# Geometric Mean Function in Python – pandas (Dataframe, Row and column wise Geometric mean)

Geometric Mean Function in python pandas is used to calculate the geometric mean of a given set of numbers, Geometric mean of a data frame, Geometric mean of column and Geometric mean of rows. let’s see an example of each we need to use the package name “stats” from scipy in calculation of geometric mean. In this section we will learn,

• How to find the geometric mean of a given set of numbers
• How to find geometric mean of a dataframe
• How to find the geometric mean of a column in dataframe
• How to find row wise geometric mean of a dataframe

#### Simple geometric mean function is shown below

```
# calculate geometric mean
from scipy import stats

print(stats.gmean([1,9,5,6,6,7]))
print(stats.gmean([4,11,15,16,5,7]))

```

output:

4.73989632394
8.47140270122

#### Geometric Mean of a dataframe:

Create dataframe

```import pandas as pd
import numpy as np
from scipy import stats

#Create a DataFrame
d = {
'Rahul','David','Andrew','Ajay','Teresa'],
'Score1':[62,47,55,74,31,77,85,63,42,32,71,57],
'Score2':[89,87,67,55,47,72,76,79,44,92,99,69]}

df = pd.DataFrame(d)
print df
```

So the resultant dataframe will be #### Geometric Mean of the column in dataframe:

```# Geometric Mean of the column in dataframe
from scipy import stats

scipy.stats.gmean(df.iloc[:,1:3],axis=0)
```

axis=0 argument calculates the column wise geometric mean of the dataframe so the result will be

array([ 55.33743527, 70.86175132])

#### Row wise geometric Mean of the dataframe:

```
# Row wise geometric mean of the dataframe
from scipy import stats

scipy.stats.gmean(df.iloc[:,1:3],axis=1)

```

axis=1 argument calculates the row wise geometric mean of the dataframe so the result will be #### Calculate the geometric mean of the specific Column:

```
# geometric mean of the specific column
scipy.stats.gmean(df.loc[:,"Score1"])

```

the above code calculates the geometric mean of the “Score1” column so the result will be

55.337435272097579

## Author

• With close to 10 years on Experience in data science and machine learning Have extensively worked on programming languages like R, Python (Pandas), SAS, Pyspark.