Set values in dataframe A by iterating from values on dataframe B
Question:
Dataframe A is similar to this :
info2 = {'speed': [None]*80}
dfA = pd.DataFrame(info2)
dfA
Dataframe B is similar to this :
info={"IndexSpeed":[7,16,44,56,80],"speed":[25,50,25,50,90]}
dfB = pd.DataFrame(info)
dfB
I need to set the values in dfA[‘speed’] by using the values in dfB.
For instance, for each row in dfA of index <=7, speed should be set at 25.
for each row of index between 8 and 16, speed should be set at 50. and so on untill all 80 rows are set.
What would be the optimal way to do this?
Answers:
You can use a merge_asof
:
dfA['speed'] = pd.merge_asof(dfA.drop(columns='speed'), dfB,
left_index=True, right_on='IndexSpeed',
direction='forward',
)['speed']
NB. dfA must be sorted on its index and dfB on IndexSpeed.
Output:
speed
0 25
1 25
2 25
3 25
4 25
.. ...
75 90
76 90
77 90
78 90
79 90
[80 rows x 1 columns]
Output as array:
array([25, 25, 25, 25, 25, 25, 25, 25, 50, 50, 50, 50, 50, 50, 50, 50, 50,
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90,
90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90])
Maybe use a dict with keys corresponding to tuples with two components
zipped = zip(pd.concat([pd.Series(0),dfB.IndexSpeed + 1]),dfB.IndexSpeed))
ind_mapper = {(i,j): k for (k,(i,j)) in enumerate(zipped)}
for lower, upper in ind_mapper:
dfA.iloc[lower:upper,0] = dfB.iloc[index_mapper[(lower, upper)],1]
Dataframe A is similar to this :
info2 = {'speed': [None]*80}
dfA = pd.DataFrame(info2)
dfA
Dataframe B is similar to this :
info={"IndexSpeed":[7,16,44,56,80],"speed":[25,50,25,50,90]}
dfB = pd.DataFrame(info)
dfB
I need to set the values in dfA[‘speed’] by using the values in dfB.
For instance, for each row in dfA of index <=7, speed should be set at 25.
for each row of index between 8 and 16, speed should be set at 50. and so on untill all 80 rows are set.
What would be the optimal way to do this?
You can use a merge_asof
:
dfA['speed'] = pd.merge_asof(dfA.drop(columns='speed'), dfB,
left_index=True, right_on='IndexSpeed',
direction='forward',
)['speed']
NB. dfA must be sorted on its index and dfB on IndexSpeed.
Output:
speed
0 25
1 25
2 25
3 25
4 25
.. ...
75 90
76 90
77 90
78 90
79 90
[80 rows x 1 columns]
Output as array:
array([25, 25, 25, 25, 25, 25, 25, 25, 50, 50, 50, 50, 50, 50, 50, 50, 50,
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90,
90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90])
Maybe use a dict with keys corresponding to tuples with two components
zipped = zip(pd.concat([pd.Series(0),dfB.IndexSpeed + 1]),dfB.IndexSpeed))
ind_mapper = {(i,j): k for (k,(i,j)) in enumerate(zipped)}
for lower, upper in ind_mapper:
dfA.iloc[lower:upper,0] = dfB.iloc[index_mapper[(lower, upper)],1]