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

start = pd.Timestamp.fromtimestamp(0).strftime('%Y-%m-%d')
times = pd.date_range(start=start, freq='M', periods=2)
print(times)

# Explanation
There are couple of puzzling things here:
* `M` is a month frequency.
* The first date is January 31st and not January 1st.

Let’s start with `M` being a month frequency. You’ve probably used the infamous `strftime` or its cousin `strptime` to convert `datetime` to or from strings. In those cases, `M` stands for minute:
```py
In [1]: t = pd.Timestamp(2020, 5, 10, 14, 21, 30)
In [2]: t.strftime('%H:%M')
Out[2]: '14:21'
```
One of the things most programmers like about pandas is that it’s one of the best-documented
open-source packages available. But, pandas is a big library, and sometimes it’s hard to find what we're looking for.

If we look at the `pandas.date_range` [documentation](https://pandas.pydata.org/docs/reference/api/pandas.date_range.html), we'll see the following:
>"**freq:** *str or DateOffset, default ‘D’*
>
>Frequency strings can have multiples, e.g., ‘5H’." 

The `M` in `date_range` stands for month-end frequency whereas the minute frequency is `T` or `min`.

This solves one puzzle and also gives us a hint as to why we see January 31st
and not January 1st. In the puxxle [Y3K](https://www.educative.io/courses/pandas-brain-teasers/create-a-pandas-timestamp-object), we saw that time 0, or epoch time, is counted from January 1, 1970.
```py
In [3]: pd.Timestamp(0)
Out[3]: Timestamp('1970-01-01 00:00:00')
```
If we follow the code of `pandas.date_range`, we'll see it converts the `freq` from `str`
to a `pandas.DateOffset`. Then, the `date_range` will use `pandas.DataOffset.apply` on start. From
there it’ll add the offset for `period` times.
Here's how this is done:
```py
In [4]: from pandas.tseries.frequencies import to_offset
In [5]: start = pd.Timestamp(0)
In [6]: offset = to_offset('M')
In [7]: offset
Out[7]: <MonthEnd>
In [8]: t0 = offset.apply(start)
In [9]: t0
Out[9]: Timestamp('1970-01-31 00:00:00')
In [10]: t0 + offset
Out[10]: Timestamp('1970-02-28 00:00:00')
```

This is what we see in this teaser’s output. Note that frequencies don’t have to be whole units. The following will give us
a date range in five-minute intervals.
```py
In [11]: pd.date_range(start=pd.Timestamp(0), periods=3, freq='5T')
Out[21]:
DatetimeIndex(['1970-01-01 00:00:00', '1970-01-01 00:05:00',
'1970-01-01 00:10:00'],
dtype='datetime64[ns]', freq='5T')
```

Let's find out how to use a pandas Timestamp and date_range.

Before We Begin

Puzzles

Wrapping Up

Create a Pandas Timestamp and date_range Object

Try it yourself

Explanation