G4. Manipulating data

Question 1

Operations that can be applied on top of tabular data structures and what is the result type?

Answer 1

Projection
Selection (retrieving a subset of records)
Filter (retrieving a subset of records given a condition).
Result type: DataFrame

Question 2

• Retrieving a subset of columns/attributes

Answer 2

Projection~read (muestra todo con lo que voy a trabajar)

Realizar una proyección para seleccionar solo las columnas ‘Nombre’ y ‘Edad’
proyeccion = df[[‘Nombre’, ‘Edad’]]

print(proyeccion)

Question 3

Retrieving a subset of records

Answer 3

Selection. (muestra un rango en el que me interesa)

edu.loc[90:94][[‘TIME’,’GEO’]]
Selection=df[df[‘Nombre’]

Question 4

Another way to select a subset of data is by applying Boolean indexing.

Answer 4

Filtering (Muestra lo que cumpla con una condición lógica)

edu[edu[‘Value’] > 6.5].tail()
filtered_data = df[df[‘column’] > value][[‘column1’, ‘column2’]]

Question 5

Boolean indexes

Answer 5

Uses the result of a Boolean operation over the data, returning a mask with True or False for each row. The rows marked True in the mask will be selected.

Question 6

(not a number) to represent missing values.

Answer 6

NaN

Question 7

Give examples particularly the way null values can be filtered. How does this work in R?

Answer 7

edu[edu[“Value”].isnull()].head()
# R Filtra filas sin valores faltantes en una columna específica (por ejemplo, ‘columna1’)
new_data <- original_data[!is.na(original_data$columna1), ]

Question 8

Which is the form of the expressions for adding columns to a DataFrame? and Rows?

Answer 8

assign a Seriesto a selection of a column that does not exist.
edu[‘ValueNorm’] = edu[‘Value’]/edu[‘Value’].max()

Question 9

Which is the form of the expressions for adding rows to a DataFrame?

Answer 9

This function receives as argument the new row, which is represented as a dictionary where the keys are the name of the columns and the values are the associated value.
edu = edu.append({“TIME”: 2000, “Value”: 5.00, “GEO”: ‘a’},
ignore_index = True)

Question 10

How can rows or columns be deleted?

Answer 10

Now, if we want to remove this column from the DataFrame, we can use the function drop. This removes the indicated rows if axis=0, or the indicated columns if axis=1.

Question 11

Do these operators belong to the data definition or the data manipulation language?

Answer 11

Data manipulation

Question 12

How can default values be added to attributes containing missing or null values?

Answer 12

fillna(), specifying which value has to be used.

Question 13

Give an example of the use of the group() method applied on a DataFrame.

Answer 13

group = edu[[“GEO”, “Value”]].groupby(‘GEO’).mean()
group.head()

Question 14

How are manipulation operators associated to DataFrames related and useful
for implementing Data Science processes?

Answer 14

They provide a powerful and flexible set of tools for data scientists to explore, clean, and analyze data efficiently.