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Pandas styling Exercises: Write a Pandas program to display the dataframe in Heatmap style

Pandas styling: Exercise-11 with Solution

Create a dataframe of ten rows, four columns with random values. Write a Pandas program to display the dataframe in Heatmap style.

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
import seaborn as sns

np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
               axis=1)
print("Original array:")
print(df)
print("\nDataframe - Heatmap style:")

cm = sns.light_palette("red", as_cmap=True)
 
df.style.background_gradient(cmap='viridis') 

Original array:

Original array:
      A         B         C         D         E
0   1.0  1.329212 -0.770033 -0.316280 -0.990810
1   2.0 -1.070816 -1.438713  0.564417  0.295722
2   3.0 -1.626404  0.219565  0.678805  1.889273
3   4.0  0.961538  0.104011 -0.481165  0.850229
4   5.0  1.453425  1.057737  0.165562  0.515018
5   6.0 -1.336936  0.562861  1.392855 -0.063328
6   7.0  0.121668  1.207603 -0.002040  1.627796
7   8.0  0.354493  1.037528 -0.385684  0.519818
8   9.0  1.686583 -1.325963  1.428984 -2.089354
9  10.0 -0.129820  0.631523 -0.586538  0.290720

Dataframe - Heatmap style:

Sample Output:

Python Pandas: pandas styling exercise-11 output

Download the Jupyter Notebook from here.

Python Code Editor:


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Previous: Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight the entire row in Yellow where a specific column value is greater than 0.5.

Next: Create a dataframe of ten rows, four columns with random values. Write a Pandas program to make a gradient color mapping on a specified column.

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Python: Tips of the Day

Python: Annotated Assignment Statement

This might not seem as impressive as some other tricks but it's a new syntax that was introduced to Python in recent years and just good to be aware of.

Annotated assignments allow the coder to leave type hints in the code. These don't have any enforcing power at least not yet. It's still nice to be able to imply some type hints and definitely offers more options than only being able to comment regarding expected types of variables.

day: str = 'Monday'
print(day)
lst: list = [1,2,3,4]
print(lst)

Output:

Monday
[1, 2, 3, 4]

Or the same thing in a shorter way:

day= 'Monday' #str
print(day)
lst= [1,2,3,4] # list
print(lst)

Output:

Monday
[1, 2, 3, 4]