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freeCodeCamp/curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-with-dataframes.md
Ilenia a38922536c Adding language to code fences and pre to console logs (#40603)
* added languages to prismjs

* added language to code fences for python for everybody

* multiline pre to python for everybody questions

* multiline pre for data analysis with python

* multiline pre in numpy questions

* single line pre for python for everybody

* single line pre for data analysis with python

* single line pre for numpy

* Revert "multiline pre in numpy questions"

This reverts commit af1a02cdd3.

* fix unneded escaping

commit suggestions from code review

Co-authored-by: Shaun Hamilton <51722130+ShaunSHamilton@users.noreply.github.com>

* Revert "single line pre for numpy"

This reverts commit 5f90981108.

* revert changes to snippet that have a language

* one last code fence to pre

* Revert "fix unneded escaping
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This reverts commit 64c84a9213.

* Revert "single line pre for data analysis with python"

This reverts commit 3bccaff753.

* Revert "single line pre for python for everybody"

This reverts commit 03a5379062.

* remove unnecessary escape

Co-authored-by: Shaun Hamilton <51722130+ShaunSHamilton@users.noreply.github.com>
2021-01-21 12:12:42 -06:00

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---
id: 5e9a093a74c4063ca6f7c15e
title: Data Cleaning with DataFrames
challengeType: 11
videoId: sTMN_pdI6S0
dashedName: data-cleaning-with-dataframes
---
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
More resources:
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
# --question--
## --text--
What will the following code print out?
```py
import pandas as pd
import numpy as np
s = pd.Series([np.nan, 1, 2, np.nan, 3])
s = s.fillna(method='ffill')
print(s)
```
## --answers--
<pre>
0 1.0
1 1.0
2 2.0
3 3.0
4 3.0
dtype: float64
</pre>
---
<pre>
0 NaN
1 1.0
2 2.0
3 2.0
4 3.0
dtype: float64
</pre>
---
<pre>
0 NaN
1 1.0
2 2.0
3 NaN
4 3.0
dtype: float64
</pre>
## --video-solution--
2