Explore puzzles designed to enhance your critical thinking and problem-solving skills using pandas. Gain insights into common pitfalls and sharpen your data manipulation techniques.

test.tar.gz

test

test2

Puzzles are a great way to practice your critical thinking and problem-solving skills. This course assumes a basic understanding of pandas.  The teasers and puzzles in this course are designed to help you practice what you already know and help avoid future mistakes.

Pandas Brain Teasers

# Try it yourself
Try executing the code below to see the result.

import pandas as pd

v = pd.Series([.1, 1., 1.1])
out = v * v
expected = pd.Series([.01, 1., 1.21])
if (out == expected).all():
  print('Math rocks!')
else:
  print('Please reinstall universe & reboot.')

# Explanation
The `out == expected` command returns a Boolean `pandas.Series`. The `all` method returns `True` if all elements are `True`.

When we look at `out` and `expected`, they seem the same.
```py
In [1]: out
Out[1]:
0 0.01
1 1.00
2 1.21
dtype: float64
In [2]: expected
Out[2]:
0 0.01
1 1.00
2 1.21
dtype: float64
```
But, when we compare them, we see something strange.
```
In [2]: out == expected
Out[2]:
0 False
1 True
2 False
dtype: bool
```
In both `out` and `expected`, only the middle value `1.00` is equal.
Looking deeper, we can see the problem.
```py
In [3]: print(out[2])
1.2100000000000002
```
There's a difference between how pandas shows the value and how
`print` does.


Let's find out how a pandas series works with the all method and equality operator.



Multiplying Values of Pandas Series

Let's find out how a pandas series works with the all method and equality operator.

Before We Begin

Puzzles

Wrapping Up

Multiplying Values of Pandas Series

Try it yourself

Explanation