Pandas group by aggregation function - Remove Top Level headers
1
I have used following aggregation dictionary:
fare_agg = {
'Fare_amount': {
'mean_fare_amount':'mean',
'meadian_fare_amount': 'median'
},
'Total_amount':{
'mean_total_amount':'mean',
'median_total_amount':'median'
},
'Trip_distance':{
'mean_trip_distance':'mean',
'median_trip_distance':'median'
}
}
df_g = df_a.groupby('type').agg(fare_agg)
When I save the data to the csv it comes up with two level headers. I tried to remove df_a.columns = df_a.columns.droplevel(0) but that didn't work. Then tried to reset_index(inplace=True) but that also didn't work. Whats the trick to get only the second level header.
Here is how the csv header looks like:
type,Trip_distance,Trip_distance,Fare_amount,Fare_amount,Total_amount,Total_amount
,mean_trip_distance,median_trip_distance,mean_fare_amount,meadian_fare_amount,median_total_amount,mean_total_amount
python pandas
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
add a comment |
1
I have used following aggregation dictionary:
fare_agg = {
'Fare_amount': {
'mean_fare_amount':'mean',
'meadian_fare_amount': 'median'
},
'Total_amount':{
'mean_total_amount':'mean',
'median_total_amount':'median'
},
'Trip_distance':{
'mean_trip_distance':'mean',
'median_trip_distance':'median'
}
}
df_g = df_a.groupby('type').agg(fare_agg)
When I save the data to the csv it comes up with two level headers. I tried to remove df_a.columns = df_a.columns.droplevel(0) but that didn't work. Then tried to reset_index(inplace=True) but that also didn't work. Whats the trick to get only the second level header.
Here is how the csv header looks like:
type,Trip_distance,Trip_distance,Fare_amount,Fare_amount,Total_amount,Total_amount
,mean_trip_distance,median_trip_distance,mean_fare_amount,meadian_fare_amount,median_total_amount,mean_total_amount
python pandas
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
1
Possible duplicate of Python Pandas - How to flatten a hierarchical index in columns. If this doesn't help, I strongly suggest you provide a Minimal, Complete, and Verifiable example.
– jpp
Nov 20 at 17:52
Related? stackoverflow.com/questions/22233488/…
– Evan
Nov 20 at 19:32
add a comment |
1
1
1
I have used following aggregation dictionary:
fare_agg = {
'Fare_amount': {
'mean_fare_amount':'mean',
'meadian_fare_amount': 'median'
},
'Total_amount':{
'mean_total_amount':'mean',
'median_total_amount':'median'
},
'Trip_distance':{
'mean_trip_distance':'mean',
'median_trip_distance':'median'
}
}
df_g = df_a.groupby('type').agg(fare_agg)
When I save the data to the csv it comes up with two level headers. I tried to remove df_a.columns = df_a.columns.droplevel(0) but that didn't work. Then tried to reset_index(inplace=True) but that also didn't work. Whats the trick to get only the second level header.
Here is how the csv header looks like:
type,Trip_distance,Trip_distance,Fare_amount,Fare_amount,Total_amount,Total_amount
,mean_trip_distance,median_trip_distance,mean_fare_amount,meadian_fare_amount,median_total_amount,mean_total_amount
python pandas
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
I have used following aggregation dictionary:
fare_agg = {
'Fare_amount': {
'mean_fare_amount':'mean',
'meadian_fare_amount': 'median'
},
'Total_amount':{
'mean_total_amount':'mean',
'median_total_amount':'median'
},
'Trip_distance':{
'mean_trip_distance':'mean',
'median_trip_distance':'median'
}
}
df_g = df_a.groupby('type').agg(fare_agg)
When I save the data to the csv it comes up with two level headers. I tried to remove df_a.columns = df_a.columns.droplevel(0) but that didn't work. Then tried to reset_index(inplace=True) but that also didn't work. Whats the trick to get only the second level header.
Here is how the csv header looks like:
type,Trip_distance,Trip_distance,Fare_amount,Fare_amount,Total_amount,Total_amount
,mean_trip_distance,median_trip_distance,mean_fare_amount,meadian_fare_amount,median_total_amount,mean_total_amount
python pandas
python pandas
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
asked Nov 20 at 17:50
Null-Hypothesis
5,5483099171
5,5483099171
1
Possible duplicate of Python Pandas - How to flatten a hierarchical index in columns. If this doesn't help, I strongly suggest you provide a Minimal, Complete, and Verifiable example.
– jpp
Nov 20 at 17:52
Related? stackoverflow.com/questions/22233488/…
– Evan
Nov 20 at 19:32
add a comment |
1
Possible duplicate of Python Pandas - How to flatten a hierarchical index in columns. If this doesn't help, I strongly suggest you provide a Minimal, Complete, and Verifiable example.
