Log and Natural Logarithmic value of a column in Pandas
Here we are discussing some generally used logarithmic and natural logarithmic values of a column in Pandas, which are as follows.
- Log on base 2 value of a column in Pandas
- Log on base 10 value of a column in Pandas
- Natural logarithmic value of a column in Pandas
- Log value of a Column in Pandas
Logarithm on base 2 value of a column in Pandas
After the dataframe is created, we can apply numpy.log2() function to the columns. In this case, we will be finding the logarithm values of the column salary. The computed values are stored in the new column “logarithm_base2”.
Python3
# Calculate logarithm to base 2 # on 'Salary' column data[ 'logarithm_base2' ] = np.log2(data[ 'Salary' ]) # Show the dataframe print (data) |
Output :
Name Salary logarithm_base2
0 Geek1 18000 14.135709
1 Geek2 20000 14.287712
2 Geek3 15000 13.872675
3 Geek4 35000 15.095067
Logarithm on base 10 value of a column in Pandas
To find the logarithm on base 10 values we can apply numpy.log10() function to the columns. In this case, we will be finding the logarithm values of the column salary. The computed values are stored in the new column “logarithm_base10”.
Python3
# Calculate logarithm to # base 10 on 'Salary' column data[ 'logarithm_base10' ] = np.log10(data[ 'Salary' ]) # Show the dataframe print (data) |
Output :
Name Salary logarithm_base10
0 Geek1 18000 4.255273
1 Geek2 20000 4.301030
2 Geek3 15000 4.176091
3 Geek4 35000 4.544068
Natural logarithmic value of a column in Pandas
To find the natural logarithmic values we can apply numpy.log() function to the columns. In this case, we will be finding the natural logarithm values of the column salary. The computed values are stored in the new column “natural_log“.
Python3
# Calculate natural logarithm on # 'Salary' column data[ 'natural_log' ] = np.log(data[ 'Salary' ]) # Show the dataframe print (data) |
Output :
Name Salary natural_log
0 Geek1 18000 9.798127
1 Geek2 20000 9.903488
2 Geek3 15000 9.615805
3 Geek4 35000 10.463103
Log value of a Column in Pandas
In this example the Python code uses Pandas to create a dataframe (‘Column_Name’). It imports the log function from the math module to compute natural logarithmic values for the column. Results are stored in ‘Log_Values,’ and the updated dataframe is displayed.
Python3
import pandas as pd from math import log # Import the log function from the math module # Creating a sample dataframe data = { 'Column_Name' : [ 2 , 4 , 8 , 16 , 32 ]} df = pd.DataFrame(data) # Calculating logarithmic values for the specified column df[ 'Log_Values' ] = df[ 'Column_Name' ]. apply ( lambda x: log(x)) # Displaying the resulting dataframe print (df) |
Output :
Column_Name Log_Values
0 2 0.693147
1 4 1.386294
2 8 2.079442
3 16 2.772589
4 32 3.465736
Log and natural Logarithmic value of a column in Pandas – Python
This article explores the computation of logarithmic and natural logarithmic values for a column in Pandas using Python.