CODEWITHSUNDEEP
pythonpandasmatplotlibseaborndata-visualization
bbartling
bbartling

Reputation: 3502

seaborn implot combine datasets into one plot

This maybe a silly question, but I have multiple plots redundant looking code all based on one years of time series data from pandas dataframes. Is it possible to combine these into one plot but with each year of data (2018,2019,2020) be a different color? Any tips appreciated not a lot of wisdom here.

g = sns.lmplot(x="CDD",y="kWh",data=df_2018_avg, fit_reg=True) 
g.fig.set_size_inches(15,8)

g.fig.suptitle(f'2018 kWh / Day Vs CDD model: R-squared {round(stats_model_2018.rsquared,2)}')

enter image description here

g = sns.lmplot(x="CDD",y="kWh",data=df_2019_avg, fit_reg=True) 
g.fig.set_size_inches(15,8)

g.fig.suptitle(f'2019 kWh / Day Vs CDD model: R-squared {round(stats_model_2019.rsquared,2)}')

enter image description here

g = sns.lmplot(x="CDD",y="kWh",data=df_2020_avg, fit_reg=True) 
g.fig.set_size_inches(15,8)

g.fig.suptitle(f'2020 kWh / Day Vs CDD model: R-squared {round(stats_model_2020.rsquared,2)}')

enter image description here

Upvotes: 0

Views: 1007

Answers (3)

Corralien
Corralien

Reputation: 120409

If you accept to use sns.regplot (axes-level function) instead of sns.lmplot (figure-level function), it's possible to combine all graphs in one:

Demo:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')
fig, ax = plt.subplots(figsize=(15, 8))

for day, df in tips.groupby('day'):
    sns.regplot(x="total_bill", y="tip", data=df, ax=ax, label=day)

ax.legend()
plt.show()

Enhanced by @JohanC

Regplot

Upvotes: 0

JohanC
JohanC

Reputation: 80299

You can add an extra column to contain the year and concatenate the dataframes to form one big dataframe. Then you can use hue='year':

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

df_2018_avg = pd.DataFrame({'CDD': np.linspace(0, 21, 100),
                            'kWh': np.linspace(9000, 10500, 100) + np.random.normal(0, 500, 100)})
df_2019_avg = pd.DataFrame({'CDD': np.linspace(0, 21, 100),
                            'kWh': np.linspace(9000, 11000, 100) + np.random.normal(0, 500, 100)})
df_2020_avg = pd.DataFrame({'CDD': np.linspace(0, 21, 100),
                            'kWh': np.linspace(8000, 10000, 100) + np.random.normal(0, 500, 100)})
df_2018_avg['year'] = 2018
df_2019_avg['year'] = 2019
df_2020_avg['year'] = 2020

g = sns.lmplot(x="CDD", y="kWh", hue="year",
               data=pd.concat([df_2018_avg, df_2019_avg, df_2020_avg]), fit_reg=True)
g.fig.set_size_inches(15, 8)
plt.show()

sns.lmplot combining dataframes

Upvotes: 1

Jonathan Gray
Jonathan Gray

Reputation: 131

Append a 'year' column to each dataframe that matches the year of the data.

df_2018_avg['year'] = 2018
df_2019_avg['year'] = 2019
df_2020_avg['year'] = 2020

Then combine each table into one dataframe

df = pd.concat([df_2018_avg, df_2019_avg, df_2020_avg])

Then just add the 'hue' argument in your seaborn function.

g = sns.lmplot(x="CDD",y="kWh",data=df, fit_reg=True, hue='year')

Upvotes: 0

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