Course Content

Advanced Techniques in pandas

Let's move further and continue extracting columns and rows by indices. So, you need to be familiar with a function similar to `loc[]`

.

Our following function is `iloc[]`

; it is like **index-location**, and you may have guessed that it allows us to work with both columns' and rows' indices.

Firstly, we need to recall indices. The **first** row has the index `0`

, the next one `1`

, the next `2`

, and so on. But also, we can count from the end (indeed, this is not convenient in datasets, but it may be helpful somehow), so the **last** one has the index `-1`

, the **second-to-last** is `-2`

, and so on...

Look at the table:

Index from the Beginning | Rows in Dataset | Index from the End |

0 | Banana | -8 |

1 | Apple | -7 |

2 | Cucumber | -6 |

3 | Strawberry | -5 |

4 | Tomato | -4 |

5 | Orange | -3 |

6 | Lemon | -2 |

7 | Blueberry | -1 |

However, we will start with the simplest implementation of the function `iloc[]`

, working with the same data set.

Look to the code example and output:

`data.iloc[0]`

- extracts the**very first**row of the dataset.

`data.iloc[1]`

- extracts the**second**row of the dataset.

`data.iloc[-1]`

- extracts the**very last**row of the dataset.

`data.iloc[-2]`

- extracts the**second-to-last**row of the dataset.

As you may have recognized, at the end of the output, the variable `Name`

shows the row number too, like `Name: 998`

.

Section 1.

Chapter 4