Seaborn Line Plot for plotting multiple parameters

Question

I have dataset as below,

index	10_YR_CAGR	5_YR_CAGR	1_YR_CAGR
c1_rev	20.5	21.5	31.5
c2_rev	20.5	22.5	24
c3_rev	21	24	27
c4_rev	20	26	30
c5_rev	24	19	15
c1_eps	21	22	23
c2_eps	21	24	25

This data has 5 companies and its parameters like rev, eps, profit etc. I need to plot as below:

rev:

x_axis-> index_col c1_rev, ...c5_rev
y_axis -> 10_YR_CAGR .. 1_YR_CAGR

eps:

x_axis -> index_col: c1_eps,...c5_eps
y_axis -> 10_YR_CAGR,... 1_YR_CAGR

etc...

I have tried with following code:

eps = analysis_df[analysis_df.index.str.contains('eps',regex=True)]

for i1 in eps.columns[eps.columns!='index']:
    sns.lineplot(x="index",y=i1,data=eps,label=i1)

I have to make a dataframe from source and then loop it. How can I try to create a for loop which loops from the main source dataframe itself?

Instead of creating a loop for separate parameters, how can I loop from the main source dataframe to create a chart of plots with parameters like rev, eps, profit to facegrid parameters? How to apply those filter in facetgrid?

My sample output of the above code,

How to plot the same sort of plot for different parameters in a single for loop?

tdy · Accepted Answer · 2021-03-19 14:28:50Z

2

The way facets are typically plotted is by "melting" your analysis_df into id/variable/value columns.

split() the index column into Company and Parameter, which we'll later use as id columns when melting:

analysis_df[['Company', 'Parameter']] = analysis_df['index'].str.split('_', expand=True)

#      index  10_YR_CAGR  5_YR_CAGR  1_YR_CAGR  Company  Parameter
#  0  c1_rev         100         21          1       c1        rev
#  1  c2_rev           1         32         24       c2        rev
# ...

melt() the CAGR columns:

melted = analysis_df.melt(
    id_vars=['Company', 'Parameter'],
    value_vars=['10_YR_CAGR', '5_YR_CAGR', '1_YR_CAGR'],
    var_name='Period',
    value_name='CAGR',
)

#      Company  Parameter      Period  CAGR
#  0        c1        rev  10_YR_CAGR   100
#  1        c2        rev  10_YR_CAGR     1
#  2        c3        rev  10_YR_CAGR    14
#  3        c1        eps  10_YR_CAGR     1
# ...
# 25        c2        pft   1_YR_CAGR    14
# 26        c3        pft   1_YR_CAGR    17

relplot() CAGR vs Company (colored by Period) for each Parameter using the melted dataframe:

sns.relplot(
    data=melted,
    kind='line',
    col='Parameter',
    x='Company',
    y='CAGR',
    hue='Period',
    col_wrap=1,
    facet_kws={'sharex': False, 'sharey': False},
)

Sample data to reproduce this plot:

import io
import pandas as pd
csv = '''
index,10_YR_CAGR,5_YR_CAGR,1_YR_CAGR
c1_rev,100,21,1
c2_rev,1,32,24
c3_rev,14,23,7
c1_eps,1,20,50
c2_eps,21,20,25
c3_eps,31,20,37
c1_pft,20,1,10
c2_pft,25,20,14
c3_pft,11,55,17
'''
analysis_df = pd.read_csv(io.StringIO(csv))

edited Mar 19, 2021 at 14:28

answered Mar 19, 2021 at 9:50

tdy

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4 Comments

Krish1992 Over a year ago

thanks for the update, how to plot the graph row by row and use ylim based on plot instead of common ylim, so that chart look good?

tdy Over a year ago

You can use col_wrap=1 to set 1 column (so 3 rows here) and facet_kws={'sharey': False} to unlink the y-axes. I edited my answer with these options.

Krish1992 Over a year ago

Thanks @tdy it worked. But x labels are missing while doing this, only last chart shows xlabels. Any idea on how to update xlabels to all plots?

tdy Over a year ago

You're welcome. For the x-labels, you can unlink the x-axes as well: facet_kws={'sharex': False, 'sharey': False} (answer updated)

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