Matplotlib中未用“ fill_between”正確填充的行之間的區域
[英]Areas between lines not filled correctly with “fill_between” in Matplotlib
問題描述
用Matplotlib將時間序列數據集繪制為：
import matplotlib.pyplot as plt
%matplotlib inline
def WillR(ohlc, window):
high, low, close = ohlc['High'], ohlc['Low'], ohlc['Close']
R = (np.nanmax(high, axis=0) - close) / (np.nanmax(high, axis=0) - np.nanmin(low, axis=0)) * -100
return R
ohlc = df[::-1]
R = WillR(ohlc, 14)
xAxis = ohlc.index
fig = plt.figure(figsize=(18,9), dpi=100)
ax = fig.add_axes([0.0, 0.6, 1.0, 0.6])
plt.ylabel('Williams '+'%R', fontsize = 16)
ax.xaxis.grid(color='grey', linestyle=':')
ax.yaxis.grid(color='grey', linestyle=':')
ax.plot(R, color='k', alpha=0.8, linewidth=1.2)
ax.axhline(-20, color='r', linewidth=0.8)
ax.axhline(-50, color='k', linestyle='dashed', linewidth=1.2)
ax.axhline(-80, color='g', linewidth=0.8)
ax.fill_between(xAxis, R, -20, where=(R >= -20), facecolor='r', edgecolor='r', alpha=0.5)
ax.fill_between(xAxis, R, -80, where=(R <= -80), facecolor='g', edgecolor='g', alpha=0.5)
在“威廉姆斯％R”圖中，“-20”行上方的區域將用紅色填充，而“ -80”行下方的區域將用紅色填充。 我無法完全填充邊界區域-參見圖1：
我還嘗試添加參數“ interpolate = True”，結果仍然不令人滿意-見圖2：
如何使其正常工作？ 謝謝
df的樣本數據如下：
Date Open High Low Close
Date
2017-06-27 2017-06-27 10.75 10.850 10.665 10.70
2017-06-26 2017-06-26 10.57 10.740 10.570 10.69
2017-06-23 2017-06-23 10.59 10.710 10.500 10.63
2017-06-22 2017-06-22 10.56 10.720 10.530 10.65
2017-06-21 2017-06-21 10.75 10.750 10.500 10.53
2017-06-20 2017-06-20 10.88 10.925 10.780 10.81
2017-06-19 2017-06-19 10.77 10.920 10.760 10.82
2017-06-16 2017-06-16 10.74 10.910 10.740 10.78
2017-06-15 2017-06-15 10.86 10.930 10.690 10.76
2017-06-14 2017-06-14 10.96 11.040 10.910 11.02
2017-06-13 2017-06-13 10.57 10.930 10.570 10.93
2017-06-09 2017-06-09 10.59 10.650 10.500 10.57
2017-06-08 2017-06-08 10.28 10.590 10.280 10.57
2017-06-07 2017-06-07 10.35 10.420 10.290 10.35
2017-06-06 2017-06-06 10.66 10.700 10.280 10.38
2017-06-05 2017-06-05 10.80 10.850 10.560 10.70
2017-06-02 2017-06-02 11.32 11.390 11.190 11.19
2017-06-01 2017-06-01 11.26 11.330 11.160 11.20
2017-05-31 2017-05-31 11.22 11.400 11.190 11.27
2017-05-30 2017-05-30 11.10 11.260 11.070 11.23
2017-05-29 2017-05-29 11.36 11.370 11.045 11.08
2017-05-26 2017-05-26 11.55 11.590 11.320 11.36
2017-05-25 2017-05-25 11.62 11.670 11.455 11.66
2017-05-24 2017-05-24 11.67 11.755 11.590 11.62
2017-05-23 2017-05-23 11.77 11.835 11.590 11.64
2017-05-22 2017-05-22 12.06 12.110 11.800 11.83
2017-05-19 2017-05-19 12.15 12.235 11.990 12.03
2017-05-18 2017-05-18 12.00 12.130 11.910 12.13
2017-05-17 2017-05-17 12.28 12.300 12.120 12.14
2017-05-16 2017-05-16 12.23 12.385 12.220 12.30
2017-05-15 2017-05-15 12.10 12.255 12.030 12.22
2017-05-12 2017-05-12 12.30 12.300 12.050 12.12
