Python for R Users: Transitioning to Python for Data Science

Introduction

Transitioning from R to Python can seem daunting, but many concepts and workflows are surprisingly similar between the two languages. This guide is designed specifically for R users who want to learn Python for data science. We’ll provide side-by-side examples of common data science tasks—including data manipulation, visualization, and modeling—to illustrate equivalent functionalities in both languages. In addition, you’ll receive practical tips to help you navigate the transition smoothly.

Data Manipulation: dplyr vs. pandas

Below is an example comparing how you filter and summarize data in R using dplyr and in Python using pandas.

R Example
Python Example

library(dplyr)

# Create sample data
data <- data.frame(
  id = 1:10,
  value = c(5, 3, 6, 2, 8, 7, 4, 9, 1, 10)
)

# Filter rows where value > 5 and calculate average
result_r <- data %>%
  filter(value > 5) %>%
  summarize(avg_value = mean(value))
print(result_r)

  avg_value
1         8

import pandas as pd

# Create sample data
data = pd.DataFrame({
    'id': list(range(1, 11)),
    'value': [5, 3, 6, 2, 8, 7, 4, 9, 1, 10]
})

# Filter rows where value > 5 and calculate average
result_py = data[data['value'] > 5]['value'].mean()
print("Average value:", result_py)

Average value: 8.0

Data Visualization: ggplot2 vs. matplotlib/Seaborn

This section compares how to create a simple scatter plot in R and Python.

R Example
Python Example

library(ggplot2)

# Generate sample data
data <- data.frame(x = rnorm(100), y = rnorm(100))

# Create a scatter plot
ggplot(data, aes(x = x, y = y)) +
  geom_point(color = "blue") +
  labs(title = "Scatter Plot in R using ggplot2", x = "X-axis", y = "Y-axis")

import matplotlib.pyplot as plt
import numpy as np

# Generate sample data
x = np.random.randn(100)
y = np.random.randn(100)

# Create a scatter plot
plt.scatter(x, y, color='blue')
plt.title("Scatter Plot in Python using matplotlib")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Machine Learning: lm() vs. scikit-learn

Here’s a basic comparison between a linear regression model in R and one in Python.

R Example
Python Example

data <- mtcars
model_r <- lm(mpg ~ wt, data = data)
summary(model_r)


Call:
lm(formula = mpg ~ wt, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5432 -2.3647 -0.1252  1.4096  6.8727 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.2851     1.8776  19.858  < 2e-16 ***
wt           -5.3445     0.5591  -9.559 1.29e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.046 on 30 degrees of freedom
Multiple R-squared:  0.7528,    Adjusted R-squared:  0.7446 
F-statistic: 91.38 on 1 and 30 DF,  p-value: 1.294e-10

import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np

# Create a simple dataset for demonstration
data = pd.DataFrame({
    'wt': np.random.rand(32)*5,
    'mpg': np.random.rand(32)*30
})

# Fit a linear regression model
model_py = LinearRegression().fit(data[['wt']], data['mpg'])
print("Coefficient:", model_py.coef_, "Intercept:", model_py.intercept_)

Coefficient: [-2.78248725] Intercept: 23.502217265928643

Tips for Transitioning from R to Python

Leverage Your Existing Knowledge:
Many R packages have Python counterparts with similar functionalities (e.g., dplyr vs. pandas, ggplot2 vs. matplotlib/Seaborn). Use these similarities to accelerate your learning.
Practice Side-by-Side:
Experiment with side-by-side examples to understand how each language handles data operations, visualization, and modeling. This comparison can help solidify your understanding and highlight subtle differences.
Start Small:
Begin with simple scripts and gradually tackle more complex tasks. Focus on one functionality at a time.
Utilize Community Resources:
Leverage online tutorials, forums, and documentation for both R and Python. Many communities, including Datonovia, offer resources specifically for transitioning between the two languages.

Conclusion

Transitioning from R to Python doesn’t have to be overwhelming. By exploring side-by-side examples in data manipulation, visualization, and modeling, you can build a solid foundation in Python while utilizing your existing R knowledge. This guide serves as a starting point—experiment, explore, and leverage the best of both worlds to enhance your data science toolkit.

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Note

Here are more articles from the same category to help you dive deeper into the topic.

Machine Learning Workflows: tidymodels vs. scikit-learn

Comparing ML Model Training, Evaluation, and Prediction in R and Python