index').sort_index().groupby('value').V.rolling(2, min_periods=2).mean().unstack(0)
Out[260]:
value a b c d
index
0 NaN NaN NaN NaN
1 2.0 2.0 NaN 2.0
2 4.0 4.0 NaN 4.0
3 6.0 6.0 NaN NaN
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
answered Nov 27 '18 at 2:15
Wen-BenWen-Ben
112k83267
112k83267
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
add a comment |
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
Perfect! Thank you so much
– Chipmunkafy
Nov 27 '18 at 2:49
add a comment |
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