To convert a categorical variable to binary (dummy variables) in Python, you can use the `pd.get_dummies()`

function from the pandas library. Here’s an example:

```
import pandas as pd
# Create a DataFrame with a categorical variable
data = {'Category': ['A', 'B', 'A', 'C', 'B']}
df = pd.DataFrame(data)
# Convert the categorical variable to binary
binary_df = pd.get_dummies(df['Category'])
# Concatenate the original DataFrame with the binary representation
df_binary = pd.concat([df, binary_df], axis=1)
# Print the result
print(df_binary)
```

In this example, we have a DataFrame `df`

with a single categorical variable “Category”. The variable has four unique categories: ‘A’, ‘B’, and ‘C’.

To convert the categorical variable to binary, we use the `pd.get_dummies()`

function, passing the column containing the categorical variable (`df['Category']`

) as an argument. This function will create a new DataFrame (`binary_df`

) with binary representation for each category.

We then use the `pd.concat()`

function to concatenate the original DataFrame `df`

with the binary representation (`binary_df`

) along the columns (`axis=1`

).

Finally, we print the resulting DataFrame (`df_binary`

) that contains the original categorical variable and its binary representation.

The output will be:

```
Category A B C
0 A 1 0 0
1 B 0 1 0
2 A 1 0 0
3 C 0 0 1
4 B 0 1 0
```

In this case, the original categorical variable “Category” is converted to binary representation using dummy variables. Each unique category is represented by a separate column with binary values: ‘A’ is represented by column ‘A’, ‘B’ by column ‘B’, and ‘C’ by column ‘C’.

You can adjust the column name and the DataFrame according to your specific use case.

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