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Pandas Styling: Exercises, Practice, Solution

[An editor is available at the bottom of the page to write and execute the scripts.]

Styling: This is a new feature and still under development. The styling is accomplished using CSS. You can write "style functions" that take scalars, DataFrames or Series, and return like-indexed DataFrames or Series with CSS "attribute: value" pairs for the values.

Pandas Styling [15 exercises with solution]

1. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight the negative numbers red and positive numbers black. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-1 output
Click me to see the sample solution

2. Create a dataframe of ten rows, four columns with random values. Convert some values to nan values. Write a Pandas program which will highlight the nan values. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-2 output
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3. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight the maximum value in each column. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-3 output
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4. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight the minimum value in each column. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-4 output
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5. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight the maximum value in last two columns. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-5 output
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6. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to set dataframe background Color black and font color yellow. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-6 output
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7. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight dataframe's specific columns. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-7 output
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8. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight dataframe's specific columns with different colors. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-8 output
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9. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to display the dataframe in table style. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-9 output
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10. 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. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-10 output
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11. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to display the dataframe in Heatmap style. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-11 output
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12. 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. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-12 output
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13. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to make a gradient color on all the values of the said dataframe. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-13 output
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14. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to display the dataframe in table style and border around the table and not around the rows. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-14 output
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15. Create a dataframe of ten rows, four columns with random values. Write a Pandas program to display bar charts in dataframe on specified columns. Go to the editor

Expected Output:

Python Pandas: pandas style exercise-15 output
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Python Code Editor:


More to Come !

Do not submit any solution of the above exercises at here, if you want to contribute go to the appropriate exercise page.

[ Want to contribute to Python Pandas exercises? Send your code (attached with a .zip file) to us at w3resource[at]yahoo[dot]com. Please avoid copyrighted materials.]

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Python: Tips of the Day

Time it:

timeit library is great for timing Python execution times. Just pass a function in string format to timeit.timeit() method and it will carry out 1 million executions to report the minimum time the code takes.

Its very useful to compare small code pieces and different functions but can be sluggish with big code.

Check out the example below demonstrating the execution time difference between 2 very similar list comprehension methods in Python:

import timeit
lst1='''list(range(100))'''
lst2='''[i for i in range(100)]'''
a=timeit.timeit(lst1)
b=timeit.timeit(lst2)
print(a, b, sep="------")

Output:

1.9301698543131351------5.389458132907748