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.

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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

s = pd.Series([], dtype='float64')
print('full' if s.all() else 'empty')

# Explanation
The `pandas.Series.all` documentation says the following:

> * It returns whether all elements are `True`, potentially over an axis.
>
> * It returns `True` unless there is at least one element within a series or along a DataFrame axis that is `False` or something that is equivalent (for example, zero or empty).

The last bullet point explains what we see. There aren't any `False` elements in the empty series. The built-in `all` function behaves the same way, like this:
```py
In [1]: all([])
Out[1]: True
```
The `all` function is like the mathematical **∀** symbol, "By convention, the formula ∀x∈∅,P(x) is always true, regardless of the formula
P(x) …"

The `any` function has the same logic, only reversed. It implies at least one element exists that is always `False`, which we can see in the sequence below:
```py
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: any([])
Out[3]: False
In [5]: pd.Series([], dtype=np.float64).any()
Out[5]: False
```

Let's find out how the all() and any() methods work with pandas series.

Before We Begin

Puzzles

Wrapping Up

The all() Method with Pandas Series

Try it yourself

Explanation