如何在不破坏标准化的情况下在图中堆叠数据

Question

I need to work with huge csv files looking something like this

x       y1      y2      y3      y4      y5      y6      y7      y8      y9
5.01    0.11    0.12    0.12    0.12    0.12    0.12    0.11    0.12    0.11
5.04    0.11    0.12    0.12    0.12    0.12    0.12    0.11    0.11    0.11
5.07    0.11    0.12    0.11    0.12    0.12    0.12    0.11    0.12    0.11
5.09    0.11    0.12    0.12    0.12    0.12    0.12    0.11    0.11    0.10
...     ...     ...     ...     ...     ...     ...     ...     ...     ...
47.88   1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00
47.90   0.91    0.99    0.88    0.96    0.94    0.98    0.86    0.86    0.85
47.93   0.70    0.81    0.63    0.76    0.73    0.78    0.61    0.61    0.59
47.95   0.46    0.55    0.41    0.51    0.47    0.53    0.40    0.39    0.38

I need to grab the data and plot it and to make it easier to compare.

I've tried to stack them on top of each other

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv', index_col=0)
plt.figure(); df.plot(stacked=True); plt.legend(loc='best')
plt.show()

And I get the following,

Stacked data

where the normalization of the data is messed up

I've tried to use the subplot keyword

plt.figure(); df.plot(subplots=True); plt.legend(loc='best')
plt.show()

In that case, I get this

Plot using subplot keyword

The sharing of the x-axis is perfect and now I don't have the normalization of the data messed up. However, I have these frames and I'm not able to remove them, also I wanted a single legend with all the entries like in the figure above.

Answer 1

I prepared four data frames for simple line graphs and used them as data frames. By using fig, we can get the legend and handle from each axis and add them to the list. Finally, we set the labels and handles retrieved by fig.legend() .

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

y1 = np.linspace(0,1,50).cumsum()
y2 = np.linspace(0,1,50).cumsum() + 0.01
y3 = np.linspace(0,1,50).cumsum() + 0.02
y4 = np.linspace(0,1,50).cumsum() + 0.03

df = pd.DataFrame({'y1':y1, 'y2':y2, 'y3':y3, 'y4':y4})

fig,ax = plt.subplots()
df.plot(subplots=True, legend=False, ax=ax)

lines = []
labels = []

for ax in fig.axes:
    axLine, axLabel = ax.get_legend_handles_labels()
    lines.extend(axLine)
    labels.extend(axLabel)
    
fig.legend(lines, labels, loc='center', bbox_to_anchor=(1.0,0.5))

plt.show()