﻿ Pandas Practice Set-1: Calculate count, minimum, maximum price for each cut of diamonds DataFrame - w3resource

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
print("Original Dataframe:")
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
Very Good  12082  336  18818
```

Python Code Editor:

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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())
```

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)```