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Please enable JavaScript in your browser settings or update your browser.Challenge | Preprocessing Data: Part I
Data Manipulation using pandas
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Data Manipulation using pandas

Data Manipulation using pandas

1. Preprocessing Data: Part I
What is Data Preprocessing?Types consistencyPoor Data PresentationManipulating StringsChallengeReplacing Specific ElementsSimultaneous ReplacementChallenge
2. Preprocessing Data: Part II
Logical InconsistencyRemoving RowsChallengeOutliersChallengeMissing ValuesFilling NA valuesChallenge
3. Grouping Data
What is Grouping Data?Grouping in pandas [1/2]ChallengeGrouping in pandas [2/2]ChallengeGrouping by Several ColumnsChallenge
4. Aggregating and Visualizing Data
Advanced Aggregation [1/2]ChallengeAdvanced Aggregation [2/2]ChallengeHistogramsChallengeBar and Scatter PlotsOther Types of GraphsChallenge 1Challenge 2
5. Joining Data
What is Joining Data?Left JoinRight JoinInner JoinOuter JoinConcatenation

bookChallenge

You've solved the first problem with wrong column type. Let's solve the remaining one (with dots). Recall that there are 4 columns with wrong types left ('morgh', 'valueh', 'grosrth', 'omphtotinch'). These columns considered to have dots as indicators for 'Not applicable'. For instance, columns valueh and grosrth are mutually exclusive, since the first one indicates the price of dwelling (i.e., house is owned) and the second one indicates the monthly rent.

The most appropriate way to solve this problem is to replace dots by NA values. In that case, we would be able to manipulate column like a numerical one.

Tarea
test

Swipe to show code editor

Perform a replacement of dot symbols . by NAs for 'morgh', 'valueh', 'grosrth', 'omphtotinch' columns. Follow the next steps:

  1. Import the NumPy library with np alias.
  2. Apply the .where() method to the df dataframe.
  3. Set the condition what values must remain unchanged. These must be non-dots values.
  4. Set the other parameter to nan value from NumPy.

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 1. Capítulo 8
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bookChallenge

You've solved the first problem with wrong column type. Let's solve the remaining one (with dots). Recall that there are 4 columns with wrong types left ('morgh', 'valueh', 'grosrth', 'omphtotinch'). These columns considered to have dots as indicators for 'Not applicable'. For instance, columns valueh and grosrth are mutually exclusive, since the first one indicates the price of dwelling (i.e., house is owned) and the second one indicates the monthly rent.

The most appropriate way to solve this problem is to replace dots by NA values. In that case, we would be able to manipulate column like a numerical one.

Tarea
test

Swipe to show code editor

Perform a replacement of dot symbols . by NAs for 'morgh', 'valueh', 'grosrth', 'omphtotinch' columns. Follow the next steps:

  1. Import the NumPy library with np alias.
  2. Apply the .where() method to the df dataframe.
  3. Set the condition what values must remain unchanged. These must be non-dots values.
  4. Set the other parameter to nan value from NumPy.

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 1. Capítulo 8
toggle bottom row

bookChallenge

You've solved the first problem with wrong column type. Let's solve the remaining one (with dots). Recall that there are 4 columns with wrong types left ('morgh', 'valueh', 'grosrth', 'omphtotinch'). These columns considered to have dots as indicators for 'Not applicable'. For instance, columns valueh and grosrth are mutually exclusive, since the first one indicates the price of dwelling (i.e., house is owned) and the second one indicates the monthly rent.

The most appropriate way to solve this problem is to replace dots by NA values. In that case, we would be able to manipulate column like a numerical one.

Tarea
test

Swipe to show code editor

Perform a replacement of dot symbols . by NAs for 'morgh', 'valueh', 'grosrth', 'omphtotinch' columns. Follow the next steps:

  1. Import the NumPy library with np alias.
  2. Apply the .where() method to the df dataframe.
  3. Set the condition what values must remain unchanged. These must be non-dots values.
  4. Set the other parameter to nan value from NumPy.

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!

You've solved the first problem with wrong column type. Let's solve the remaining one (with dots). Recall that there are 4 columns with wrong types left ('morgh', 'valueh', 'grosrth', 'omphtotinch'). These columns considered to have dots as indicators for 'Not applicable'. For instance, columns valueh and grosrth are mutually exclusive, since the first one indicates the price of dwelling (i.e., house is owned) and the second one indicates the monthly rent.

The most appropriate way to solve this problem is to replace dots by NA values. In that case, we would be able to manipulate column like a numerical one.

Tarea
test

Swipe to show code editor

Perform a replacement of dot symbols . by NAs for 'morgh', 'valueh', 'grosrth', 'omphtotinch' columns. Follow the next steps:

  1. Import the NumPy library with np alias.
  2. Apply the .where() method to the df dataframe.
  3. Set the condition what values must remain unchanged. These must be non-dots values.
  4. Set the other parameter to nan value from NumPy.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 8
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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