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

**(True or False)** The `stack()` function has no parameters.


(True or False) The <code>stack()</code> function has no parameters.

```python
student_ID  Marks  Grade
0           23     98     A
1           43     79     B
2           12     54     D
3           33     87     A
4           67     65     C
5           89     NA     NA
```


```python
0  student_ID    23
   Marks         98
   Grade          A
1  student_ID    43
   Marks         79
   Grade          B
2  student_ID    12
   Marks         54
   Grade          D
3  student_ID    33
   Marks         87
   Grade          A
4  student_ID    67
   Marks         65
   Grade          C
5  student_ID    89 
   Marks         NA
   Grade         NA
```

```python
0  student_ID    23
   Marks         98
   Grade          A
1  student_ID    43
   Marks         79
   Grade          B
2  student_ID    12
   Marks         54
   Grade          D
3  student_ID    33
   Marks         87
   Grade          A
4  student_ID    67
   Marks         65
   Grade          C
```

```python
0  student_ID    23
   Marks         98
   Grade          A
1  student_ID    43
   Marks         79
   Grade          B
2  student_ID    12
   Marks         54
   Grade          D
3  student_ID    33
   Marks         87
   Grade          A
4  student_ID    67
   Marks         65
   Grade          C
5  student_ID    89
```

Given the following DataFrame, if `stack()` function is used, what will be the output?

```python
student_ID  Marks  Grade
0           23     98     A
1           43     79     B
2           12     54     D
3           33     87     A
4           67     65     C
5           89     NA     NA

```


Given the following DataFrame, if <code>stack()</code> function is used, what will be the output?
<pre><code class="language-python">student_ID Marks Grade
0 23 98 A
1 43 79 B
2 12 54 D
3 33 87 A
4 67 65 C
5 89 NA NA

</code></pre>

There will be an error because before the parameter `dropna`, we also need to specify `level`.

```python
0  student_ID    23
   Marks         98
   Grade          A
1  student_ID    43
   Marks         79
   Grade          B
2  student_ID    12
   Marks         54
   Grade          D
3  student_ID    33
   Marks         87
   Grade          A
4  student_ID    67
   Marks         65
   Grade          C
5  student_ID    89 
```

Given this DataFrame called `df`, what will be the output after the following statement?

```python
df.stack(dropna=False)
```

```python
student_ID  Marks  Grade
0           23     98     A
1           43     79     B
2           12     54     D
3           33     87     A
4           67     65     C
5           89     NA     NA
```


Given this DataFrame called <code>df</code>, what will be the output after the following statement?
<pre><code class="language-python">df.stack(dropna=False)
</code></pre>
<pre><code class="language-python">student_ID Marks Grade
0 23 98 A
1 43 79 B
2 12 54 D
3 33 87 A
4 67 65 C
5 89 NA NA
</code></pre>

**(True or False)** The `unstack()` function has a parameter `level`, which can be assigned a negative value.

(True or False) The <code>unstack()</code> function has a parameter <code>level</code>, which can be assigned a negative value.

**(True or False)** The `melt()` function is used to change the DataFrame format from wide to long.

(True or False) The <code>melt()</code> function is used to change the DataFrame format from wide to long.

**(True or False)** The `unmelt()` function is the opposite of a `melt()` function.

(True or False) The <code>unmelt()</code> function is the opposite of a <code>melt()</code> function.

Quiz yourself on melting, transposing, stacking, and unstacking data.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: Melting, Transposing, Stacking, and Unstacking Data