How to use math.log10() function on whole pandas dataframe
I want to do logarithm of every value in pandas dataframe. I have tried this but it does not work:
#Reading data from excel and rounding values on 2 decimal places
import math
import pandas as pd
data = pd.read_excel("DataSet.xls").round(2)
log_data= math.log10(data)
It gives me this error:
TypeError: must be real number, not DataFrame
Do you have any idea what to do?
python pandas
asked 1 hour ago
AleksandarAleksandar
908
add a comment |
I want to do logarithm of every value in pandas dataframe. I have tried this but it does not work:
#Reading data from excel and rounding values on 2 decimal places
import math
import pandas as pd
data = pd.read_excel("DataSet.xls").round(2)
log_data= math.log10(data)
It gives me this error:
TypeError: must be real number, not DataFrame
Do you have any idea what to do?
python pandas
asked 1 hour ago
AleksandarAleksandar
908
add a comment |
I want to do logarithm of every value in pandas dataframe. I have tried this but it does not work:
#Reading data from excel and rounding values on 2 decimal places
import math
import pandas as pd
data = pd.read_excel("DataSet.xls").round(2)
log_data= math.log10(data)
It gives me this error:
TypeError: must be real number, not DataFrame
Do you have any idea what to do?
python pandas
asked 1 hour ago
AleksandarAleksandar
908
I want to do logarithm of every value in pandas dataframe. I have tried this but it does not work:
#Reading data from excel and rounding values on 2 decimal places
import math
import pandas as pd
data = pd.read_excel("DataSet.xls").round(2)
log_data= math.log10(data)
It gives me this error:
TypeError: must be real number, not DataFrame
Do you have any idea what to do?
python pandas
python pandas
asked 1 hour ago
AleksandarAleksandar
908
asked 1 hour ago
AleksandarAleksandar
908
asked 1 hour ago
AleksandarAleksandar
908
asked 1 hour ago
AleksandarAleksandar
908
asked 1 hour ago
AleksandarAleksandar
908
908
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
Use the numpy version, not math
import numpy as np
np.log10(df)
answered 1 hour ago
ecortazarecortazar
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
From what it seems math.log10 cannot handle neither dataframes nor ndarrays.
So you can either go with np.log10 and reconstruct the dataframe as pointed out in other solutions, or if you want to go with math.log10, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Note however that this solution will be slower than a vectorized approach using np.log10.
df.applymap(math.log10)
Example
df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))
print(df)
a b c d e f
0 3 4 1 1 2 1
1 4 4 4 3 4 1
2 4 3 3 1 4 1
3 3 4 1 3 1 1
4 1 2 3 4 2 1
5 1 3 3 1 4 3
df.applymap(math.log10)
a b c d e f
0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121
answered 1 hour ago
yatuyatu
12.3k31340
add a comment |
You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.
You can test it:
df.applymap(math.log10)
edited 53 mins ago
IanS
8,56232763
answered 1 hour ago
Valentin MercierValentin Mercier
8710
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
Use the numpy version, not math
import numpy as np
np.log10(df)
answered 1 hour ago
ecortazarecortazar
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
Use the numpy version, not math
import numpy as np
np.log10(df)
answered 1 hour ago
ecortazarecortazar
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
Use the numpy version, not math
import numpy as np
np.log10(df)
answered 1 hour ago
ecortazarecortazar
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Use the numpy version, not math
import numpy as np
np.log10(df)
answered 1 hour ago
ecortazarecortazar
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 1 hour ago
ecortazarecortazar
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 1 hour ago
ecortazarecortazar
1713
answered 1 hour ago
ecortazarecortazar
1713
1713
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
From what it seems math.log10 cannot handle neither dataframes nor ndarrays.
So you can either go with np.log10 and reconstruct the dataframe as pointed out in other solutions, or if you want to go with math.log10, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Note however that this solution will be slower than a vectorized approach using np.log10.
df.applymap(math.log10)
Example
df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))
print(df)
a b c d e f
0 3 4 1 1 2 1
1 4 4 4 3 4 1
2 4 3 3 1 4 1
3 3 4 1 3 1 1
4 1 2 3 4 2 1
5 1 3 3 1 4 3
df.applymap(math.log10)
a b c d e f
0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121
answered 1 hour ago
yatuyatu
12.3k31340
add a comment |
From what it seems math.log10 cannot handle neither dataframes nor ndarrays.
So you can either go with np.log10 and reconstruct the dataframe as pointed out in other solutions, or if you want to go with math.log10, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Note however that this solution will be slower than a vectorized approach using np.log10.
df.applymap(math.log10)
Example
df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))
print(df)
a b c d e f
0 3 4 1 1 2 1
1 4 4 4 3 4 1
2 4 3 3 1 4 1
3 3 4 1 3 1 1
4 1 2 3 4 2 1
5 1 3 3 1 4 3
df.applymap(math.log10)
a b c d e f
0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121
answered 1 hour ago
yatuyatu
12.3k31340
add a comment |
From what it seems math.log10 cannot handle neither dataframes nor ndarrays.
So you can either go with np.log10 and reconstruct the dataframe as pointed out in other solutions, or if you want to go with math.log10, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Note however that this solution will be slower than a vectorized approach using np.log10.
df.applymap(math.log10)
Example
df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))
print(df)
a b c d e f
0 3 4 1 1 2 1
1 4 4 4 3 4 1
2 4 3 3 1 4 1
3 3 4 1 3 1 1
4 1 2 3 4 2 1
5 1 3 3 1 4 3
df.applymap(math.log10)
a b c d e f
0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121
answered 1 hour ago
yatuyatu
12.3k31340
From what it seems math.log10 cannot handle neither dataframes nor ndarrays.
So you can either go with np.log10 and reconstruct the dataframe as pointed out in other solutions, or if you want to go with math.log10, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Note however that this solution will be slower than a vectorized approach using np.log10.
df.applymap(math.log10)
Example
df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))
print(df)
a b c d e f
0 3 4 1 1 2 1
1 4 4 4 3 4 1
2 4 3 3 1 4 1
3 3 4 1 3 1 1
4 1 2 3 4 2 1
5 1 3 3 1 4 3
df.applymap(math.log10)
a b c d e f
0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121
answered 1 hour ago
yatuyatu
12.3k31340
edited 31 mins ago
answered 1 hour ago
yatuyatu
12.3k31340
answered 1 hour ago
yatuyatu
12.3k31340
answered 1 hour ago
yatuyatu
12.3k31340
12.3k31340
add a comment |
add a comment |
You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.
You can test it:
df.applymap(math.log10)
edited 53 mins ago
IanS
8,56232763
answered 1 hour ago
Valentin MercierValentin Mercier
8710
add a comment |
You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.
You can test it:
df.applymap(math.log10)
edited 53 mins ago
IanS
8,56232763
answered 1 hour ago
Valentin MercierValentin Mercier
8710
add a comment |
You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.
You can test it:
df.applymap(math.log10)
edited 53 mins ago
IanS
8,56232763
answered 1 hour ago
Valentin MercierValentin Mercier
8710
You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.
You can test it:
df.applymap(math.log10)
edited 53 mins ago
IanS
8,56232763
answered 1 hour ago
Valentin MercierValentin Mercier
8710
edited 53 mins ago
IanS
8,56232763
edited 53 mins ago
IanS
8,56232763
edited 53 mins ago
IanS
8,56232763
8,56232763
answered 1 hour ago
Valentin MercierValentin Mercier
8710
answered 1 hour ago
Valentin MercierValentin Mercier
8710
answered 1 hour ago
Valentin MercierValentin Mercier
8710
8710
add a comment |
add a comment |
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