Delve into effective data manipulation with pandas. Discover filtering, merging, grouping, and reshaping techniques through hands-on examples, best practices, and performance tips for real-world analysis.

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The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data.

In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas.

The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

Assume a Series `S`. Which method can be used to reset the index in the Series?


<p>Assume a Series <code>S</code>. Which method can be used to reset the index in the Series?</p>


```python3
10    2022
20    2022
30    2022
40    2022
50    2022
```

```python3
10    2022
20    2022
30    2022
40    2022
```

```python3
10    2022
20    2023
30    2024
40    2025
50    2026
```

```python3
10    2022
20    2023
30    2024
40    2025
```

What will be the output of the following command?

```python3
S = pd.Series(2022, index = range(10, 60, 10))
```

<p>What will be the output of the following command?</p>
<pre><code class="language-python3">S = pd.Series(2022, index = range(10, 60, 10))
</code></pre>


Consider a series `S` that contains ten random numbers:

|  **index** | **marks**  |
| - | - |
|  1 |  43 |
|  2 | 30  |
|  3 | 67  |
|  4 |  10 |
|  5 |  35 |
|  6 | 19  |
|  7 |  45 |
|  8 |  61 |
|  9 | 78  |
|  10 |  12 |

Which command will give us the 4 highest numbers in ascending order?


<p>Consider a series <code>S</code> that contains ten random numbers:</p>
<table>
<thead>
<tr>
<th><strong>index</strong></th>
<th><strong>marks</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>43</td>
</tr>
<tr>
<td>2</td>
<td>30</td>
</tr>
<tr>
<td>3</td>
<td>67</td>
</tr>
<tr>
<td>4</td>
<td>10</td>
</tr>
<tr>
<td>5</td>
<td>35</td>
</tr>
<tr>
<td>6</td>
<td>19</td>
</tr>
<tr>
<td>7</td>
<td>45</td>
</tr>
<tr>
<td>8</td>
<td>61</td>
</tr>
<tr>
<td>9</td>
<td>78</td>
</tr>
<tr>
<td>10</td>
<td>12</td>
</tr>
</tbody>
</table>
<p>Which command will give us the 4 highest numbers in ascending order?</p>


Look at the given Series `S` below:

|  **index** | **data**  |
| - | - |
|  0 |  12 |
|  1 | 43  |
|  2 | 30  |
|  3 |  67 |
| 4 | 90|
|  5 |  23 |
|  6 | 40  |

We want to update the numbers below 40 to be equal to 60. Which command will do this?





<p>Look at the given Series <code>S</code> below:</p>
<table>
<thead>
<tr>
<th><strong>index</strong></th>
<th><strong>data</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>12</td>
</tr>
<tr>
<td>1</td>
<td>43</td>
</tr>
<tr>
<td>2</td>
<td>30</td>
</tr>
<tr>
<td>3</td>
<td>67</td>
</tr>
<tr>
<td>4</td>
<td>90</td>
</tr>
<tr>
<td>5</td>
<td>23</td>
</tr>
<tr>
<td>6</td>
<td>40</td>
</tr>
</tbody>
</table>
<p>We want to update the numbers below 40 to be equal to 60. Which command will do this?</p>


Quiz yourself on indexing, manipulation methods, and data cleaning.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: Indexing, Manipulation Methods, and Data Cleaning