Creating multiple lines in lineplot in seaborn from dataset

Question

I have a big dataset that I would like to plot as a line plot in seaborn.

My dataset is consisted of different measurments of specific plant that were taken in different data, so basically I would like to plot each row in my dataset as one line in the line plot as I have tried to demonstrate in this image:

For that goal I have tried to use seaborn with the next code:

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

####THERE WAS MORE PROCESSING OF THE DATA THAT I BELIEVE IS IRRELEVANT

####Here I have tried to determine what I want in my axis. I haven't really use the value wavelength

#X axis
wavelength=new_db.columns.values[5:] 

#y axis- values
#basically the outcome of this suppose to be a table with only values when ach row represents a hour
colum=new_db.columns.tolist()
new_db=new_db[cols[1:]]

#here i'm trying to create the plot
sns.set(style="whitegrid")
sns.lineplot(data=new_db, palette="tab10", linewidth=2.5)

#ERROR

ValueError: These style levels are missing dashes: set(['747.5', '814.81', '842.44', '906.34', '433.71', '667.2', '431.09', '512.97', '850.75', '882.67', '751.61', '911.92', '601.11', '847.98', '917.5', '828.61', '679.4', '440.29', '705.21', '729.74', '421.9', '959.5', '648.26', '956.69', '446.87', '445.55', '727.01', '605.14', '506.33', '856.29', '531.58', '889.63', '576.97', '924.49', '503.68', '897.98', '707.93', '970.73', '953.89', '839.67', '510.31', '678.04', '772.17', '473.24', '659.08', '813.43', '442.92', '781.78', '688.9', '623.98', '684.82', '634.76', '834.14', '955.29', '575.63', '589.03', '817.57', '474.56', '638.81', '935.68', '454.77', '571.62', '871.55', '587.69', '987.61'...............

(The original message was much longer with more numbers.)

I have tried also to create it with this:

sns.set(style="whitegrid")
ax = sns.lineplot(x="wavelength", y="new_db")

but got this error:

ValueError: Could not interpret input 'wavelength'

I don't know how to solve it.

This is the prosuct I would like to get in the end:

René · Accepted Answer · 2019-12-17 14:03:21Z

4

How about transposing you DataFrame?

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

df = pd.DataFrame(np.random.rand(3, 10)).T
print(df)

DataFrame:

          0         1         2
0  0.341005  0.925077  0.541746
1  0.324769  0.558320  0.902804
2  0.647871  0.630663  0.607212
3  0.722298  0.745091  0.630445
4  0.209836  0.386576  0.076790
5  0.347911  0.237178  0.446102
6  0.174991  0.777129  0.109934
7  0.022564  0.388223  0.464438
8  0.359771  0.722761  0.837942
9  0.091696  0.474859  0.840078

Plot

sns.lineplot(data=df.iloc[:, :2])

Result:

answered Dec 17, 2019 at 14:03

René

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2 Comments

Reut Over a year ago

that looks good and the transposing worked though I don't know what it really does. Could you change theoretically the names in the legend?

René Over a year ago

It basically swaps the rows and columns. Add df.columns = ['label 1', 'label 2', 'label 3'] before plotting to add names in the legend.

game0ver · Accepted Answer · 2019-12-17 17:01:37Z

2

I think the default len for dash styles list is 6. If you want more styles, you have to define them manually and add them through:

dash_styles = ["",
               (4, 1.5),
               (1, 1),
               (3, 1, 1.5, 1),
               (5, 1, 1, 1),
               (5, 1, 2, 1, 2, 1),
               (2, 2, 3, 1.5),
               (1, 2.5, 3, 1.2),
                # etc 
               ]

sns.lineplot(...,  dashes=dash_styles,...)

edited Dec 17, 2019 at 17:01

game0ver

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answered Dec 17, 2019 at 14:05

dalyds

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1 Comment

Darshan Jain Over a year ago

Can you elaborate on the logic of how to create the dash_styles? I am not able to understand it.

TBhavnani · Accepted Answer · 2020-06-09 07:21:36Z

1

Just put dashes=False and it should solve your issue and then you can build on that using the various parameters.

sns.lineplot(data=df,  dashes = False)

answered Jun 9, 2020 at 7:21

TBhavnani

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