Python Matplotlib繪圖數(shù)據(jù)點(diǎn)位置錯(cuò)亂問(wèn)題解決方法

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在繪制正負(fù)樣本在各個(gè)特征維度上的CDF（累積分布）圖時(shí)出現(xiàn)了以下問(wèn)題：

問(wèn)題具體表現(xiàn)為：

1.幾個(gè)負(fù)樣本的數(shù)據(jù)點(diǎn)位置倒錯(cuò)

2.X軸刻度變成了亂七八糟一團(tuán)鬼東西

最終解決辦法

造成上述情況的原因其實(shí)是由于輸入matplotlib.plot()函數(shù)的數(shù)據(jù)x_data和y_data從CSV文件中直接導(dǎo)入后格式為string，因此才會(huì)導(dǎo)致所有數(shù)據(jù)點(diǎn)的x坐標(biāo)都被直接刻在了x軸上，且由于坐標(biāo)數(shù)據(jù)格式錯(cuò)誤，部分點(diǎn)也就表現(xiàn)為“亂點(diǎn)”。解決辦法就是導(dǎo)入x，y數(shù)據(jù)后先將其轉(zhuǎn)化為float型數(shù)據(jù)，然后輸入plot()函數(shù)，問(wèn)題即解決。

補(bǔ)充知識(shí)：matplotlib如何在繪制時(shí)間序列時(shí)跳過(guò)無(wú)數(shù)據(jù)的區(qū)間

其實(shí)官方文檔里就提供了方法,這里簡(jiǎn)單的翻譯并記錄一下.

11.1.9 Skip dates where there is no data
When plotting time series, e.g., financial time series, one often wants to leave out days on which there is no data, e.g., weekends.
By passing in dates on the x-xaxis, you get large horizontal gaps on periods when there is not data.

The solution is to pass in some proxy x-data, e.g., evenly sampled indices, and then use a custom formatter to format these as dates.
The example below shows how to use an ‘index formatter' to achieve the desired plot:

解決方案是通過(guò)傳遞x軸數(shù)據(jù)的代理,比如下標(biāo),

然后通過(guò)自定義的'formatter'去取到相對(duì)應(yīng)的時(shí)間信息

manual內(nèi)示例代碼:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.ticker as ticker

#讀數(shù)據(jù)
r = mlab.csv2rec('../data/aapl.csv')
r.sort()
r = r[-30:] # get the last 30 days
N = len(r)
ind = np.arange(N) # the evenly spaced plot indices
def format_date(x, pos=None):
 #保證下標(biāo)不越界,很重要,越界會(huì)導(dǎo)致最終plot坐標(biāo)軸label無(wú)顯示
 thisind = np.clip(int(x+0.5), 0, N-1)
 return r.date[thisind].strftime('%Y-%m-%d')

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(ind, r.adj_close, 'o-')
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
fig.autofmt_xdate()
plt.show()

示例:

同樣一段數(shù)據(jù)上為原始,下為去掉無(wú)數(shù)據(jù)間隔區(qū)間

import pandas as PD
import numpy as NP
import matplotlib.pyplot as PLT
import matplotlib.ticker as MTK

file = r'vix_series.csv'
df = PD.read_csv(file, parse_dates=[0, 2])
#用下標(biāo)代理原始時(shí)間戳數(shù)據(jù)
idx_pxy = NP.arange(df.shape[0])
#下標(biāo)-時(shí)間轉(zhuǎn)換func
def x_fmt_func(x, pos=None):
 idx = NP.clip(int(x+0.5), 0, df.shape[0]-1)
 return df['datetime'].iat[idx]
#繪圖流程
def decorateAx(ax, xs, ys, x_func):
 ax.plot(xs, ys, color="green", linewidth=1, line)
 ax.plot(ax.get_xlim(), [0,0], color="blue", 
   linewidth=0.5, line)
 if x_func:
  #set數(shù)據(jù)代理func
  ax.xaxis.set_major_formatter(MTK.FuncFormatter(x_func))
 ax.grid(True)
 return

fig = PLT.figure()
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
decorateAx(ax1, df['datetime'], df['vix_all'], None)
decorateAx(ax2, idx_pxy, df['vix_all'], x_fmt_func)
#優(yōu)化label顯示,非必須
fig.autofmt_xdate()
PLT.show()

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