2017-05-11 2017-05-11 12.30 12.445 12.270 12.34
2017-05-10 2017-05-10 11.99 12.415 11.950 12.25
2017-05-09 2017-05-09 12.07 12.100 11.730 11.79
2017-05-08 2017-05-08 12.14 12.230 12.060 12.10
2017-05-05 2017-05-05 12.24 12.250 12.020 12.04
2017-05-04 2017-05-04 12.23 12.270 12.050 12.14
2017-05-03 2017-05-03 12.43 12.460 12.200 12.23
2017-05-02 2017-05-02 12.44 12.470 12.310 12.46
2017-05-01 2017-05-01 12.25 12.440 12.230 12.44
2017-04-28 2017-04-28 12.47 12.480 12.230 12.32
2017-04-27 2017-04-27 12.39 12.515 12.380 12.50
2017-04-26 2017-04-26 12.03 12.510 12.030 12.37
2017-04-24 2017-04-24 12.00 12.055 11.920 12.03
2017-04-21 2017-04-21 11.88 11.990 11.840 11.88
2017-04-20 2017-04-20 11.78 11.840 11.710 11.80
2017-04-19 2017-04-19 11.70 11.745 11.610 11.74
2017-04-18 2017-04-18 11.95 11.950 11.605 11.74
2017-04-13 2017-04-13 11.95 12.010 11.920 11.95
2017-04-12 2017-04-12 12.05 12.050 11.945 12.01
2017-04-11 2017-04-11 11.95 12.140 11.945 12.08
2017-04-10 2017-04-10 11.79 11.930 11.780 11.91
2017-04-07 2017-04-07 11.83 11.830 11.690 11.78
2017-04-06 2017-04-06 11.85 11.870 11.740 11.78
2017-04-05 2017-04-05 12.06 12.140 11.800 11.96
2017-04-04 2017-04-04 12.03 12.160 11.930 12.07
2017-04-03 2017-04-03 12.14 12.180 11.860 11.98
2017-03-30 2017-03-30 12.01 12.230 11.970 12.14
2017-03-29 2017-03-29 11.99 12.050 11.900 12.03
1 個解決方案
解決方案1
2 已采納 2017-07-02 11:42:32
數據需要排序。 然后，選項interpolate=True將提供所需的繪圖。
u = u""" Date Date1 Open High Low Close
2017-06-27 2017-06-27 10.75 10.850 10.665 10.70
2017-06-26 2017-06-26 10.57 10.740 10.570 10.69
2017-06-23 2017-06-23 10.59 10.710 10.500 10.63
2017-06-22 2017-06-22 10.56 10.720 10.530 10.65
2017-06-21 2017-06-21 10.75 10.750 10.500 10.53
2017-06-20 2017-06-20 10.88 10.925 10.780 10.81
2017-06-19 2017-06-19 10.77 10.920 10.760 10.82
2017-06-16 2017-06-16 10.74 10.910 10.740 10.78
2017-06-15 2017-06-15 10.86 10.930 10.690 10.76
2017-06-14 2017-06-14 10.96 11.040 10.910 11.02
2017-06-13 2017-06-13 10.57 10.930 10.570 10.93
2017-06-09 2017-06-09 10.59 10.650 10.500 10.57
2017-06-08 2017-06-08 10.28 10.590 10.280 10.57
2017-06-07 2017-06-07 10.35 10.420 10.290 10.35
2017-06-06 2017-06-06 10.66 10.700 10.280 10.38
2017-06-05 2017-06-05 10.80 10.850 10.560 10.70
2017-06-02 2017-06-02 11.32 11.390 11.190 11.19
2017-06-01 2017-06-01 11.26 11.330 11.160 11.20
2017-05-31 2017-05-31 11.22 11.400 11.190 11.27
2017-05-30 2017-05-30 11.10 11.260 11.070 11.23
2017-05-29 2017-05-29 11.36 11.370 11.045 11.08
2017-05-26 2017-05-26 11.55 11.590 11.320 11.36
2017-05-25 2017-05-25 11.62 11.670 11.455 11.66
2017-05-24 2017-05-24 11.67 11.755 11.590 11.62
2017-05-23 2017-05-23 11.77 11.835 11.590 11.64
2017-05-22 2017-05-22 12.06 12.110 11.800 11.83
