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Python pandas has a pct_change function which I use to calculate the returns for stock prices in a dataframe:
ndf['Return']= ndf['TypicalPrice'].pct_change() I am using the following code to get logarithmic returns, but it gives the exact same values as the pct.change() function:
ndf['retlog']=np.log(ndf['TypicalPrice'].astype('float64')/ndf['TypicalPrice'].astype('float64').shift(1)) #np is for numpy 
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Log returns are simply the natural log of 1 plus the arithmetic return. So how about this? df['pct_change'] = df.price.pct_change() df['log_return'] = np.log(1 + df.pct_change)
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”.
Logarithmic return is also called the continuously compounded return. This means that the frequency of compounding does not matter, making returns of different assets easier to compare.
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
df1 = pd.DataFrame (df1,columns=['Name','University_Rank']) Natural log of the column (University_Rank) is computed using log () function and stored in a new column namely “log_value” as shown below.
Python pandas has a pct_change function which I use to calculate the returns for stock prices in a dataframe: I am using the following code to get logarithmic returns, but it gives the exact same values as the pct.change () function: Here is one way to calculate log return using .shift ().
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”. Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
Here is one way to calculate log return using .shift(). And the result is similar to but not the same as the gross return calculated by pct_change(). Can you upload a copy of your sample data (dropbox share link) to reproduce the inconsistency you saw?
import pandas as pd import numpy as np np.random.seed(0) df = pd.DataFrame(100 + np.random.randn(100).cumsum(), columns=['price']) df['pct_change'] = df.price.pct_change() df['log_ret'] = np.log(df.price) - np.log(df.price.shift(1)) Out[56]: price pct_change log_ret 0 101.7641 NaN NaN 1 102.1642 0.0039 0.0039 2 103.1429 0.0096 0.0095 3 105.3838 0.0217 0.0215 4 107.2514 0.0177 0.0176 5 106.2741 -0.0091 -0.0092 6 107.2242 0.0089 0.0089 7 107.0729 -0.0014 -0.0014 .. ... ... ... 92 101.6160 0.0021 0.0021 93 102.5926 0.0096 0.0096 94 102.9490 0.0035 0.0035 95 103.6555 0.0069 0.0068 96 103.6660 0.0001 0.0001 97 105.4519 0.0172 0.0171 98 105.5788 0.0012 0.0012 99 105.9808 0.0038 0.0038 [100 rows x 3 columns] If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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