Matplotlib で表を作成し、png形式で画像として保存する

Matplotlibで表を作成する場合、一度グラフと表を作成し、表だけ表示を消す。

import matplotlib.pyplot as plt
import six

fig = plt.figure()
ax = fig.add_subplot(111)

col_labels = ['col1', 'col2', 'col3']
row_labels = ['row1', 'row2', 'row3']
table_vals = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]

# Draw table
the_table = plt.table(cellText=table_vals,
colWidths=[0.1] * 3,
rowLabels=row_labels,
colLabels=col_labels,
loc='center')
the_table.auto_set_font_size(False)
the_table.set_fontsize(24)
the_table.scale(4, 4)

# Removing ticks and spines enables you to get the figure only with table
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
plt.tick_params(axis='y', which='both', right=False, left=False, labelleft=False)
for pos in ['right','top','bottom','left']:
plt.gca().spines[pos].set_visible(False)
plt.savefig('matplotlib-table', bbox_inches='tight', pad_inches=0.05)
plt.show()

(1) fig = plt.figure(figsize=(width height))
figの大きさを指定できる。値を指定しない場合のデフォルトは
[6.4, 4.8].

(2) Titleを付ける場合

ax.set_title(label='TOP 10 Stock Price Gainer ', fontsize=25,
　　　　　　 loc="left", pad=0.5,
                 backgroundcolor='green',
                 color='white',
                 verticalalignment= 'baseline')

(2) ax =fig.add_subplot(111)
# figure内の枠の大きさとどこに配置している。subplot(行の数,列の数,何番目に配置しているか)

(3) 表を作成する

(Subplot Parameterの指定）
   plt.subplots_adjust(
        left=0.22,
        bottom=0.11,
        right=0.65,
        top=0.88,
        wspace=0.20,
        hspace=0.20)

left = 0.125 # the left side of the subplots of the figure
right = 0.9   # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.9     # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for space between subplots,
              # expressed as a fraction of the average axis width
hspace = 0.2 # the amount of height reserved for space between subplots,
              # expressed as a fraction of the average axis height

（列幅の指定）
colWidths=[0.1] * 3,

列幅を変える場合は、それぞれ指定する。
colWidths=[0.5, 0.1, 0.2]

（テーブルの場所）
loc=’center’　：表を中央に配置する。

(画像ファイルにした際に表示される大きさ（Scale）)
   the_table.scale(5, 5)
( width 5 inches, and its height 5 inches.)
(4)　表の表示の削除
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
plt.tick_params(axis='y', which='both', right=False, left=False, labelleft=False)
又は
plt.axis(‘off’)

（セルの色を変える）

    # Cell Coloring
    for col in range(data_len):
        col_num= data_len-(col+1)
        if data['Close'][col_num]-data['Close'][col_num-1]>0:
            the_table[(col+1,5)].set_facecolor('#adff2f')
        else:
            the_table[(col+1,5)].set_facecolor('#ff0000')

(セルの色を変える：)
table.properties()メソッドやset_text_props()等を使って変更
https://matplotlib.org/3.1.0/api/table_api.html

    # Cell Coloring
    table_props = the_table.properties()
    table_cells = table_props['child_artists']
    for i in range(4):
        the_table[(0,i)].set_facecolor('green')
        the_table[(0,i)].get_text().set_fontsize('25')
        the_table[(0,i)].get_text().set_color('white')
        the_table[(0,i)].get_text().set_fontweight('bold')
    for i in range(len(to_csv)):
        n =i +1
        the_table[(n,0)].get_text().set_fontweight('bold')
        the_table[(n,0)].get_text().set_fontsize('35')

https://www.geeksforgeeks.org/matplotlib-axes-axes-table-in-python/

# Implementation of matplotlib function 

import matplotlib.pyplot as plt 

val1 = ["{:X}".format(i) for i in range(10)] 

val2 = ["{:02X}".format(10 * i) for i in range(10)] 

val3 = [["" for c in range(10)] for r in range(10)] 

fig, ax = plt.subplots() 

ax.set_axis_off() 

table = ax.table( 

    cellText = val3,  

    rowLabels = val2,  

    colLabels = val1, 

    rowColours =["palegreen"] * 10,  

    colColours =["palegreen"] * 10, 

    cellLoc ='center',  

    loc ='upper left')         

ax.set_title('matplotlib.axes.Axes.table() function Example', 

             fontweight ="bold") 

plt.show() 

