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The following code will look for any existing DataFrames in the pandas library, and then create a new one with the mean values for Num of Cars, Tyres, Years, and Price and Total.

from rpy2.robjects import LinkedVectors, ColumnVector, DataFrame, IntegerVector

from rpy2.robjects.packages import importr

from rpy2.robjects.packages import importr.data

from rpy2.robjects.packages import importr.datasets

from rpy2.robjects.packages import importr.sp

from rpy2.robjects.packages import importr.dask

from rpy2.robjects.packages import importr.stats

import pandas as pd

# Read the data from an existing pandas DataFrame if they exist.

# This is for testing purposes

df = pd.read_csv('https://raw.githubusercontent.com/rpy2/datasets/master/bikes.csv')

# We need to create a new DataFrame with the aggregate values

df = DataFrame(LinkedVectors(['Num of Cars','Tyres','Years','Price'], df.columns), df.index)

df['Total'] = df.sum()

To run this in R, you simply run the following in the R console.

import numpy as np

import pandas as pd

# Load and clean some data

data = pd.DataFrame(np.random.randint(0,100,size=(300, 15)))

# Compute descriptive statistics

desc_stats = pd.Series(data.describe().to_dict())

# Prepare an aggregate frame

agg_stats = pd.DataFrame(desc_stats.to_dict())

# Get a list of all unique column names

columns = list(agg_stats.columns)

# Create a new output DataFrame

new_stats = pd.

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