[PYTHON] Data visualization method using matplotlib (+ pandas) (3)

[Matplotlib](http://matplotlib. We will focus on data visualization with org /). From this time, it will be a combination technique in combination with pandas.

Line graph

When plotting objects in a series or data frame, it defaults to a line chart.

import numpy as np
from pandas import *
from pylab import *
import matplotlib.pyplot as plt
from numpy.random import randn

#Series simple plotting
s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))
s.plot()

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

#Simple plotting of data frames
df = DataFrame(np.random.randn(10, 4).cumsum(0),
               columns=['A','B','C','D'],
               index=np.arange(0, 100, 10))
df.plot()
plt.show()
plt.savefig("image2.png ")

Most of the methods for plotting in pandas can optionally specify a matplotlib subplot object for the ax parameter.

For a list of options you can specify for plot, you may want to refer to the official documentation below.

pandas.DataFrame.plot http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.DataFrame.plot.html

bar graph

The most commonly used is kind, which allows you to specify a linetype. If kind ='bar', it will be a bar graph.

#Visualize the series
data = Series(np.random.randn(16), index=list('abcdefghijklmnop'))
#Vertical bar graph
data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)
#Horizontal bar graph
data.plot(kind='barh', ax=axes[1], color='r', alpha=0.6)

plt.show()
plt.savefig("image3.png ")

If you make the data frame a bar chart, the values in each row are grouped together.

#Visualize the data frame
df = DataFrame(np.random.randn(6, 4),
               index=['1','2','3','4','5','6'],
               columns=Index(['A','B','C','D'], name='Genus'))

print( df )
# =>
# Genus         A         B         C         D
# 1     -0.350817 -0.017378 -0.991230 -0.223608
# 2      0.478712 -0.472764  0.677484 -0.852312
# 3      1.402219  0.381440  0.370080  0.682125
# 4     -1.733590  0.296124 -0.014841  1.140705
# 5      0.373399  1.150718  1.341984  1.040759
# 6     -0.013301 -0.202793 -1.367493 -0.572954

df.plot()
plt.show()
plt.savefig("image4.png ")

df.plot(kind='bar') #Make a bar graph
plt.show(grid=False, alpha=0.8)
plt.savefig("image5.png ")

df.plot(kind='barh', stacked=True, alpha=0.5) #Make a stacked bar graph(stacked option)
plt.show()
plt.savefig("image6.png ")

Visualize financial data

Previous [Analysis of financial data and its visualization](http://qiita.com/ynakayama/items/32b1ca2a108876f889cc Try plotting with pandas + matplotlib using csv used in).

df = read_csv('stock_px.csv') #Read CSV

print( df.head(10) ) #The beginning of the data frame
# =>
# [6 rows x 4 columns]
#             Unnamed: 0  AAPL   MSFT    XOM     SPX
# 0  2003-01-02 00:00:00  7.40  21.11  29.22  909.03
# 1  2003-01-03 00:00:00  7.45  21.14  29.24  908.59
# 2  2003-01-06 00:00:00  7.45  21.52  29.96  929.01
# 3  2003-01-07 00:00:00  7.43  21.93  28.95  922.93
# 4  2003-01-08 00:00:00  7.28  21.31  28.83  909.93
# 5  2003-01-09 00:00:00  7.34  21.93  29.44  927.57
# 6  2003-01-10 00:00:00  7.36  21.97  29.03  927.57
# 7  2003-01-13 00:00:00  7.32  22.16  28.91  926.26
# 8  2003-01-14 00:00:00  7.30  22.39  29.17  931.66
# 9  2003-01-15 00:00:00  7.22  22.11  28.77  918.22

df.plot()
plt.show()
plt.savefig("image7.png ")

It was very easy to visualize the CSV data.