Problem with linear regression and summarize

Question

I would like to create a plot of my linear regression model showing bike sales for each year summed up at one point, and not like now that there are two points separately.

This is my code:

from sklearn.linear_model import LinearRegression
from sklearn import datasets, linear_model

## Wzrost lub maleje zakup rowerow
## (Purchase of bicycles increases or decreases)
plot1 = df.groupby('Year')['Product_Category'].value_counts().rename('count').reset_index()

x = plot1['Year'].values.reshape(-1, 1)
y = plot1['count'].values.reshape(-1, 1)

# plot #
## linear ##
regr = linear_model.LinearRegression()
regr.fit(x, y)
y_pred = regr.predict(x_test)

#plot#
plt.scatter(x, y,  color='black')
plt.plot(x, y, color='blue', linewidth=3)

This is my plot:

Yes excatly, but i dont know why I have two points each year instead of one accumulated one — Przemek Dabek
– Przemek Dabek, Commented Jan 12, 2021 at 16:21

antoine · Accepted Answer · 2021-01-12 16:55:49Z

1

As what I can understand from your example, this maybe a solution, replace value_counts by count.

Example data:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'Year': [ 2019, 2019, 2020, 2021], 'Product_Category': ['a', 'b', 'c', 'd']})
print(df)
   Year Product_Category
0  2019                a
1  2019                b
2  2020                c
3  2021                d

The count will return:

plot1 = df.groupby('Year')['Product_Category'].count().rename('count').reset_index()
print(plot1)

  Year  count
0  2019      2
1  2020      1
2  2021      1


plot1 = df.groupby('Year')['Product_Category'].count().rename('count').reset_index()
#x,y#
x = plot1['Year'].values.reshape(-1, 1)
y = plot1['count'].values.reshape(-1, 1)
# plot #

#plot#
plt.scatter(x, y,  color='black')
plt.plot(x, y, color='blue', linewidth=3)

edited Jan 12, 2021 at 16:55

answered Jan 12, 2021 at 16:50

antoine

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