CODEWITHSUNDEEP
pythonpandasmatplotlibseabornfacet-grid
Krish1992
Krish1992

Reputation: 81

Seaborn Line Plot for plotting multiple parameters

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:

eps:

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,

enter image description here

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

Upvotes: 1

Views: 1125

Answers (1)

tdy
tdy

Reputation: 41327

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

  1. 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
# ...

  2. 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

  3. 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},
)

relplot 3x1

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))

Upvotes: 2

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