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

 What will be the output of following code?

```python
 import pandas as pd
 L = [ 'A', 'B', 'C', 'D', 'E']
 df = pd.DataFrame(L)
 print(df[ -2 : ])
```

<p>What will be the output of following code?</p>
<pre><code class="language-python"> import pandas as pd
 L = [ 'A', 'B', 'C', 'D', 'E']
 df = pd.DataFrame(L)
 print(df[ -2 : ])
</code></pre>


We're given a DataFrame that has 10 rows and 3 columns. Which rows of the data frame will the following command show?

```python
df.head(-1)
```

<p>We’re given a DataFrame that has 10 rows and 3 columns. Which rows of the data frame will the following command show?</p>
<pre><code class="language-python">df.head(-1)
</code></pre>


```python
   a    b
a  1    2
b  2    3
c  3    4
```

```python
   a     b    c
a  1     2    NaN  
b  NaN   2    3
```

```python
    a    b
a   1    2
b   2    3
```


```python
    a    b
a  1.0  2.0
b  2.0  NaN
c  NaN  3.0
```

Which DataFrame will be created using the following code?

```python
import pandas as pd
S = {'a':pd.Series(range(1, 3), index = ['a', 'b']),
'b':pd.Series(range(2, 4), index = ['a', 'c']),
}
df = pd.DataFrame(S)
print(df)
```

<p>Which DataFrame will be created using the following code?</p>
<pre><code class="language-python">import pandas as pd
S = {'a':pd.Series(range(1, 3), index = ['a', 'b']),
'b':pd.Series(range(2, 4), index = ['a', 'c']),
}
df = pd.DataFrame(S)
print(df)
</code></pre>


This will throw a "division by 0" error because one of the values is 0.

|   | **M1**  | **M2**  | **M3**  |
| - | - | - | - |
|  **S1** |  NaN | 1  |  0.5 |
| **S2**  |  0.333333 | 4  |  0.2 |

|   | **M1**  | **M2**  | **M3**  |
| - | - | - | - |
|  **S1** |  Inf | 1  |  0.5 |
| **S2**  |  0.333333 | 0.25 |  0.2 |

Suppose we have a `df1` with the following content:

|   | **M1**  | **M2**  | **M3**  |
| - | - | - | - |
|  **S1** |  0 | 1  |  2 |
| **S2**  |  3 | 4  |  5 |

Suppose the following operation is then performed:

```python
1/df1
```

What will be the output?

<p>Suppose we have a <code>df1</code> with the following content:</p>
<table>
<thead>
<tr>
<th></th>
<th><strong>M1</strong></th>
<th><strong>M2</strong></th>
<th><strong>M3</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>S1</strong></td>
<td>0</td>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td><strong>S2</strong></td>
<td>3</td>
<td>4</td>
<td>5</td>
</tr>
</tbody>
</table>
<p>Suppose the following operation is then performed:</p>
<pre><code class="language-python">1/df1
</code></pre>
<p>What will be the output?</p>


This cannot be done as the dimensions of the DataFrame `df2` contain an extra row that is not there in `df1` as well as a different column.

|   | **M1**  | **M2**  | **M3**  | **M4** |
| - | - | - | - | - |
|  **S1** |  NaN | NaN  |  NaN | NaN |
| **S2**  |  0 | NaN  |  3 | NaN |
| **S3**  |  9 | NaN  |  12 | NaN|

|   | **M1**  | **M2**  | **M3**  | **M4** |
| - | - | - | - | - |
|  **S1** |  0 | 0  |  0 | 0 |
| **S2**  |  0 | 0  |  3 | 0 |
| **S3**  |  9 | 0  |  12 | 0|

|   | **M1**  | **M2**  | **M3**  | **M4** |
| - | - | - | - | - |
|  **S1** |  0 | NaN  |  4 | 5 |
| **S2**  |  3 | 1  |  3 | 2 |
| **S3**  |  9 | 4  |  12 | 8|

```

Assume that we have a DataFrame `df1`:

|   | **M1**  | **M2**  | **M3**  |
| - | - | - | - |
|  **S1** |  0 | 1  |  2 |
| **S2**  |  3 | 4  |  5 |

And we have another DataFrame `df2`: 

|   | **M1**  | **M2**  | **M3**  |
| - | - | - | - |
|  **S1** |  0 | 1  |  2 |
| **S2**  |  3 | 4  |  5 |
| **S3**  |  6 | 7  |  8 |

The following command is then entered:

```python
df1.add(df2, fill_value = 0)
```

What will be the output?



<p>Assume that we have a DataFrame <code>df1</code>:</p>
<table>
<thead>
<tr>
<th></th>
<th><strong>M1</strong></th>
<th><strong>M2</strong></th>
<th><strong>M3</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>S1</strong></td>
<td>0</td>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td><strong>S2</strong></td>
<td>3</td>
<td>4</td>
<td>5</td>
</tr>
</tbody>
</table>
<p>And we have another DataFrame <code>df2</code>:</p>
<table>
<thead>
<tr>
<th></th>
<th><strong>M1</strong></th>
<th><strong>M2</strong></th>
<th><strong>M3</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>S1</strong></td>
<td>0</td>
<td>1</td>
<td>2</td>
</tr>
<tr>
<td><strong>S2</strong></td>
<td>3</td>
<td>4</td>
<td>5</td>
</tr>
<tr>
<td><strong>S3</strong></td>
<td>6</td>
<td>7</td>
<td>8</td>
</tr>
</tbody>
</table>
<p>The following command is then entered:</p>
<pre><code class="language-python">df1.add(df2, fill_value = 0)
</code></pre>
<p>What will be the output?</p>


Quiz yourself on DataFrames and math methods on DataFrames.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: DataFrames and Math Methods on DataFrames