This time, the CSV file looked at some publication data - The year of publication; the number of papers published in that year; and the number of citaations papers in that year got over time. The data was collected from Google Scholar for one person.
The prompt used (again using Code Interpreter plug-in
The transcript is shown below.
Analyse this data as a Data Analyst including linear and polynominal fitring
Interestingly it spotted some errors in its own code and corrected them.
I apologize for the inconvenience, there seems to have been a technical issue with the code execution. Let's try to load the dataset again.
data_new = pd.read_csv('/mnt/data/papervcitation.csv')
data_new.head()
Apologies for the inconvenience. It seems there was a mistake in the code execution. We need to import the pandas library first. Let's correct that and proceed.
import pandas as pd
data_new = pd.read_csv('/mnt/data/papervcitation.csv')
data_new.head()
RESULT
year citations no of papers
0 1999 5 2
1 2000 0 0
2 2001 23 3
3 2002 76 2
4 2003 36 4
This dataset contains three columns: 'year', 'citations', and 'no of papers'.
- 'year': This seems to represent the year in which the papers were published or the citations were received.
- 'citations': This likely represents the total number of citations received in each year.
- 'no of papers': This probably represents the number of papers published in each year.
Let's proceed with some basic data analysis. We'll start by checking the basic statistics of the dataset.
RESULT
year citations no of papers
count 25.000000 25.000000 25.000000
mean 2011.000000 23.560000 4.960000
std 7.359801 24.627356 4.286413
min 1999.000000 0.000000 0.000000
25% 2005.000000 2.000000 2.000000
50% 2011.000000 19.000000 4.000000
75% 2017.000000 39.000000 7.000000
max 2023.000000 81.000000 14.000000
Here are some basic statistics for the 'year', 'citations', and 'no of papers' columns:
- The data spans 25 years, from 1999 to 2023.
- The average number of citations per year is approximately 23.56, with a standard deviation of about 24.63.
- The number of citations received in a year ranges from 0 to 81.
- The average number of papers published per year is approximately 4.96, with a standard deviation of about 4.29.
- The number of papers published in a year ranges from 0 to 14.
Next, let's visualize the trend of 'citations' and 'no of papers' over the years. We'll also perform linear and polynomial fitting to these trends.
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
linear_regressor = LinearRegression()
X = data_new['year'].values.reshape(-1,1)
y_citations = data_new['citations'].values
y_papers = data_new['no of papers'].values
linear_regressor.fit(X, y_citations)
y_citations_pred = linear_regressor.predict(X)
linear_regressor.fit(X, y_papers)
y_papers_pred = linear_regressor.predict(X)
poly = PolynomialFeatures(degree=3)
X_poly = poly.fit_transform(X)
poly_regressor = LinearRegression()
poly_regressor.fit(X_poly, y_citations)
y_citations_poly_pred = poly_regressor.predict(X_poly)
poly_regressor.fit(X_poly, y_papers)
y_papers_poly_pred = poly_regressor.predict(X_poly)
plt.figure(figsize=(12,6))
plt.scatter(data_new['year'], data_new['citations'], color = 'blue', label='Citations')
plt.scatter(data_new['year'], data_new['no of papers'], color = 'red', label='No of Papers')
plt.plot(data_new['year'], y_citations_pred, color = 'blue', linestyle = '--', label='Citations Linear Fit')
plt.plot(data_new['year'], y_papers_pred, color = 'red', linestyle = '--', label='No of Papers Linear Fit')
plt.plot(data_new['year'], y_citations_poly_pred, color = 'blue', linestyle = ':', label='Citations Polynomial Fit')
plt.plot(data_new['year'], y_papers_poly_pred, color = 'red', linestyle = ':', label='No of Papers Polynomial Fit')
plt.title('Citations and No of Papers over Years with Linear and Polynomial Fits')
plt.xlabel('Year')
plt.ylabel('Count')
plt.legend()
plt.grid(True)
plt.show()
I apologize for the oversight. It seems I forgot to import the matplotlib library which is used for creating plots. Let's correct that and proceed.
import matplotlib.pyplot as plt
plt.figure(figsize=(12,6))
plt.scatter(data_new['year'], data_new['citations'], color = 'blue', label='Citations')
plt.scatter(data_new['year'], data_new['no of papers'], color = 'red', label='No of Papers')
plt.plot(data_new['year'], y_citations_pred, color = 'blue', linestyle = '--', label='Citations Linear Fit')
plt.plot(data_new['year'], y_papers_pred, color = 'red', linestyle = '--', label='No of Papers Linear Fit')
plt.plot(data_new['year'], y_citations_poly_pred, color = 'blue', linestyle = ':', label='Citations Polynomial Fit')
plt.plot(data_new['year'], y_papers_poly_pred, color = 'red', linestyle = ':', label='No of Papers Polynomial Fit')
plt.title('Citations and No of Papers over Years with Linear and Polynomial Fits')
plt.xlabel('Year')
plt.ylabel('Count')
plt.legend()
plt.grid(True)
plt.show()
The plot above shows the original data for 'Citations' and 'No of Papers' along with the fitted linear and polynomial curves for each.
The linear fit provides a simple model of the overall trend, while the polynomial fit can capture more complex patterns in the data. However, the polynomial fit can also be more susceptible to fluctuations in the data and may not generalize as well to future data.
From the graph, it's evident that both the number of papers and citations have a general upward trend over the years. The number of citations seems to have a sharper increase compared to the number of papers, indicating that each paper might be getting more citations over the years. The polynomial fit captures the fluctuation in the data more accurately than the linear fit.
If you have more specific questions or if there's another aspect of the data you'd like to explore, please let me know!
Thoughts: Not bad the linear fit is reasonable. Especially " indicating that each paper might be getting more citations over the years. " is a valid conclusion. The poly fitting worked but is not great technique in this case.
All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon
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