pandas 如何在python中的同一个图形上绘制多个密度图
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How to plot multiple density plots on the same figure in python
提问by JAG2024
I know this is going to end up being a really messy plot, but I am curious to know what the most efficient way to do this is. I have some data that looks like this in a csv file:
我知道这最终会成为一个非常混乱的情节，但我很想知道最有效的方法是什么。我在 csv 文件中有一些看起来像这样的数据：
ROI Band Min Max Mean Stdev
1 red_2 Band 1 0.032262 0.124425 0.078073 0.028031
2 red_2 Band 2 0.021072 0.064156 0.037923 0.012178
3 red_2 Band 3 0.013404 0.066043 0.036316 0.014787
4 red_2 Band 4 0.005162 0.055781 0.015526 0.013255
5 red_3 Band 1 0.037488 0.10783 0.057892 0.018964
6 red_3 Band 2 0.02814 0.07237 0.04534 0.014507
7 red_3 Band 3 0.01496 0.112973 0.032751 0.026575
8 red_3 Band 4 0.006566 0.029133 0.018201 0.006897
9 red_4 Band 1 0.022841 0.148666 0.065844 0.0336
10 red_4 Band 2 0.018651 0.175298 0.046383 0.042339
11 red_4 Band 3 0.012256 0.045111 0.024035 0.009711
12 red_4 Band 4 0.001493 0.033822 0.014678 0.007788
13 red_5 Band 1 0.030513 0.18098 0.090056 0.044456
37 bcs_1 Band 1 0.013059 0.076753 0.037674 0.023172
38 bcs_1 Band 2 0.035227 0.08826 0.057672 0.015005
39 bcs_1 Band 3 0.005223 0.028459 0.010836 0.006003
40 bcs_1 Band 4 0.009804 0.031457 0.018094 0.007136
41 bcs_2 Band 1 0.018134 0.083854 0.040654 0.018333
42 bcs_2 Band 2 0.016123 0.088613 0.045742 0.020168
43 bcs_2 Band 3 0.008065 0.030557 0.014596 0.007435
44 bcs_2 Band 4 0.004789 0.016514 0.009815 0.003241
45 bcs_3 Band 1 0.021092 0.077993 0.037246 0.013696
46 bcs_3 Band 2 0.011918 0.068825 0.028775 0.013758
47 bcs_3 Band 3 0.003969 0.021714 0.011336 0.004964
48 bcs_3 Band 4 0.003053 0.015763 0.006283 0.002425
49 bcs_4 Band 1 0.024466 0.079989 0.049291 0.018032
50 bcs_4 Band 2 0.009274 0.093137 0.041979 0.019347
51 bcs_4 Band 3 0.006874 0.027214 0.014386 0.005386
52 bcs_4 Band 4 0.005679 0.026662 0.014529 0.006505
And I want to create one probability density plot with 8 lines: 4 of which the 4 bands for "red" and the other will be the 4 bands for "black".So far I have this for just Band 1 in both red and black ROIs. But my code outputs two different plots. I have tried using subplot but that has not worked for me.
我想用 8 条线创建一个概率密度图：其中 4 个“红色”波段，另一个将是“黑色”的 4 个波段。到目前为止，我只有红色和黑色的波段 1投资回报率。但是我的代码输出了两个不同的图。我试过使用 subplot 但这对我没有用。
Help? I know my approach is verbose and clunky, so smarter solutions much appreciated!
帮助？我知道我的方法冗长而笨拙，因此非常感谢更智能的解决方案！
Load packages
加载包
import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
files = ['example.csv']
Organize the data
整理资料
for f in files:
fn = f.split('.')[0]
dat = pd.read_csv(f)
df0 = dat.loc[:, ['ROI', 'Band', 'Mean']]
# parse by soil type
red = df0[df0['ROI'].str.contains("red")]
black = df0[df0['ROI'].str.contains("bcs")]
# parse by band
red.b1 = red[red['Band'].str.contains("Band 1")]
red.b2 = red[red['Band'].str.contains("Band 2")]
red.b3 = red[red['Band'].str.contains("Band 3")]
red.b4 = red[red['Band'].str.contains("Band 4")]
black.b1 = black[black['Band'].str.contains("Band 1")]
black.b2 = black[black['Band'].str.contains("Band 2")]
black.b3 = black[black['Band'].str.contains("Band 3")]
black.b4 = black[black['Band'].str.contains("Band 4")]
Plot the figure
绘制图形
pd.DataFrame(black.b1).plot(kind="density")
pd.DataFrame(red.b1).plot(kind="density")
plt.show()
I'd like for the figure to have 8 lines on it.
我希望图中有 8 行。
回答by piRSquared
groupby+ str.split
groupby+ str.split
df.groupby([df.ROI.str.split('_').str[0], 'Band']).Mean.plot.kde();
If you want a legend
如果你想要传奇
df.groupby([df.ROI.str.split('_').str[0], 'Band']).Mean.plot.kde()
plt.legend();
回答by Robbie
Something to help lead you in the right direction:
可以帮助您朝着正确方向前进的东西：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame()
for i in range(8):
mean = 5-10*np.random.rand()
std = 6*np.random.rand()
df['score_{0}'.format(i)] = np.random.normal(mean, std, 60)
fig, ax = plt.subplots(1,1)
for s in df.columns:
df[s].plot(kind='density')
fig.show()
Basically just looping through the columns, and plotting as you go. Having more control over the figure is very helpful.
基本上只是循环遍历列，然后随心所欲地绘图。对数字有更多的控制是非常有帮助的。
