Sea Level Predictor

Hi people,

Despite being almost a copy-paste from the first part that is correctly executing, in the second keep getting the following: LinAlgError: SVD did not converge in Linear Least Squares

CSV file:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import linregress

df =pd.read_csv(r'')

#Use matplotlib to create a scatter plot using the Year column as the x-
#axis and the CSIRO Adjusted Sea Level column as the y-axis
#fig, ax = plt.subplots(figsize=(16, 9))
plt.scatter(df['Year'], df['CSIRO Adjusted Sea Level']) #Unhide if you wanna plot separately

#Use the linregress function from scipy.stats to get the slope and y-
#intercept of the line of best fit. Plot the line of best fit over the 
#top of the scatter plot. Make the line go through the year 2050 to 
#predict the sea level rise in 2050.
#result = linregress(df["Year"], df["CSIRO Adjusted Sea Level"])
extra_years = {"Year": pd.Series(range(2014,2051))}
df1 = pd.concat([df,pd.DataFrame(extra_years)])
df1 = df1.drop(columns=['Lower Error Bound','Upper Error Bound','NOAA Adjusted Sea Level'])
df2 = df1.fillna(0)
mymodel = np.poly1d(np.polyfit(df["Year"], df['CSIRO Adjusted Sea Level'], 3)) #m,b = np.polyfit(x, y, 1)
#myline = np.linspace(1880, 2050)
df2 = plt.plot(df2['Year'], m*df1['Year'] + b, color='r') #linear regression
df3 = plt.plot(myline, mymodel(myline), color='g') #polinomial regression is more accurate in this case #Unhide if you wanna plot separately

#Plot a new line of best fit just using the data from year 2000 through 
#the most recent year in the dataset. Make the line also go through the 
#year 2050 to predict the sea level rise in 2050 if the rate of rise 
#continues as it has since the year 2000.
df2000 = df1.copy()
df2000 = df2000[(df2000['Year'] >= 2000) & (df2000['Year'] <= 2050)]
plt.scatter(df2000['Year'], df2000['CSIRO Adjusted Sea Level'])

m,b = np.polyfit(df2000['Year'], df2000['CSIRO Adjusted Sea Level'], 1) #m,b = np.polyfit(x, y, 1)
#myline2000 = np.linspace(1880, 2050)
df3000 = plt.plot(df2000['Year'], m*df2000['Year'] + b, color='r') #linear regression
#df4000 = plt.plot(myline, mymodel2000(myline), color='g') #polinomial regression is more accurate in this case


This means you’ve set up an unsolvable math problem in your code

This line looks suspicious. It doesn’t generate an error for you?

I just removed it when posting here cause it wont work for anyone else

Yeah i guess so… Any suggestion for getting it running?

Removed what? If you changed the code before posting, that makes it harder to trace the error.

I think he just removed the filename here:

df =pd.read_csv(r’')

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I’m not able to reproduce your error. I got the error:

NameError: name ‘m’ is not defined

So I uncommented this line:

m,b = np.polyfit(x, y, 1)

but now I get the error:

NameError: name ‘x’ is not defined

Did you fork the FCC boilerplate for this code, or you are writing it from scratch? I notice that it does not start with

def draw_plot():

as the boilerplate does

Here im using the Jupyter notebook, and writting from scratch

You might want to use the boilerplate I think it has a helpful format and steps, as well as a few crucial structural pieces for the tests to work when you import it into replit or however you run the tests.

Can you explain the errors I got with your code, or why I wasn’t able to replicate your error?

I ran it on Google Colab because replit was too slow and laggy and kept checking dependencies and doing weird things.

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Will look into the boilerplate, just wanna from scratch cause i have not much experinence and wanna learn as much as possible and look it from different perspectives.

Later on will try with Google Colab , yeah replit is quite painful and i cant open the boilerplates there.

Now busy so when further investigating will tell you, thank you guys

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