piplyr 1.4

dplyr - like data manipulation

piplyr

piplyr is a Python package that provides dplyr-like data manipulation capabilities for pandas DataFrames. It simplifies data manipulation tasks in Python by offering a set of intuitive methods for data filtering, selection, transformation, and aggregation.

Installation

You can install piplyr directly from PyPI:

pip install piplyr

Usage

Here are some basic examples of how to use piplyr:

Initializing piplyr

import pandas as pd
from piplyr import piplyr

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Initialize piplyr with the DataFrame
pi = piplyr(df)

Grouping Data

# Group by a column
grouped_pi = pi.group_by('A')

Sorting Data

# Sort the DataFrame by a column

sorted_pi = pi.sort_by('B').to_df

to_df

Use to_df to convert your generated data to a Pandas' DataFrame

Selecting Columns

# Select specific columns
selected_pi = pi.select('A', 'B')

Dropping Columns

# Drop specific columns
dropped_pi = pi.drop_col('B')

Renaming Columns

# Rename columns in the DataFrame
renamed_pi = pi.rename_col({'A': 'new_A'})

Filtering Rows

# Filter rows based on a condition
filtered_pi = pi.filter_row('A > 1')

Mutating Columns

# Add a new column or modify existing ones
mutated_pi = pi.mutate(new_col=lambda x: x['A'] * 2)

Summarizing Data

# Summarize the DataFrame
summarized_pi = pi.summarize(mean_A=lambda x: x['A'].mean())

Executing SQL Queries

# Execute an SQL query on the DataFrame. You have to use from df regardless of your DataFrame name
sql_pi = pi.sql_plyr('SELECT * FROM df WHERE A > 1')

Chaining Methods

# Chain multiple operations
result_pi = pi.select('A', 'B').filter_row('A > 1').summarize(avg_B=lambda x: x['B'].mean())

Additional Methods

The package also includes several other methods like join, count_na, distinct, pivot_longer, pivot_wider, clean_names, separate, str_pad, str_sub, str_extract, str_detect, str_len, str_lower, str_upper, str_startswith, str_endswith, str_contains, fct_lump, fct_infreq, fct_relevel, fct_recode, fct_reorder and others.

Each of these methods provides specific data manipulation functionalities and can be explored further in the package documentation.

More Examples

Please consult docstrings of various methods that include explnations and examples.

Contributing

Contributions to piplyr are welcome! Please refer to the contribution guidelines for more information.

License

This project is licensed under the MIT License.