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

df = pd.DataFrame([
  ['Sterling', 83.4],
  ['Cheryl', 97.2],
  ['Lana', 13.2],
], columns=['name', 'sum'])
df.late_fee = 3.5
print(df)

# Explanation
Where did the `late_fee` column go?
Python’s objects are very dynamic. We can add attributes to most of them
as we please.
```py
In [1]: class Point:
...: def __init__(self, x, y):
...: self.x, self.y = x, y
In [2]: p = Point(1, 2)
In [3]: p.x, p.y
Out[3]: (1, 2)
In [4]: p.z = 3
In [5]: p.z
Out[5]: 3
```
Pandas lets us access columns both by square brackets (for example, `df[name]`) and by attributes (`df.name`). But, square brackets are a much better choice than attributes in all scenarios.

One reason it's good to use square brackets is that when we add an attribute to a `DataFrame`, it doesn't register as a new column. Another reason is that column names in CSV,
JSON, and other formats can contain spaces or other characters that are not
valid Python identifiers, meaning we won’t be able to find them through
attribute access. If we try to do that, `df.product id` will fail while `df['product id']` will work.
The last reason we should only use square brackets is that it's confusing when we don't.
```py
In [6]: df.sum
Out[6]:
<bound method DataFrame.sum of name sum
0 Sterling 83.4
1 Cheryl 97.2
2 Lana 13.2>
```
In the snippet above, we get the DataFrame `sum` method and not the `sum` column. Also, most people probably expect `late_fee` to be a series like the other columns. But, as we can see in the snippet below, it's not:
```py
In [7]: df.late_fee
Out[7]: 3.5
```

Sometimes we would like to add metadata to a DataFrame, like the name of the
file where the data was read from. Instead of adding a new attribute—for example, `df.originating_file = '/path/to/sales.db'`—there’s an experimental attribute called `attrs` for storing metadata in a
DataFrame.
```py
In [8]: df.attrs['originating_file'] = '/path/to/sales.db'
In [9]: df.attrs
Out[9]: {'originating_file': '/path/to/sales.db'}
```

Let's find out how to add a column to a pandas DataFrame.

Before We Begin

Puzzles

Wrapping Up

Insert Columns into a Pandas DataFrame

Try it yourself

Explanation