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Pandas Practice Set-1: Count the number of unique values in cut series of diamonds DataFrame

Pandas Practice Set-1: Exercise-34 with Solution

Write a Pandas program to count the number of unique values in cut series of diamonds DataFrame.

Sample Solution:

Python Code:

import pandas as pd
diamonds = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv')
print("Original Dataframe:")
print(diamonds.head())
print("\nNumber of unique values in cut series of diamonds DataFrame:")
print(diamonds.cut.nunique())

Sample Output:

Original Dataframe:
   carat      cut color clarity  depth  table  price     x     y     z
0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43
1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31
2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31
3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63
4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75

Number of unique values in cut series of diamonds DataFrame:
5

Python Code Editor:


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Next: Write a Pandas program to compute a cross-tabulation of two Series in diamonds DataFrame.

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Python: Tips of the Day

Merges two or more lists into a list of lists, combining elements from each of the input lists based on their positions

Example:

def tips_merge(*args, fill_value=None):
  max_length = max([len(lst) for lst in args])
  result = []
  for i in range(max_length):
    result.append([
      args[k][i] if i < len(args[k])
	  else fill_value for k in range(len(args))
    ])
  return result
print(tips_merge(['x', 'y'], [1, 2], [True, False])) 
print(tips_merge(['x'], [1, 2], [True, False])) 
print(tips_merge(['x'], [1, 2], [True, False], fill_value = '_')) 

Output:

[['x', 1, True], ['y', 2, False]]
[['x', 1, True], [None, 2, False]]
[['x', 1, True], ['_', 2, False]]