How do I multiply certain columns by a constant?
I been trying to work on this problem for the past 1 hour but with little to no success and it is a mess.
I have a df
Age Bronx Manhatten Brooklyn Queens
0 10 11 13 12
1 8 7 9 9
2 5 8 7 6
3 3 2 3 4
4 8 6 8 7
5 10 12 13 14
6 11 13 12 10
7 7 8 6 8
How do I multiply all the columns (Bronx, Manhatten, Brooklyn, Queens) for the
age 0 by 0.05
age 1 by 0.02
age 2,3,4, by 0.2
and then for all the other columns drop it.
python pandas numpy
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
add a comment |
I been trying to work on this problem for the past 1 hour but with little to no success and it is a mess.
I have a df
Age Bronx Manhatten Brooklyn Queens
0 10 11 13 12
1 8 7 9 9
2 5 8 7 6
3 3 2 3 4
4 8 6 8 7
5 10 12 13 14
6 11 13 12 10
7 7 8 6 8
How do I multiply all the columns (Bronx, Manhatten, Brooklyn, Queens) for the
age 0 by 0.05
age 1 by 0.02
age 2,3,4, by 0.2
and then for all the other columns drop it.
python pandas numpy
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
add a comment |
I been trying to work on this problem for the past 1 hour but with little to no success and it is a mess.
I have a df
Age Bronx Manhatten Brooklyn Queens
0 10 11 13 12
1 8 7 9 9
2 5 8 7 6
3 3 2 3 4
4 8 6 8 7
5 10 12 13 14
6 11 13 12 10
7 7 8 6 8
How do I multiply all the columns (Bronx, Manhatten, Brooklyn, Queens) for the
age 0 by 0.05
age 1 by 0.02
age 2,3,4, by 0.2
and then for all the other columns drop it.
python pandas numpy
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
I been trying to work on this problem for the past 1 hour but with little to no success and it is a mess.
I have a df
Age Bronx Manhatten Brooklyn Queens
0 10 11 13 12
1 8 7 9 9
2 5 8 7 6
3 3 2 3 4
4 8 6 8 7
5 10 12 13 14
6 11 13 12 10
7 7 8 6 8
How do I multiply all the columns (Bronx, Manhatten, Brooklyn, Queens) for the
age 0 by 0.05
age 1 by 0.02
age 2,3,4, by 0.2
and then for all the other columns drop it.
python pandas numpy
python pandas numpy
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
asked Nov 15 '18 at 18:38
OptimusPrimeOptimusPrime
18810
18810
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Using mul with your own dict to map with age
age=pd.Series({0:0.5,1:0.2,2:0.2,3:0.2,4:0.2})
df.set_index('Age').mul(age,axis=0).fillna(df.set_index('Age')).reset_index()
Out[116]:
index Bronx Manhatten Brooklyn Queens
0 0 5.0 5.5 6.5 6.0
1 1 1.6 1.4 1.8 1.8
2 2 1.0 1.6 1.4 1.2
3 3 0.6 0.4 0.6 0.8
4 4 1.6 1.2 1.6 1.4
5 5 10.0 12.0 13.0 14.0
6 6 11.0 13.0 12.0 10.0
7 7 7.0 8.0 6.0 8.0
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
add a comment |
There are a lot of ways to do this:
def multiply_age(age):
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
return age
df['Age'].apply(multiply_age)
df['Age'].apply(multiply_age) will return a series of age. You can then make it a dataframe by doing pd.DataFrame(df['Age'].apply(multiply_age)).
