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

df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['b', 'c'])
df = pd.concat([df1, df2])

print(df.dtypes)

# Explanation
If we look at the dtypes of `df1` and `df2`, we'll see they're `int64`:
```py
In [1]: df1.dtypes
Out[1]:
a int64
b int64
dtype: object
In [2]: df2.dtypes
Out[2]:
b int64
c int64
dtype: object
```
Why did the teaser output show the `a` and `c` columns as `float64`?



The `pandas.concat` can handle frames with different columns. By default, it will assume `nan` values in the missing labels for a specific column. As we saw in
the puzzle [Div Sum](https://www.educative.io/courses/pandas-brain-teasers/the-sum-method-with-pandas-series), pandas will change the dtype of a series to allow missing values, and that’s what’s happening here.

Let's find out how concatenation works in a pandas DataFrame.

Before We Begin

Puzzles

Wrapping Up

Concatenate Values of Two DataFrames

Try it yourself

Explanation