Simple Example usage

The tidyversetopandas package brings the familiar syntax of R’s tidyverse to Python’s pandas library, making it an invaluable tool for data scientists and analysts who are transitioning from R to Python. This documentation provides a real-life example demonstrating how to use the key functions of tidyversetopandas, including mutate, filter, select, and arrange.

Getting Started

First, ensure that tidyversetopandas is installed and imported alongside pandas:

# Import necessary packages
import pandas as pd
from tidyversetopandas import tidyversetopandas as ttp

Example Data

We will use a sample dataset representing sales data for illustration. Let’s create a pandas DataFrame:

# Sample data
data = {
    "ProductID": [101, 102, 103, 104],
    "Sales": [250, 150, 300, 200],
    "Region": ["East", "West", "East", "South"],
}
df = pd.DataFrame(data)

# Display the initial DataFrame
print("Initial DataFrame:")
df

Initial DataFrame:

	ProductID	Sales	Region
0	101	250	East
1	102	150	West
2	103	300	East
3	104	200	South

Using `tidyversetopandas`

1. Mutate: Adding and Modifying Columns

Suppose we want to calculate the VAT (Value Added Tax) for each sale, assuming a flat rate of 15%. We can use the mutate function to add this new column:

# Adding a new column for VAT
df = ttp.mutate(df, "VAT = Sales * 0.15")

print("\nDataFrame after applying 'mutate':")
df

DataFrame after applying 'mutate':

	ProductID	Sales	Region	VAT
0	101	250	East	37.5
1	102	150	West	22.5
2	103	300	East	45.0
3	104	200	South	30.0

2. Filter: Row-wise Filtering

If we need to filter the data to include only sales greater than $200, filter comes in handy:

# Filtering rows where sales are greater than 200
df = ttp.filter(df, "Sales > 200")
print("\nDataFrame after applying 'filter':")
df

DataFrame after applying 'filter':

	ProductID	Sales	Region	VAT
0	101	250	East	37.5
2	103	300	East	45.0

3. Select: Choosing Specific Columns

To focus on specific columns, for instance, ProductID and VAT, use the select function:

# Selecting specific columns
df = ttp.select(df, "ProductID", "VAT")
print("\nDataFrame after applying 'select':")
df

DataFrame after applying 'select':

	ProductID	VAT
0	101	37.5
2	103	45.0

4. Arrange: Sorting Data

Finally, to sort the data based on ProductID in descending order:

# Sorting the DataFrame
df = ttp.arrange(df, False, "ProductID")
print("\nDataFrame after applying 'arrange':")
df

DataFrame after applying 'arrange':

	ProductID	VAT
2	103	45.0
0	101	37.5

Conclusion

With these simple and intuitive functions, tidyversetopandas makes the transition from R’s tidyverse to Python’s pandas seamless and efficient. Whether it’s data manipulation, analysis, or preparation for visualization, this package ensures a smooth and familiar workflow for R users in the Python environment.

Remember, this package is in development, and more features and improvements are to be expected. For more detailed information, refer to the full documentation and the repository on GitHub.