﻿ 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
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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## Python: Tips of the Day

Finding the most common elements in an iterable:

Example:

```# collections.Counter lets you find the most common
# elements in an iterable:

import collections
c = collections.Counter('helloworld')

print(c)

print (c.most_common(3))
```

Output:

```Counter({'l': 3, 'o': 2, 'h': 1, 'e': 1, 'w': 1, 'r': 1, 'd': 1})
[('l', 3), ('o', 2), ('h', 1)]
```