Pandas Practice Set-1: Calculate count, minimum, maximum price for each cut of diamonds DataFrame
Pandas Practice Set-1: Exercise-29 with Solution
Write a Pandas program to calculate count, minimum, maximum price for each cut 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("\nCount, minimum, maximum price for each cut of diamonds DataFrame:")
print(diamonds.groupby('cut').price.agg(['count', 'min', 'max']))
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 Count, minimum, maximum price for each cut of diamonds DataFrame: count min max cut Fair 1610 337 18574 Good 4906 327 18788 Ideal 21551 326 18806 Premium 13791 326 18823 Very Good 12082 336 18818
Python Code Editor:
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Previous: Write a Pandas program to calculate the mean of price for each cut of diamonds DataFrame.
Next: Write a Pandas program to create a side-by-side bar plot of the diamonds DataFrame.
What is the difficulty level of this exercise?
Python: Tips of the Day
Python: Cache results with decorators
There is a great way to cache functions with decorators in Python. Caching will help save time and precious resources when there is an expensive function at hand.
Implementation is easy, just import lru_cache from functools library and decorate your function using @lru_cache.
from functools import lru_cache @lru_cache(maxsize=None) def fibo(a): if a <= 1: return a else: return fibo(a-1) + fibo(a-2) for i in range(20): print(fibo(i), end="|") print("\n\n", fibo.cache_info())
Output:
0|1|1|2|3|5|8|13|21|34|55|89|144|233|377|610|987|1597|2584|4181| CacheInfo(hits=36, misses=20, maxsize=None, currsize=20)
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