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Calculating the Number of Missing Values | Preprocessing Data
Advanced Techniques in pandas
course content

Contenido del Curso

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
2. Dealing With Conditions
3. Extracting Data
4. Aggregating Data
5. Preprocessing Data

bookCalculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarea

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 5. Capítulo 2
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bookCalculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarea

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 5. Capítulo 2
toggle bottom row

bookCalculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarea

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Tarea

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 5. Capítulo 2
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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