[雪峰磁针石博客]Bokeh数据可视化工具3其他数据结构绘图
使用numpy创建线状图
#Creating line plots using NumPy arrays
#Import required packages
import numpy as np
import random
from bokeh.io import output_file, show
from bokeh.plotting import figure
#Creating an array for the points along the x and y axes
array_x =np.array([1,2,3,4,5,6])
array_y = np.array([5,6,7,8,9,10])
#Creating a line plot
plot = figure()
plot.line(array_x, array_y)
#Output the plot
output_file('numpy_line.html')
show(plot)
image.png
使用numpy创建散列图
#Creating scatter plots using NumPy arrays
#Import required packages
import numpy as np
import random
from bokeh.io import output_file, show
from bokeh.plotting import figure
#Creating arrays for two different categories of points
x_red = np.array([1,2,3,4,5])
y_red = np.array([5,6,7,8,9])
x_blue = np.array([10,11,12,13])
y_blue = np.array([14,15,16,17])
#Creating the categorical scatter plot
plot = figure()
plot.circle(x_red, y_red, size = 9, color = 'red', alpha = 0.8)
plot.circle(x_blue, y_blue, size = 9, color = 'blue', alpha = 0.8)
#Output the plot
output_file('numpy_scatter.html')
show(plot)
image.png
使用pandas DataFrame创建时序图
苹果股票的高值：
#Creating a time series plot using a Pandas DataFrame
#Importing the required packages
import pandas as pd
#Read in the data
df = pd.read_csv('/home/andrew/code/kaggle-code/stock_data/all_stocks_5yr.csv')
#Filtering for apple stocks
df_apple = df[df['Name'] == 'AAL']
#df_apple.loc['date'] = df_apple['date'].astype('datetime64')
df_apple['date'] = pd.to_datetime(df_apple['date'])
print(df_apple.dtypes)
#Import the required packages
from bokeh.io import output_file, show
from bokeh.plotting import figure
#Create the time series plot
plot = figure(x_axis_type = 'datetime', x_axis_label = 'date', y_axis_label = 'High Prices')
plot.line(x = df_apple['date'], y = df_apple['high'])
#Output the plot
output_file('pandas_time.html')
show(plot)
image.png
- 参考资料
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.astype.html
https://stackoverflow.com/questions/20625582/how-to-deal-with-settingwithcopywarning-in-pandas
另外一个更简单的演示：
import pandas as pd
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.layouts import row
output_file('fig.html')
test = pd.DataFrame({'datetime':pd.date_range('1/1/1880', periods=2000),'foo':np.arange(2000)})
print(test['datetime'])
fig = figure(x_axis_type="datetime")
fig.line(x='datetime',y='foo', source=test)
test = test.set_index('datetime')
fig2 = figure(x_axis_type="datetime")
fig2.line(x='datetime', y='foo', source=test)
show(row(fig, fig2))
image.png
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参考资料
https://stackoverflow.com/questions/34974615/timeseries-in-bokeh-using-a-dataframe-with-index
https://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html
