﻿ Pandas Practice Set-1: Get the true memory usage by diamonds DataFrame - w3resource

# Pandas Practice Set-1: Get the true memory usage by diamonds DataFrame

## Pandas Practice Set-1: Exercise-60 with Solution

Write a Pandas program to get the true memory usage by diamonds DataFrame.

Sample Solution:

Python Code:

``````import pandas as pd
print("Original Dataframe:")
print("\nTrue memory usage by diamonds DataFrame:")
print(diamonds.info(memory_usage='deep'))
``````

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

True memory usage by diamonds DataFrame:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 53940 entries, 0 to 53939
Data columns (total 10 columns):
carat      53940 non-null float64
cut        53940 non-null object
color      53940 non-null object
clarity    53940 non-null object
depth      53940 non-null float64
table      53940 non-null float64
price      53940 non-null int64
x          53940 non-null float64
y          53940 non-null float64
z          53940 non-null float64
dtypes: float64(6), int64(1), object(3)
memory usage: 12.4 MB
None
```

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

What is the difficulty level of this exercise?

﻿

## Python: Tips of the Day

Returns the symmetric difference between two lists, after applying the provided function to each list element of both

Example:

```def tips_symmetric_difference_by(p, q, fn):
_p, _q = set(map(fn, p)), set(map(fn, q))
return [item for item in p if fn(item) not in _q] + [item for item in q if fn(item) not in _p]
from math import floor
print(tips_symmetric_difference_by([4.2, 2.4], [4.6, 6.8],floor))
```

Output:

```[2.4, 6.8]
```