折线图示例
#!/usr/bin/python2.7 import numpy as np
from matplotlib import pyplot as plt
from dbtools import raw_data
from utils import moving_sum def moving_sum(array, window):
if type(array) is not np.ndarray:
raise TypeError('Expected one dimensional numpy array.') remainder = array.size % window
if 0 != remainder:
array = array[remainder:]
array = array.reshape((array.size/window,window))
sum_arr = np.sum(array,axis=1) return sum_arr def run():
window = 3
y_lst = raw_data('2018-08-03 00:00:00', length=3600*24)
raw_arr = np.array(y_lst) sum_arr = moving_sum(raw_arr,window)
res = np.true_divide(sum_arr[1:],sum_arr[:-1]) threshold = 0.5
upper = np.array([1+threshold]*res.size)
lower = np.array([1-threshold]*res.size) plt.plot(upper,lw=1,color='red',label='Upper')
plt.plot(res,lw=1,color='blue',label='Trend')
plt.plot(lower,lw=1,color='red',label='Lower') r_idx = np.argwhere(np.abs(res-1) > 0.5)
plt.plot(r_idx, res[r_idx], 'ro') plt.legend()
plt.show() return (r_idx,res[r_idx])
画布和子图
import numpy as np
import matplotlib.pyplot as plt fig = plt.figure(figsize=[10,8])
ax1 = fig.add_subplot(2,1,1)
x1 = np.linspace(0.1,10,99,endpoint = False)
y1 = np.log(x1)
ax1.plot(x1,y1)
ax1.set_title('Logarithmic function') x2 = np.linspace(0, 5, num = 100)
y2 = np.e**x2
ax2 = fig.add_subplot(2,1,2)
ax2.plot(x2,y2)
ax2.set_title('Exponential function') plt.subplots_adjust(hspace =0.2)
fig.show()
柱状图
import numpy as np
import matplotlib.pyplot as plt data = [32,48,19,85]
labels = ['Jan','Feb','Mar','Apr'] plt.bar(np.arange(len(data)),data,color='lightgreen')
plt.xticks(np.arange(len(data)),labels)
plt.show()
饼状图
import numpy as np
import matplotlib.pyplot as plt data = [35,47,13,5]
labels = ['Bili','iQiYi','Tencent','YouKu']
plt.pie(data,labels=labels,autopct="%.2f%%",explode=[0.1,0,0,0],shadow=True)
plt.show()
Matplotlib画正弦余弦曲线
K线图
pip3.6 install https://github.com/matplotlib/mpl_finance/archive/master.zip from mpl_finance import candlestick_ochl
matplotlib.finance has been deprecated
# https://github.com/Gypsying/iPython/blob/master/601318.csv
In [2]: import pandas as pd
In [3]: df = pd.read_csv('601318.csv', index_col='date', parse_dates=['date'])
In [4]: df.head()
Out[4]:
Unnamed: 0 open close high low volume code
date
2007-03-01 0 21.878 20.473 22.302 20.040 1977633.51 601318
2007-03-02 1 20.565 20.307 20.758 20.075 425048.32 601318
2007-03-05 2 20.119 19.419 20.202 19.047 419196.74 601318
2007-03-06 3 19.253 19.800 20.128 19.143 297727.88 601318
2007-03-07 4 19.817 20.338 20.522 19.651 287463.78 601318
In [5]: from matplotlib.dates import date2num
In [6]: df['time'] = date2num(df.index.to_pydatetime())
In [7]: df.head()
Out[7]:
Unnamed: 0 open close high low volume code time
date
2007-03-01 0 21.878 20.473 22.302 20.040 1977633.51 601318 732736.0
2007-03-02 1 20.565 20.307 20.758 20.075 425048.32 601318 732737.0
2007-03-05 2 20.119 19.419 20.202 19.047 419196.74 601318 732740.0
2007-03-06 3 19.253 19.800 20.128 19.143 297727.88 601318 732741.0
2007-03-07 4 19.817 20.338 20.522 19.651 287463.78 601318 732742.0
In [8]: df.shape
Out[8]: (2563, 8)
In [9]: df.size
Out[9]: 20504
In [10]: 2563*8
Out[10]: 20504
In [11]:
数据源
import numpy as np
import pandas as pd
from matplotlib.dates import date2num
from mpl_finance import candlestick_ochl # 构建 candlestick_ochl 的sequence of (time, open, close, high, low, ...)
df = pd.read_csv('601318.csv', index_col='date', parse_dates=['date'])
# time must be in float days format - see date2num
df['time'] = date2num(df.index.to_pydatetime())
# 原始数据比较多，截取一部分做展示
df = df.iloc[:300,:]
arr = df[['time','open','close','high','low']].values fig = plt.figure(figsize=[14,7])
ax = fig.add_subplot(1,1,1) candlestick_ochl(ax,arr) plt.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
fig.show()
