DataGenerator is a Python library used for generating random and structured data. It provides various functions to create data for different purposes such as testing, training, or data analytics. One function of DataGenerator is `add_map_column`. It allows you to add a new column to a dataset by mapping values from an existing column to a new column based on a given function.
For example, let's say you have a dataframe with a column called "age" and you want to add a new column called "age_group" based on the age values. You can use `add_map_column` to map the age values to age groups as shown below:
``` python
from datagenerator import DataGenerator
# Create a dataset
dataset = DataGenerator.create_dataframe({'age': [25, 32, 42, 18, 55]})
# Define a function to map age values to age groups
def map_age_to_group(age):
if age < 20:
return 'Teenager'
elif age < 30:
return 'Young Adult'
elif age < 50:
return 'Middle Aged'
else:
return 'Older Adult'
# Use add_map_column to add a new column based on the mapping function
dataset = DataGenerator.add_map_column(dataset, 'age_group', 'age', map_age_to_group)
# Display the resulting dataframe with the new age_group column
print(dataset)
# Output:
# age age_group
# 0 25 Young Adult
# 1 32 Middle Aged
# 2 42 Middle Aged
# 3 18 Teenager
# 4 55 Older Adult
```
In this example, we used `add_map_column` to add a new column to the dataset called "age_group". We mapped the age values to age groups based on a given function that we defined. The resulting dataframe has the new column with the mapped age groups.
`add_map_column` is part of the `datagenerator.data_ops` module in the DataGenerator package library.
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