– jpp
Nov 20 at 17:52
Related? stackoverflow.com/questions/22233488/…
– Evan
Nov 20 at 19:32
1
1
Possible duplicate of Python Pandas - How to flatten a hierarchical index in columns. If this doesn't help, I strongly suggest you provide a Minimal, Complete, and Verifiable example.
– jpp
Nov 20 at 17:52
Possible duplicate of Python Pandas - How to flatten a hierarchical index in columns. If this doesn't help, I strongly suggest you provide a Minimal, Complete, and Verifiable example.
– jpp
Nov 20 at 17:52
Related? stackoverflow.com/questions/22233488/…
– Evan
Nov 20 at 19:32
Related? stackoverflow.com/questions/22233488/…
– Evan
Nov 20 at 19:32
add a comment |
1 Answer
1
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oldest
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This should do what you want:
df_g.columns = df_g.columns.droplevel(-1)
Example:
df = pd.DataFrame({'type':['a','a','b','b'],'Fare_amount':[2,5,3,4],
'Total_amount':[2,3,4,1],'Trip_distance' :[2,2,4,4]})
df_g = df.groupby('type').agg(fare_agg)
df_g.columns = df_g.columns.droplevel(-1)
print(df_g)
Fare_amount Fare_amount Total_amount Total_amount Trip_distance
type
a 3.5 3.5 2.5 2.5 2
b 3.5 3.5 2.5 2.5 4
Trip_distance
type
a 2
b 4
answered Dec 1 at 19:04
nixon
3,2521221
add a comment |
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1 Answer
1
active
oldest
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1 Answer
1
active
oldest
votes
active
oldest
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active
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votes
0
This should do what you want:
df_g.columns = df_g.columns.droplevel(-1)
Example:
df = pd.DataFrame({'type':['a','a','b','b'],'Fare_amount':[2,5,3,4],
'Total_amount':[2,3,4,1],'Trip_distance' :[2,2,4,4]})
df_g = df.groupby('type').agg(fare_agg)
df_g.columns = df_g.columns.droplevel(-1)
print(df_g)
Fare_amount Fare_amount Total_amount Total_amount Trip_distance
type
a 3.5 3.5 2.5 2.5 2
b 3.5 3.5 2.5 2.5 4
Trip_distance
type
a 2
b 4
answered Dec 1 at 19:04
nixon
3,2521221
add a comment |
0
This should do what you want:
df_g.columns = df_g.columns.droplevel(-1)
Example:
df = pd.DataFrame({'type':['a','a','b','b'],'Fare_amount':[2,5,3,4],
'Total_amount':[2,3,4,1],'Trip_distance' :[2,2,4,4]})
df_g = df.groupby('type').agg(fare_agg)
df_g.columns = df_g.columns.droplevel(-1)
print(df_g)
Fare_amount Fare_amount Total_amount Total_amount Trip_distance
type
a 3.5 3.5 2.5 2.5 2
b 3.5 3.5 2.5 2.5 4
Trip_distance
type
a 2
b 4
answered Dec 1 at 19:04
nixon
3,2521221
add a comment |
0
0
0
This should do what you want:
df_g.columns = df_g.columns.droplevel(-1)
Example:
df = pd.DataFrame({'type':['a','a','b','b'],'Fare_amount':[2,5,3,4],
'Total_amount':[2,3,4,1],'Trip_distance' :[2,2,4,4]})
df_g = df.groupby('type').agg(fare_agg)
df_g.columns = df_g.columns.droplevel(-1)
print(df_g)
Fare_amount Fare_amount Total_amount Total_amount Trip_distance
type
a 3.5 3.5 2.5 2.5 2
b 3.5 3.5 2.5 2.5 4
Trip_distance
type
a 2
b 4
answered Dec 1 at 19:04
nixon
3,2521221
This should do what you want:
df_g.columns = df_g.columns.droplevel(-1)
Example:
df = pd.DataFrame({'type':['a','a','b','b'],'Fare_amount':[2,5,3,4],
'Total_amount':[2,3,4,1],'Trip_distance' :[2,2,4,4]})
df_g = df.groupby('type').agg(fare_agg)
df_g.columns = df_g.columns.droplevel(-1)
print(df_g)
Fare_amount Fare_amount Total_amount Total_amount Trip_distance
type
a 3.5 3.5 2.5 2.5 2
b 3.5 3.5 2.5 2.5 4
Trip_distance
type
a 2
b 4
answered Dec 1 at 19:04
nixon
3,2521221
answered Dec 1 at 19:04
nixon
3,2521221
answered Dec 1 at 19:04
nixon
3,2521221
answered Dec 1 at 19:04
nixon
3,2521221
3,2521221
add a comment |
add a comment |
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1
Possible duplicate of Python Pandas - How to flatten a hierarchical index in columns. If this doesn't help, I strongly suggest you provide a Minimal, Complete, and Verifiable example.
– jpp
Nov 20 at 17:52
Related? stackoverflow.com/questions/22233488/…
– Evan
Nov 20 at 19:32