PythonNewb PythonNewb - 6 months ago 21
Python Question

How to add a line of best fit to scatter plot

I'm currently working with Pandas and matplotlib to perform some data visualization and I want to add a line of best fit to my scatter plot.

Here is my code:

import matplotlib
import matplotlib.pyplot as plt
import pandas as panda
import numpy as np

def PCA_scatter(filename):

matplotlib.style.use('ggplot')

data = panda.read_csv(filename)
data_reduced = data[['2005', '2015']]

data_reduced.plot(kind='scatter', x='2005', y='2015')
plt.show()

PCA_scatter('file.csv')


How do I go about this?

Answer

You can use np.polyfit() and np.poly1d(). Estimate a first degree polynomial using the same x values, and add to the ax object created by the .scatter() plot. Using an example:

import numpy as np

     2005   2015
0   18882  21979
1    1161   1044
2     482    558
3    2105   2471
4     427   1467
5    2688   2964
6    1806   1865
7     711    738
8     928   1096
9    1084   1309
10    854    901
11    827   1210
12   5034   6253

Estimate first-degree polynomial:

z = np.polyfit(x=df.loc[:, 2005], y=df.loc[:, 2015], deg=1)
p = np.poly1d(z)
df['trendline'] = p(df.loc[:, 2005])

     2005   2015     trendline
0   18882  21979  21989.829486
1    1161   1044   1418.214712
2     482    558    629.990208
3    2105   2471   2514.067336
4     427   1467    566.142863
5    2688   2964   3190.849200
6    1806   1865   2166.969948
7     711    738    895.827339
8     928   1096   1147.734139
9    1084   1309   1328.828428
10    854    901   1061.830437
11    827   1210   1030.487195
12   5034   6253   5914.228708

and plot:

ax = df.plot.scatter(x=2005, y=2015)
df.set_index(2005, inplace=True)
df.trendline.sort_index(ascending=False).plot(ax=ax)
plt.gca().invert_xaxis()

To get:

enter image description here

Also provides the the line equation:

'y={0:.2f} x + {1:.2f}'.format(z[0],z[1])

y=1.16 x + 70.46
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