Log and natural logarithmic value of a column in pandas python is carried out using log2(), log10() and log()function of numpy. Let’s see how to
- Get the natural logarithmic value of column in pandas (natural log – loge())
- Get the logarithmic value of the column in pandas with base 2 – log2()
- Get the logarithmic value of the column in pandas with base 10 – log10()
With an example of each. First let’s create a dataframe
import pandas as pd import numpy as np #Create a DataFrame df1 = { 'Name':['George','Andrea','micheal','maggie','Ravi','Xien','Jalpa'], 'University_Rank':[6,47,21,74,32,77,8]} df1 = pd.DataFrame(df1,columns=['Name','University_Rank']) print(df1)
df1 will be
Natural logarithmic value of a column in pandas (loge)
Natural log of the column (University_Rank) is computed using log() function and stored in a new column namely “log_value” as shown below.
df1['log_value'] = np.log(df1['University_Rank']) print(df1)
natural log of a column (log to the base e) is calculated and populated, so the resultant dataframe will be
Logarithmic value of a column in pandas (log2)
log to the base 2 of the column (University_Rank) is computed using log2() function and stored in a new column namely “log2_value” as shown below
df1['log2_value'] = np.log2(df1['University_Rank']) print(df1)
so the resultant dataframe will be
Logarithmic value of a column in pandas (log10)
log to the base 10 of the column (University_Rank) is computed using log10() function and stored in a new column namely “log10_value” as shown below
df1['log10_value'] = np.log10(df1['University_Rank']) print(df1)
so the resultant dataframe will be