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
def relu(n):
  if n < 0:
    return 0
  return n
    
arr = pd.Series([-1, 0, 1])
print(relu(arr))

# Explanation
The problematic line is `if n < 0:`. Here `n` is the result of `arr < 0`, which is a `pandas.Series`.
```py
In [1]: import pandas as pd
In [2]: arr = pd.Series([-1, 0, 1])
In [3]: arr < 0
Out[3]:
0 True
1 False
2 False
dtype: bool
```
Once `arr < 0` is computed, we use it in an `if` statement because of how Boolean values work in Python.
Every Python object, not just `True` and `False`, has a Boolean value.

We can test the truth value of a Python object using the built-in `bool` function. In Python, everything is `True` except the following:
* $0$ numbers like $0$, $0.0$ and $0+0j$.
* Empty collections like `[]`, `{}` and `''`.
* `None`
* `False`


In addition, any object can state its own Boolean value using
the `__bool__` special method. The Boolean logic for `pandas.Series` is different from the logic of a list or a tuple because it raises an exception.

```py
In [4]: bool(arr < 0)
...
ValueError: The truth value of a Series is ambiguous.
Use a.empty, a.bool(), a.item(), a.any() or a.all().
```

Let's find out why the puzzle gives a ValueError and how we can fix it.

Before We Begin

Puzzles

Wrapping Up

Relational Operators with Pandas Series

Try it yourself

Explanation