Make a multiline stacked plot from .CSV file in matplotlib

Question

I am trying to create a multiline stacked plot from .csv file in matplotlib.

My .csv looks like this:

date    person 1    person 2    person 3
3/1/18  45.0        34.0        91.0
3/2/18  88.0        87.0        84.0
3/3/18  56.0        98.0        65.0
3/4/18  34.0        34.0        53.0
3/5/18  56.0        60.0        56.0

This should be simple but I can't get the slicing correct. Here is what I have:

import pandas as pd 
import matplotlib.pyplot as plt

df = pd.read_csv("test.csv")

df.head()

df.plot(xlim=(0, 10)

plt.title('Test')
plt.legend()
plt.show()

I don't want them to be stacked on the same chart. I'd like them to be stacked together like in this link: http://www.k-wave.org/documentation/stackedPlot.php

Would anyone be able to help?

I read through this: Make a multiline plot from .CSV file in matplotlib but not a lot of it is making sense to me because of how this person wanted to plot it. Mine seems more simple and straightforward.

ImportanceOfBeingErnest · Accepted Answer · 2019-03-09 00:40:58Z

0

In principle the desired plot can be achieved as follows.

Normalize the dataframe to the range between 0 and 1,
Shift each column by one more unit
plot the dataframe

Complete example:

u ="""date    person 1    person 2    person 3
3/1/18  45.0        34.0        91.0
3/2/18  88.0        87.0        84.0
3/3/18  56.0        98.0        65.0
3/4/18  34.0        34.0        53.0
3/5/18  56.0        60.0        56.0"""

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

df = pd.read_csv(io.StringIO(u), sep="\s\s+", engine="python")
df["date"] = pd.to_datetime(df["date"], format="%m/%d/%y")
df = df.set_index("date")

# normalize
df /= df.values.max()
# shift colums
df += np.arange(len(df.columns))

ax = df.plot()
ax.set_yticks(np.arange(len(df.columns)))
ax.set_yticklabels(df.columns)
plt.show()

Note how the plot is pretty hard to read. So as an alternative one could plot 3 individual plots, such that the data scales are preserved, using subplots=True:

u ="""date    person 1    person 2    person 3
3/1/18  45.0        34.0        91.0
3/2/18  88.0        87.0        84.0
3/3/18  56.0        98.0        65.0
3/4/18  34.0        34.0        53.0
3/5/18  56.0        60.0        56.0"""

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

df = pd.read_csv(io.StringIO(u), sep="\s\s+", engine="python")
df["date"] = pd.to_datetime(df["date"], format="%m/%d/%y")
df = df.set_index("date")

ax = df.plot(subplots=True)

plt.show()

answered Mar 9, 2019 at 0:40

ImportanceOfBeingErnest

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1 Comment

Maria Wolfie Over a year ago

Hi thank you so much for the help! I ran into few issues but figured out I needed to format that data like you did. Which worked! I also figured out to use ax = df.plot(figsize(10,20), subplots=True) to change the figure size. And this ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) to adjust the legend size! Thank you for all your help! I need to understand normalizing the dataframe more . I don’t understand what you mean by range between 0 and 1, so I plan to google and see what comes up!

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