Contenido del Curso
Preprocessing Data
Preprocessing Data
Types Conversion
You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:
- storing integer or float values as string variables.
- storing date and time values as strings.
- storing values in a form that can be converted to a more suitable one.
Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:
| unnamed | id | diet | pulse | time | kind |
| 35 | 12 | low fat | 104 | 30 min | walking |
| 64 | 22 | low fat | 104 | 15 min | running |
| 10 | 4 | low fat | 82 | 15 min | rest |
| 18 | 7 | no fat | 87 | 1 min | rest |
| 48 | 17 | no fat | 103 | 1 min | walking |
It makes sense to modify the time column data: all rows contain the duration in minutes, so info about time units (min, sec, ot hours) is useless. We're gonna remove the extra symbols and store only numerical values, which additionally will be converted to int.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¿Todo estuvo claro?
Types Conversion
You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:
- storing integer or float values as string variables.
- storing date and time values as strings.
- storing values in a form that can be converted to a more suitable one.
Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:
| unnamed | id | diet | pulse | time | kind |
| 35 | 12 | low fat | 104 | 30 min | walking |
| 64 | 22 | low fat | 104 | 15 min | running |
| 10 | 4 | low fat | 82 | 15 min | rest |
| 18 | 7 | no fat | 87 | 1 min | rest |
| 48 | 17 | no fat | 103 | 1 min | walking |
It makes sense to modify the time column data: all rows contain the duration in minutes, so info about time units (min, sec, ot hours) is useless. We're gonna remove the extra symbols and store only numerical values, which additionally will be converted to int.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¿Todo estuvo claro?
Types Conversion
You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:
- storing integer or float values as string variables.
- storing date and time values as strings.
- storing values in a form that can be converted to a more suitable one.
Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:
| unnamed | id | diet | pulse | time | kind |
| 35 | 12 | low fat | 104 | 30 min | walking |
| 64 | 22 | low fat | 104 | 15 min | running |
| 10 | 4 | low fat | 82 | 15 min | rest |
| 18 | 7 | no fat | 87 | 1 min | rest |
| 48 | 17 | no fat | 103 | 1 min | walking |
It makes sense to modify the time column data: all rows contain the duration in minutes, so info about time units (min, sec, ot hours) is useless. We're gonna remove the extra symbols and store only numerical values, which additionally will be converted to int.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¿Todo estuvo claro?
You can discover that data can be stored in the dataset in the wrong format or type. The most common cases are:
- storing integer or float values as string variables.
- storing date and time values as strings.
- storing values in a form that can be converted to a more suitable one.
Let's explore the dataset exercise containing info about diet, pulse, time, and kind of different exercises. There is sample data:
| unnamed | id | diet | pulse | time | kind |
| 35 | 12 | low fat | 104 | 30 min | walking |
| 64 | 22 | low fat | 104 | 15 min | running |
| 10 | 4 | low fat | 82 | 15 min | rest |
| 18 | 7 | no fat | 87 | 1 min | rest |
| 48 | 17 | no fat | 103 | 1 min | walking |
It makes sense to modify the time column data: all rows contain the duration in minutes, so info about time units (min, sec, ot hours) is useless. We're gonna remove the extra symbols and store only numerical values, which additionally will be converted to int.
Tarea
Apply the type conversion to the time column. Remove the last 4 symbols which are equal to min and convert the rest to int. Check the sample.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opcionesCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones