[英]Python Matplotlib wont show rolling averages over existing subplot
I've created subplots which contain 2 bars sitting side-by-side. 我创建了包含2个并排放置的条形图的子图。 I now want to add 7 day rolling averages as lines over the top of the bars. 我现在想将7天滚动平均值添加为柱线上方的线条。
I can't get the plot to work, whichever I define last seems to occupy the figure. 我无法让情节起作用,无论我最后定义的哪个似乎都占据了这个数字。 I'd like all 4 of these plots to be on the same figure. 我希望所有这四个图都在同一图上。 How can I achieve this? 我该如何实现?
date_index = pd.date_range(df.Created.min(), df.Created.max(), freq='D')
fig = plt.figure()
ax2 = fig.add_subplot(111)
ax22 = ax2.twinx()
s1 = CDdf.resample('D', on='Created').size().fillna(0).reindex(date_index, fill_value=0)
s2 = CDdf.groupby('Created')['Machine Count'].first().fillna(0).reindex(date_index, fill_value=0)
s = (s1/s2).fillna(0).reindex(date_index, fill_value=0)
s1.plot(kind='bar', ax=ax2, position=0, label='Sales Total', width=0.25)
s.plot(kind='bar', ax=ax22, color='red', position=1, label='Adjusted For Machine Count', width=0.25)
s1rolling = s1.rolling(window=7,center=False).mean().fillna(0).reindex(date_index, fill_value=0)
plt.plot(s1rolling, color='blue', label='_nolegend_')
srolling = s.rolling(window=7,center=False).mean().fillna(0).reindex(date_index, fill_value=0)
plt.plot(srolling, color='red', label='_nolegend_')
ax2.set_ylim(0,s1.max()*1.1)
ax22.set_ylim(0,s1.max()*1.1)
plt.legend(loc='upper left')
plt.ylabel('Frequency')
plt.title('Items Deposited Per Day')
ticklabels = s.index.strftime('%Y-%m-%d')
ax2.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.show()
Data sample: 数据样本:
Created Quoted Price Machine Count
6 2017-10-06 0.99 3
454 2017-10-21 0.43 11
534 2017-10-21 0.98 11
487 2017-10-21 0.05 11
530 2017-10-21 0.05 11
482 2017-10-21 0.06 11
503 2017-10-21 0.05 11
416 2017-10-21 0.24 11
532 2017-10-21 0.07 11
469 2017-10-21 0.52 11
459 2017-10-21 0.05 11
515 2017-10-21 1.82 11
411 2017-10-21 0.32 11
539 2017-10-21 0.05 11
508 2017-10-21 0.23 11
1057 2017-10-28 0.07 11
1037 2017-10-28 0.06 11
1042 2017-10-28 0.17 11
1048 2017-10-28 0.34 11
1028 2017-10-28 0.09 11
1053 2017-10-28 0.50 11
1055 2017-10-28 1.33 11
1149 2017-10-29 0.25 11
1142 2017-10-29 0.12 11
1160 2017-10-29 0.05 11
Usually the datetimes utilities of pandas and matplotlib are incompatible. 通常,pandas和matplotlib的datetimes实用程序不兼容。 If you use a matplotlib.dates
object on a date axis created with pandas then this will in most cases fail. 如果在使用熊猫创建的日期轴上使用matplotlib.dates
对象,则在大多数情况下,此操作将失败。
Here is a solution where pandas is used for plotting and matplotlib for formatting (see comments): 这是一个使用pandas进行绘图而使用matplotlib进行格式化的解决方案(请参见注释):
import matplotlib.pyplot as plt # version 2.1.0
import matplotlib.ticker as ticker
import pandas as pd # version 0.21.0
df = pd.read_csv('data.csv', delim_whitespace=True, index_col=0, parse_dates=['Created'])
date_index = pd.date_range(df.Created.min(), df.Created.max(), freq='D')
_, ax = plt.subplots()
s1 = df.resample('D', on='Created').size().fillna(0).reindex(date_index, fill_value=0)
s2 = df.groupby('Created')['MachineCount'].first().fillna(0).reindex(date_index, fill_value=0)
s = (s1 / s2).fillna(0).reindex(date_index, fill_value=0)
s1rolling = s1.rolling(window=7, center=False).mean().fillna(0).reindex(date_index, fill_value=0)
s2rolling = s2.rolling(window=7, center=False).mean().fillna(0).reindex(date_index, fill_value=0)
srolling = s.rolling(window=7, center=False).mean().fillna(0).reindex(date_index, fill_value=0)
# Plot with pandas without date axis (i.e. use_index=False).
s1.plot(kind='bar', color='C0', position=0, label='Sales Total', width=0.25, use_index=False)
s.plot(kind='bar', color='C1', position=1, label='Adjusted For Machine Count', width=0.25, use_index=False)
# Plot with pandas without date axis (i.e. use_index=False).
s1rolling.plot(kind='line', color='C0', label='_nolegend_', use_index=False)
srolling.plot(kind='line', color='C1', label='_nolegend_', use_index=False)
plt.ylim(0, s1.max() * 1.1)
plt.legend(loc='upper left')
plt.ylabel('Frequency')
plt.title('Items Deposited Per Day')
# Format date axis with matplotlib.
ticklabels = s1.index.strftime('%Y-%m-%d')
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
I hope this will help you. 我希望这能帮到您。
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