Data visualization method using matplotlib (+ pandas) (5)

This is the final episode of the data visualization story that continued until previous.

Scatter plot

We will use the data from pydata-book as before.

pydata-book/ch08/macrodata.csv https://github.com/pydata/pydata-book/blob/master/ch08/macrodata.csv

import numpy as np
from pandas import *
import matplotlib.pyplot as plt

#Read CSV data
macro = read_csv('macrodata.csv')

#Pick up some columns
data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]

# .diff()The method changes the value to the difference from the previous row
#Because it starts with NaN.dropna()Remove with method
trans_data = np.log(data).diff().dropna()

# trans_data will be a dataset showing the changes from the previous row
#Show last 5 lines
print( trans_data[-5:] )
# =>
#           cpi        m1  tbilrate     unemp
# 198 -0.007904  0.045361 -0.396881  0.105361
# 199 -0.021979  0.066753 -2.277267  0.139762
# 200  0.002340  0.010286  0.606136  0.160343
# 201  0.008419  0.037461 -0.200671  0.127339
# 202  0.008894  0.012202 -0.405465  0.042560

#Plot a scatter plot from two columns
plt.scatter(trans_data['m1'], trans_data['unemp'])

plt.show()
plt.savefig("image.png ")

image.png

Scatterplot matrix

[Scatter Plot Matrix](http://www.okada.jp.org/RWiki/?%A5%B0%A5%E9%A5%D5%A5%] is a scatter plot of all pairs of a series of variables. A3% A5% C3% A5% AF% A5% B9% BB% B2% B9% CD% BC% C2% CE% E3% BD% B8% A1% A7% BB% B6% C9% DB% BF% DE% It is B9% D4% CE% F3). You can create this with the scatter_matrix function.

#Generate a scatterplot matrix
from pandas.tools.plotting import scatter_matrix
scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3)

plt.show()
plt.savefig("image2.png ")

image2.png

It serves as a simple and powerful way to look at the correlation of any two 1D data.

reference

Introduction to data analysis with Python-Data processing using NumPy and pandas http://www.oreilly.co.jp/books/9784873116556/

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