2017-05-19 2017-05-19 12.15 12.235 11.990 12.03
2017-05-18 2017-05-18 12.00 12.130 11.910 12.13
2017-05-17 2017-05-17 12.28 12.300 12.120 12.14
2017-05-16 2017-05-16 12.23 12.385 12.220 12.30
2017-05-15 2017-05-15 12.10 12.255 12.030 12.22
2017-05-12 2017-05-12 12.30 12.300 12.050 12.12
2017-05-11 2017-05-11 12.30 12.445 12.270 12.34
2017-05-10 2017-05-10 11.99 12.415 11.950 12.25
2017-05-09 2017-05-09 12.07 12.100 11.730 11.79
2017-05-08 2017-05-08 12.14 12.230 12.060 12.10
2017-05-05 2017-05-05 12.24 12.250 12.020 12.04
2017-05-04 2017-05-04 12.23 12.270 12.050 12.14
2017-05-03 2017-05-03 12.43 12.460 12.200 12.23
2017-05-02 2017-05-02 12.44 12.470 12.310 12.46
2017-05-01 2017-05-01 12.25 12.440 12.230 12.44
2017-04-28 2017-04-28 12.47 12.480 12.230 12.32
2017-04-27 2017-04-27 12.39 12.515 12.380 12.50
2017-04-26 2017-04-26 12.03 12.510 12.030 12.37
2017-04-24 2017-04-24 12.00 12.055 11.920 12.03
2017-04-21 2017-04-21 11.88 11.990 11.840 11.88
2017-04-20 2017-04-20 11.78 11.840 11.710 11.80
2017-04-19 2017-04-19 11.70 11.745 11.610 11.74
2017-04-18 2017-04-18 11.95 11.950 11.605 11.74
2017-04-13 2017-04-13 11.95 12.010 11.920 11.95
2017-04-12 2017-04-12 12.05 12.050 11.945 12.01
2017-04-11 2017-04-11 11.95 12.140 11.945 12.08
2017-04-10 2017-04-10 11.79 11.930 11.780 11.91
2017-04-07 2017-04-07 11.83 11.830 11.690 11.78
2017-04-06 2017-04-06 11.85 11.870 11.740 11.78
2017-04-05 2017-04-05 12.06 12.140 11.800 11.96
2017-04-04 2017-04-04 12.03 12.160 11.930 12.07
2017-04-03 2017-04-03 12.14 12.180 11.860 11.98
2017-03-30 2017-03-30 12.01 12.230 11.970 12.14
2017-03-29 2017-03-29 11.99 12.050 11.900 12.03"""
import io
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
#%matplotlib inline
def WillR(ohlc, window):
high, low, close = ohlc['High'], ohlc['Low'], ohlc['Close']
R = (np.nanmax(high, axis=0) - close) / (np.nanmax(high, axis=0) - np.nanmin(low, axis=0)) * -100
return R
df = pd.read_csv(io.StringIO(u), delim_whitespace=True, index_col=0, parse_dates=True)
ohlc = df[::-1]
R = WillR(ohlc, 14)
xAxis = ohlc.index
fig = plt.figure(figsize=(18,9), dpi=100)
ax = plt.subplot()
plt.ylabel('Williams '+'%R', fontsize = 16)
ax.xaxis.grid(color='grey', linestyle=':')
ax.yaxis.grid(color='grey', linestyle=':')
ax.plot(R, color='k', alpha=0.8, linewidth=1.2)
ax.axhline(-20, color='r', linewidth=0.8)
ax.axhline(-50, color='k', linestyle='dashed', linewidth=1.2)
ax.axhline(-80, color='g', linewidth=0.8)
ax.fill_between(xAxis, R, -20, where=(R >= -20), facecolor='r', edgecolor='r', alpha=0.5, interpolate=True)
ax.fill_between(xAxis, R, -80, where=(R <= -80), facecolor='g', edgecolor='g', alpha=0.5, interpolate=True)
plt.show()
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