Pandas styling Exercises: Write a Pandas program which will highlight the nan values
Pandas styling: Exercise-2 with Solution
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.
Sample Solution :
Python Code :
import pandas as pd
import numpy as np
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)
df.iloc[0, 2] = np.nan
df.iloc[3, 3] = np.nan
df.iloc[4, 1] = np.nan
df.iloc[9, 4] = np.nan
print("Original array:")
print(df)
def color_negative_red(val):
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
print("\nNegative numbers red and positive numbers black:")
df.style.highlight_null(null_color='red')
Original array:
Original array: A B C D E 0 1.0 1.329212 NaN -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 NaN 0.850229 4 5.0 NaN 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 NaN Negative numbers red and positive numbers black:
Sample 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 negative numbers red and positive numbers black.
Next: Create a dataframe of ten rows, four columns with random values. Write a Pandas program to highlight the maximum value in each column.
What is the difficulty level of this exercise?
Python: Tips of the Day
Find current directory and file's directory:
To get the full path to the directory a Python file is contained in, write this in that file:
import os dir_path = os.path.dirname(os.path.realpath(__file__))
(Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.)
To get the current working directory use
import os cwd = os.getcwd()
Documentation references for the modules, constants and functions used above:
- The os and os.path modules.
- The __file__ constant
- os.path.realpath(path) (returns "the canonical path of the specified filename, eliminating any symbolic links encountered in the path")
- os.path.dirname(path) (returns "the directory name of pathname path")
- os.getcwd() (returns "a string representing the current working directory")
- os.chdir(path) ("change the current working directory to path")
Ref: https://bit.ly/3fy0R6m
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