How to use ChatGPT for data science?

ChatGPT makes life easier for anyone working with data.

ChatGPT can do a lot of cool things, one of which is writing code.

You just give the right instructions and ChatGPT will do the work for you. It helps you create SQL queries using natural language, solve your coding problems, translate your Python code into R, Java, Julia, and more.

Here’s how to use ChatGPT for programming and data science.

1. Ask a coding question to ChatGPT

If you have a coding question, you go to StackOverflow and hope that one of the most voted answers solves your problem.

Well, now you can ask the same question to ChatGPT. Say, for example, we forgot how to merge dictionaries in Python, so we ask

How to merge dictionaries in Python?

As shown in the figure, in addition to giving the correct answer, ChatGPT also provides a variety of dictionary merging options.

But that’s not all! You can ask questions related to pandas, numpy, matplotlib, and other data science libraries.

2. Translate Python code into R

Assume you are a data scientist who knows Python and is new to R. If you want to convert Python code to R, you can ask ChatGPT

to translate the following function from Python to R:

def get_square (num):

return num * num

ChatGPT not only does the job, but it also does a good job of explaining the functionality and how to do it in R use it.

Very useful for those who barely know how to print "Hello World in R".

But there’s more! What if you use Pandas in Python and for some reason need to replicate your work in R? ChatGPT can help.

Translate the following functions from Python to R

def get_stats (event_name):
df_stats = df[df[ 'event_name' ]==event_name]
stats = df_stats[ 'player_id' ]
stats = stats.value_counts()

return stats

See After arriving at the result, now you know that you can select columns using the "$" symbol in R.

I even tested the code in R and it worked fine.

3. Natural language to SQL query

Open AI claims that you can write SQL queries using natural language. Let's test it out.

Let’s start with a simple query.

Create a SQL query to find users who live in Shanghai and are over 30 years old.

This is just a simple SQL query, let's add more conditions to test it.

The query lists the names of departments that have employed more than 10 employees in the past 3 months. The following SQL table and its attributes:

# Employee (id, name, department_id)

# Department (id, name, address)

# Salary_Payments(id, employee_id, amount, date)

SQL generated by ChatGPT

SELECT d.name AS department_name
FROM Department d
JOIN Employee e ON d.id = e.department_id
JOIN Salary_Payments sp ON e.id = sp.employee_id
WHERE sp.date >= DATEADD(month, -3, GETDATE())
GROUP BY d.name
HAVING COUNT(DISTINCT e.id) > 10;

4. Text classification

We can also use ChatGPT classifies texts as positive sentiment/negative sentiment

Categories the sentiment in these texts:

1. “I love my job”

2. “Crazy chatbot!”

3. “My dog ​​is cute”

4. “I hate eating cilantro”

5. Data visualization

We can use ChatGPT for visualization. We just need to specify the programming language and libraries to use.

Use matplotlib to draw linear regression in Python

ChatGPT also lists the corresponding steps, and finally displays the complete example code.

import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression

# 准备数据
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.array([2, 4, 5, 7, 8, 10, 11, 13, 14, 16])

# 训练线性回归模型并进行预测
x = x.reshape(-1, 1)
model = LinearRegression().fit(x, y)
y_pred = model.predict(x)

# 绘制数据和回归线
plt.scatter(x, y)
plt.plot(x, y_pred, color='red')
plt.show()

I copied/pasted the code and got the image below.

Cool, isn't it? There are hundreds of things you can do with this chatbot.

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