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

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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

df = pd.DataFrame([
  [1, 1, 1],
  [2, 2, 2],
  [3, 3, 3],
  [4, 4, 4],
  [5, 5, 5],
])

print(len(df.loc[1:3]))

# Explanation
Slices in Python are #key# half-open ranges: A half-open range includes the first element but excludes the last one. #key#. We get values from the first index up to, but not including, the last index:
```py
In [1]: chars = ['a', 'b', 'c', 'd', 'e']
In [2]: chars[1:3]
Out[2]: ['b', 'c']
```
Most of the time, pandas works in the same way, which we can see below:
```py
In [3]: s = pd.Series(chars)
In [4]: s[1:3]
Out[4]:
1 b
2 c
dtype: object
```
Here are three different ways to slice a `pandas.Series` or a `pandas.DataFrame`:
* Use `loc`, which works by label.
* Use `iloc`, which works by offset.
* Use a `slice` notation such as `s[1:3]`, which works like `iloc`.

The `loc` function works by label, and it slices on a closed range that includes the last index, like this:
```py
In [5]: df[1:3]
Out[5]:
  0 1 2
1 2 2 2
2 3 3 3
In [6]: df.iloc[1:3]
Out[6]:
  0 1 2
1 2 2 2
2 3 3 3
In [7]: df.loc[1:3]
Out[7]:
  0 1 2
1 2 2 2
2 3 3 3
3 4 4 4
```
We should remember to watch out for this off-by-one error when using `.loc`.

Let's find out how length and slicing operations work in pandas DataFrames.

Before We Begin

Puzzles

Wrapping Up

Print the Length of DataFrames at a Specified Location

Try it yourself

Explanation