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create a heatmap of two categorical variables

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0

I have the following datasets of three variables:

1. df['Score'] Float dummy (1 or 0)


2. df['Province'] an object column where each row is a region


3. df['Product type'] an object indicating the industry.

I would like to create a jointplot where on the x axis I have the different industries, on the y axis the different provinces and as colours of my jointplot I have the relative frequency of the score.
Something like this.
https://seaborn.pydata.org/examples/hexbin_marginals.html

For the time being, I could only do the following

mean = df.groupby(['Province', 'Product type'])['score'].mean()

But i am not sure how to plot it.

Thanks!

python pandas plot

share|improve this question

edited Mar 23 at 10:00

N8888

4561513

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Possible duplicate of Making heatmap from pandas DataFrame
  
  – perl
  Mar 23 at 10:17
  

  
  
  
  

add a comment | 

0

I have the following datasets of three variables:

1. df['Score'] Float dummy (1 or 0)


2. df['Province'] an object column where each row is a region


3. df['Product type'] an object indicating the industry.

I would like to create a jointplot where on the x axis I have the different industries, on the y axis the different provinces and as colours of my jointplot I have the relative frequency of the score.
Something like this.
https://seaborn.pydata.org/examples/hexbin_marginals.html

For the time being, I could only do the following

mean = df.groupby(['Province', 'Product type'])['score'].mean()

But i am not sure how to plot it.

Thanks!

python pandas plot

share|improve this question

edited Mar 23 at 10:00

N8888

4561513

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  Possible duplicate of Making heatmap from pandas DataFrame
  
  – perl
  Mar 23 at 10:17
  

  
  
  
  

add a comment | 

I have the following datasets of three variables:

1. df['Score'] Float dummy (1 or 0)


2. df['Province'] an object column where each row is a region


3. df['Product type'] an object indicating the industry.

I would like to create a jointplot where on the x axis I have the different industries, on the y axis the different provinces and as colours of my jointplot I have the relative frequency of the score.
Something like this.
https://seaborn.pydata.org/examples/hexbin_marginals.html

For the time being, I could only do the following

mean = df.groupby(['Province', 'Product type'])['score'].mean()

But i am not sure how to plot it.

Thanks!

python pandas plot

share|improve this question

edited Mar 23 at 10:00

N8888

4561513

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

mean = df.groupby(['Province', 'Product type'])['score'].mean()

share|improve this question

edited Mar 23 at 10:00

N8888

4561513

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

share|improve this question

edited Mar 23 at 10:00

N8888

4561513

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

edited Mar 23 at 10:00

N8888

4561513

edited Mar 23 at 10:00

N8888

4561513

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

asked Mar 23 at 9:57

Filippo SebastioFilippo Sebastio

19819

1 Answer
1

active

oldest

votes

0

If you are looking for a heatmap, you could use seaborn heatmap function. However you need to pivot your table first.

Just creating a small example:

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

score = [1, 1, 1, 0, 1, 0, 0, 0]
provinces = ['Place1' ,'Place2' ,'Place2', 'Place3','Place1', 'Place2','Place3','Place1']
products = ['Product1' ,'Product3' ,'Product2', 'Product2','Product1', 'Product2','Product1','Product1']
df = pd.DataFrame('Province': provinces,
 'Product type': products,
 'score': score
 )

My df looks like:

 'Province''Product type''score'
0 Place1 Product1 1
1 Place2 Product3 1
2 Place2 Product2 1
3 Place3 Product2 0
4 Place1 Product1 1
5 Place2 Product2 0
6 Place3 Product1 0
7 Place1 Product1 0

Then:

df_heatmap = df.pivot_table(values='score',index='Province',columns='Product type',aggfunc=np.mean)
sns.heatmap(df_heatmap,annot=True)
plt.show()

The result is:

share|improve this answer

answered Mar 23 at 15:13

vmouffronvmouffron

9016

add a comment | 

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0

If you are looking for a heatmap, you could use seaborn heatmap function. However you need to pivot your table first.