=================================================================================

こんな図

import yfinance as yf
import pandas as pd    
import numpy as np     
import matplotlib.pyplot as plt
import datetime
import matplotlib.dates as mdates
from matplotlib.dates import drange
from matplotlib.dates import DateFormatter


ticker_name="VOO"
DJIA = yf.Ticker(ticker_name)
data=DJIA.history(
    period='5d'
    )
data2=DJIA.history(
    period='6d'
    )

data.drop(columns=['Dividends','Stock Splits'],inplace=True)
print(data)

# Row Label
row_labels_pre_sorted= []
row_labels=[]


for d in data["Close"].keys():
    modified_d = d.strftime('%Y-%m-%d')
    
    row_labels_pre_sorted.append(modified_d)

row_labels = sorted(row_labels_pre_sorted, reverse=True)

# Value 
row_name=[]
for Open, High, Low, Close, Volume in zip(data['Open'],data['High'],data['Low'],data['Close'],data['Volume']):
    open_p = '{:,.2f}'.format(Open)
    row_name.append(open_p)
    high_p = '{:,.2f}'.format(High)
    row_name.append(high_p)
    Low_p = '{:,.2f}'.format(Low)
    row_name.append(Low_p)
    Close_p = '{:,.2f}'.format(Close)
    row_name.append(Close_p)
    row_name.append(Volume/1000000)



list1= row_name[:5]
list2= row_name[5:10]
list3= row_name[5+5:10+5]
list4= row_name[5+5*2:10+5*2]
list5= row_name[5+5*3:10+5*3]

val_list =[list5, list4, list3,list2,list1]

table_vals = val_list


# New table

fig = plt.figure(figsize=(12, 4))

ax = fig.add_subplot(111)
ax.set_title('Ticker Name :  '+ticker_name, fontsize=36, pad=50)
#fig.suptitle('Ticker:  Dow Jones Industry Average(DJIA)', fontsize=32)

row1 = 'Open'
row2 = 'High'
row3 = 'Low'
row4 = 'Close'
row5 = 'Volume(Mil)'

col_labels = [row1, row2,row3,row4,row5]

# Draw table
the_table = plt.table(cellText=table_vals,
                      colWidths=[0.1] * 5,
                      rowLabels=row_labels,
                      colLabels=col_labels,
                      loc='center')
the_table.auto_set_font_size(False)
the_table.set_fontsize(20)

# Cell Coloring

if data['Close'][4]-data['Close'][3]>0:
    the_table[(1,3)].set_facecolor('#adff2f')
else:
    the_table[(1,3)].set_facecolor('#ff0000')

if data['Close'][3]-data['Close'][2]>0:
    the_table[(2,3)].set_facecolor('#adff2f')
else:
    the_table[(2,3)].set_facecolor('#ff0000')

if data['Close'][2]-data['Close'][1]>0:
    the_table[(3,3)].set_facecolor('#adff2f')
else:
    the_table[(3,3)].set_facecolor('#ff0000')

if data['Close'][1]-data['Close'][0]>0:
    the_table[(4,3)].set_facecolor('#adff2f')
else:
    the_table[(4,3)].set_facecolor('#ff0000')

if data['Close'][1]-data2['Close'][0]>0:
    the_table[(5,3)].set_facecolor('#adff2f')
else:
    the_table[(5,3)].set_facecolor('#ff0000')
    

the_table.scale(4, 4)

# Removing ticks and spines enables you to get the figure only with table
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
plt.tick_params(axis='y', which='both', right=False, left=False, labelleft=False)

for pos in ['right','top','bottom','left']:
    plt.gca().spines[pos].set_visible(False)

plt.savefig(ticker_name, bbox_inches='tight', pad_inches=0.05)
plt.show()

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2020年6月22日月曜日

Matplotlib で表を作成し、png形式で画像として保存する

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