You can also try this:
def multiply_age(row):
age = row['Age']
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
row['Age'] = age
return row
df.apply(lambda row: multiply_age(row), axis=1)['Age']
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Using mul with your own dict to map with age
age=pd.Series({0:0.5,1:0.2,2:0.2,3:0.2,4:0.2})
df.set_index('Age').mul(age,axis=0).fillna(df.set_index('Age')).reset_index()
Out[116]:
index Bronx Manhatten Brooklyn Queens
0 0 5.0 5.5 6.5 6.0
1 1 1.6 1.4 1.8 1.8
2 2 1.0 1.6 1.4 1.2
3 3 0.6 0.4 0.6 0.8
4 4 1.6 1.2 1.6 1.4
5 5 10.0 12.0 13.0 14.0
6 6 11.0 13.0 12.0 10.0
7 7 7.0 8.0 6.0 8.0
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
add a comment |
Using mul with your own dict to map with age
age=pd.Series({0:0.5,1:0.2,2:0.2,3:0.2,4:0.2})
df.set_index('Age').mul(age,axis=0).fillna(df.set_index('Age')).reset_index()
Out[116]:
index Bronx Manhatten Brooklyn Queens
0 0 5.0 5.5 6.5 6.0
1 1 1.6 1.4 1.8 1.8
2 2 1.0 1.6 1.4 1.2
3 3 0.6 0.4 0.6 0.8
4 4 1.6 1.2 1.6 1.4
5 5 10.0 12.0 13.0 14.0
6 6 11.0 13.0 12.0 10.0
7 7 7.0 8.0 6.0 8.0
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
add a comment |
Using mul with your own dict to map with age
age=pd.Series({0:0.5,1:0.2,2:0.2,3:0.2,4:0.2})
df.set_index('Age').mul(age,axis=0).fillna(df.set_index('Age')).reset_index()
Out[116]:
index Bronx Manhatten Brooklyn Queens
0 0 5.0 5.5 6.5 6.0
1 1 1.6 1.4 1.8 1.8
2 2 1.0 1.6 1.4 1.2
3 3 0.6 0.4 0.6 0.8
4 4 1.6 1.2 1.6 1.4
5 5 10.0 12.0 13.0 14.0
6 6 11.0 13.0 12.0 10.0
7 7 7.0 8.0 6.0 8.0
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
Using mul with your own dict to map with age
age=pd.Series({0:0.5,1:0.2,2:0.2,3:0.2,4:0.2})
df.set_index('Age').mul(age,axis=0).fillna(df.set_index('Age')).reset_index()
Out[116]:
index Bronx Manhatten Brooklyn Queens
0 0 5.0 5.5 6.5 6.0
1 1 1.6 1.4 1.8 1.8
2 2 1.0 1.6 1.4 1.2
3 3 0.6 0.4 0.6 0.8
4 4 1.6 1.2 1.6 1.4
5 5 10.0 12.0 13.0 14.0
6 6 11.0 13.0 12.0 10.0
7 7 7.0 8.0 6.0 8.0
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
answered Nov 15 '18 at 18:41
Wen-BenWen-Ben
117k83469
117k83469
add a comment |
add a comment |
There are a lot of ways to do this:
def multiply_age(age):
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
return age
df['Age'].apply(multiply_age)
df['Age'].apply(multiply_age) will return a series of age. You can then make it a dataframe by doing pd.DataFrame(df['Age'].apply(multiply_age)).
You can also try this:
def multiply_age(row):
age = row['Age']
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
row['Age'] = age
return row
df.apply(lambda row: multiply_age(row), axis=1)['Age']
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
add a comment |
There are a lot of ways to do this:
def multiply_age(age):
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
return age
df['Age'].apply(multiply_age)
df['Age'].apply(multiply_age) will return a series of age. You can then make it a dataframe by doing pd.DataFrame(df['Age'].apply(multiply_age)).
You can also try this:
def multiply_age(row):
age = row['Age']
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
row['Age'] = age
return row
df.apply(lambda row: multiply_age(row), axis=1)['Age']
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
add a comment |
There are a lot of ways to do this:
def multiply_age(age):
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
return age
df['Age'].apply(multiply_age)
df['Age'].apply(multiply_age) will return a series of age. You can then make it a dataframe by doing pd.DataFrame(df['Age'].apply(multiply_age)).
You can also try this:
def multiply_age(row):
age = row['Age']
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
row['Age'] = age
return row
df.apply(lambda row: multiply_age(row), axis=1)['Age']
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
There are a lot of ways to do this:
def multiply_age(age):
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
return age
df['Age'].apply(multiply_age)
df['Age'].apply(multiply_age) will return a series of age. You can then make it a dataframe by doing pd.DataFrame(df['Age'].apply(multiply_age)).
You can also try this:
def multiply_age(row):
age = row['Age']
if age == 0:
age *= 0.05
elif age == 1:
age *= 0.02
elif age in {2, 3, 4}:
age *= 0.2
row['Age'] = age
return row
df.apply(lambda row: multiply_age(row), axis=1)['Age']
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
answered Nov 15 '18 at 18:42
Eric WangEric Wang
33519
33519
add a comment |
add a comment |
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