Pandas Practice Set-1: Calculate the mean of price for each cut of diamonds DataFrame
Pandas Practice Set-1: Exercise-28 with Solution
Write a Pandas program to calculate the mean of price for each cut of diamonds DataFrame.
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("\nCalculate the mean of price for each cut:") print(diamonds.groupby('cut').price.mean())
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 Calculate the mean of price for each cut: cut Fair 4358.757764 Good 3928.864452 Ideal 3457.541970 Premium 4584.257704 Very Good 3981.759891 Name: price, dtype: float64
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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())
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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