Just creating a small example:

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

score = [1, 1, 1, 0, 1, 0, 0, 0]
provinces = ['Place1' ,'Place2' ,'Place2', 'Place3','Place1', 'Place2','Place3','Place1']
products = ['Product1' ,'Product3' ,'Product2', 'Product2','Product1', 'Product2','Product1','Product1']
df = pd.DataFrame('Province': provinces,
 'Product type': products,
 'score': score
 )

My df looks like:

 'Province''Product type''score'
0 Place1 Product1 1
1 Place2 Product3 1
2 Place2 Product2 1
3 Place3 Product2 0
4 Place1 Product1 1
5 Place2 Product2 0
6 Place3 Product1 0
7 Place1 Product1 0

Then:

df_heatmap = df.pivot_table(values='score',index='Province',columns='Product type',aggfunc=np.mean)
sns.heatmap(df_heatmap,annot=True)
plt.show()

The result is:

share|improve this answer

answered Mar 23 at 15:13

vmouffronvmouffron

9016

add a comment | 

0

If you are looking for a heatmap, you could use seaborn heatmap function. However you need to pivot your table first.

Just creating a small example:

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

score = [1, 1, 1, 0, 1, 0, 0, 0]
provinces = ['Place1' ,'Place2' ,'Place2', 'Place3','Place1', 'Place2','Place3','Place1']
products = ['Product1' ,'Product3' ,'Product2', 'Product2','Product1', 'Product2','Product1','Product1']
df = pd.DataFrame('Province': provinces,
 'Product type': products,
 'score': score
 )

My df looks like:

 'Province''Product type''score'
0 Place1 Product1 1
1 Place2 Product3 1
2 Place2 Product2 1
3 Place3 Product2 0
4 Place1 Product1 1
5 Place2 Product2 0
6 Place3 Product1 0
7 Place1 Product1 0

Then:

df_heatmap = df.pivot_table(values='score',index='Province',columns='Product type',aggfunc=np.mean)
sns.heatmap(df_heatmap,annot=True)
plt.show()

The result is:

share|improve this answer

answered Mar 23 at 15:13

vmouffronvmouffron

9016

add a comment | 

If you are looking for a heatmap, you could use seaborn heatmap function. However you need to pivot your table first.

Just creating a small example:

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

score = [1, 1, 1, 0, 1, 0, 0, 0]
provinces = ['Place1' ,'Place2' ,'Place2', 'Place3','Place1', 'Place2','Place3','Place1']
products = ['Product1' ,'Product3' ,'Product2', 'Product2','Product1', 'Product2','Product1','Product1']
df = pd.DataFrame('Province': provinces,
 'Product type': products,
 'score': score
 )

My df looks like:

 'Province''Product type''score'
0 Place1 Product1 1
1 Place2 Product3 1
2 Place2 Product2 1
3 Place3 Product2 0
4 Place1 Product1 1
5 Place2 Product2 0
6 Place3 Product1 0
7 Place1 Product1 0

Then:

df_heatmap = df.pivot_table(values='score',index='Province',columns='Product type',aggfunc=np.mean)
sns.heatmap(df_heatmap,annot=True)
plt.show()

The result is:

share|improve this answer

answered Mar 23 at 15:13

vmouffronvmouffron

9016

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

score = [1, 1, 1, 0, 1, 0, 0, 0]
provinces = ['Place1' ,'Place2' ,'Place2', 'Place3','Place1', 'Place2','Place3','Place1']
products = ['Product1' ,'Product3' ,'Product2', 'Product2','Product1', 'Product2','Product1','Product1']
df = pd.DataFrame('Province': provinces,
 'Product type': products,
 'score': score
 )

 'Province''Product type''score'
0 Place1 Product1 1
1 Place2 Product3 1
2 Place2 Product2 1
3 Place3 Product2 0
4 Place1 Product1 1
5 Place2 Product2 0
6 Place3 Product1 0
7 Place1 Product1 0

df_heatmap = df.pivot_table(values='score',index='Province',columns='Product type',aggfunc=np.mean)
sns.heatmap(df_heatmap,annot=True)
plt.show()

share|improve this answer

answered Mar 23 at 15:13

vmouffronvmouffron

9016

answered Mar 23 at 15:13

vmouffronvmouffron

9016

answered Mar 23 at 15:13

vmouffronvmouffron

9016