phenome-utils 0.3.0

A Python package for data manipulation and analysis utilities

📚 Python Script Documentation for main.py

Welcome to the documentation for the main.py file. This file contains a series of utility functions designed to manipulate, transform, and analyze pandas DataFrames. The main modules used in this script are pandas, numpy, itertools, and matplotlib.

Installation

pip install phenome-utils

OR

pip install git+https://git.phenome.health/trent.leslie/phenome-utils

📑 Index

1. sum_and_sort_columns
2. binary_threshold_matrix_by_col
3. binary_threshold_matrix_by_row
4. concatenate_csvs_in_directory
5. aggregate_function
6. aggregate_duplicates
7. generate_subcategories
8. generate_subcategories_with_proportions
9. bin_continuous
10. load_latest_yyyymmdd_file
11. remove_rows_with_na_threshold
12. impute_na_in_columns

---

1. sum_and_sort_columns

Description

Sum the numerical columns of a DataFrame, remove columns with a sum of zero, and sort the columns in descending order based on their sum. Optionally, plot a histogram of the non-zero column sums.

Parameters

Parameter	Type	Description
df	DataFrame	The input DataFrame.
plot_histogram	bool	Whether to plot a histogram of the non-zero column sums. Defaults to False.

Returns

Type	Description
DataFrame	A DataFrame with columns sorted in descending order based on their sum, zero-sum columns removed, and non-numeric columns preserved as the first columns.

Example Usage

# Example code demonstrating usage
sorted_df = sum_and_sort_columns(df, plot_histogram=True)

📊 Visualization

If plot_histogram is set to True, a histogram of the non-zero column sums will be displayed.

---

2. binary_threshold_matrix_by_col

Description

Convert a DataFrame's numerical columns to a binary matrix based on percentile thresholds and filter based on a second DataFrame.

Parameters

Parameter	Type	Description
df	DataFrame	The input DataFrame.
lower_threshold	int	The lower percentile threshold. Defaults to 1.
upper_threshold	int	The upper percentile threshold. Defaults to 99.
second_df	DataFrame	A second DataFrame with 'subcategory' and 'decimal_proportion' columns.
decimal_proportion_threshold	float	Threshold for filtering the second DataFrame. Defaults to 0.1.
filter_column	str	Column name in the original DataFrame to filter based on 'subcategory' from the second DataFrame.

Returns

Type	Description
DataFrame	A binary matrix where numerical column values outside the thresholds are 1 and within the thresholds are 0. Object columns are preserved.

Example Usage

# Example code demonstrating usage
binary_df = binary_threshold_matrix_by_col(df, lower_threshold=5, upper_threshold=95, second_df=second_df, filter_column='category')

---

3. binary_threshold_matrix_by_row

Description

Convert a DataFrame's numerical rows to a binary matrix based on percentile thresholds and filter based on a second DataFrame.

Parameters

Parameter	Type	Description
df	DataFrame	The input DataFrame.
lower_threshold	int	The lower percentile threshold. Defaults to 1.
upper_threshold	int	The upper percentile threshold. Defaults to 99.
second_df	DataFrame	A second DataFrame with 'subcategory' and 'decimal_proportion' columns.
decimal_proportion_threshold	float	Threshold for filtering the second DataFrame. Defaults to 0.1.
filter_column	str	Column name in the original DataFrame to filter based on 'subcategory' from the second DataFrame.

Returns

Type	Description
DataFrame	A binary matrix where numerical row values outside the thresholds are 1 and within the thresholds are 0. Object columns are preserved.

Example Usage

# Example code demonstrating usage
binary_df = binary_threshold_matrix_by_row(df, lower_threshold=5, upper_threshold=95, second_df=second_df, filter_column='category')

---

4. concatenate_csvs_in_directory

Description

Concatenate CSV files from a root directory and its subdirectories.

Parameters

Parameter	Type	Description
root_dir	str	The root directory to start the search.
filter_string	str	A string that must be in the filename to be included. Defaults to None.
file_extension	str	The file extension to search for. Defaults to "csv".
csv_filename	str	The filename to save the concatenated CSV. If not provided, returns the DataFrame.

Returns

Type	Description
DataFrame or None	A concatenated DataFrame of all the CSVs if csv_filename is not provided. Otherwise, saves the DataFrame and returns None.

Example Usage

# Example code demonstrating usage
concatenated_df = concatenate_csvs_in_directory('/path/to/directory', filter_string='data', csv_filename='output.csv')

---

5. aggregate_function

Description

General-purpose aggregation function.

Parameters

Parameter	Type	Description
x	pd.Series	Input series
numeric_method	str	Method to aggregate numeric data. Supports 'median', 'mean', and 'mode'. Default is 'median'.
substitute	any	Value to substitute when all values are NaN or mode is empty. Default is np.nan.

Returns

Type	Description
any	Aggregated value

Example Usage

# Example code demonstrating usage
aggregated_value = aggregate_function(pd.Series([1, 2, 3, np.nan]), numeric_method='mean')

---

6. aggregate_duplicates

Description

Aggregates duplicates in a dataframe based on specified grouping columns and a chosen aggregation method for numeric types.

Parameters

Parameter	Type	Description
df	pd.DataFrame	Input dataframe
group_columns	list	List of column names to group by
numeric_method	str	Method to aggregate numeric data. Supports 'median', 'mean', and 'mode'. Default is 'median'.
substitute	any	Value to substitute when all values are NaN or mode is empty. Default is np.nan.

Returns

Type	Description
pd.DataFrame	Dataframe with aggregated duplicates

Example Usage

# Example code demonstrating usage
aggregated_df = aggregate_duplicates(df, group_columns=['category'], numeric_method='mean')

---

7. generate_subcategories

Description

Generate subcategories by combining values from specified columns.

Parameters

Parameter	Type	Description
df	pd.DataFrame	The input dataframe.
columns	list	List of columns to generate subcategories from.
col_separator	str	Separator to use between column names for new columns. Defaults to '_'.
val_separator	str	Separator to use between values when combining. Defaults to ' '.
missing_val	str	Value to replace missing data in specified columns. Defaults to 'NA'.

Returns

Type	Description
pd.DataFrame	DataFrame with new subcategory columns.
list	List of all column names (original + generated).

Example Usage

# Example code demonstrating usage
df, all_columns = generate_subcategories(df, columns=['col1', 'col2'])

---

8. generate_subcategories_with_proportions

Description

Generate subcategories by combining values from specified columns and calculate their proportions.

Parameters

Parameter	Type	Description
df	pd.DataFrame	The input dataframe.
columns	list	List of columns to generate subcategories from.
solo_columns	list	List of columns to be considered on their own.
col_separator	str	Separator to use between column names for new columns. Defaults to '_'.
val_separator	str	Separator to use between values when combining. Defaults to ' '.
missing_val	str	Value to replace missing data in specified columns. Defaults to 'NA'.
overall_category_name	str	Name for the overall category. Defaults to 'overall'.

Returns

Type	Description
pd.DataFrame	DataFrame with new subcategory columns.
list	List of all column names (original + generated).
pd.DataFrame	DataFrame with subcategory and its decimal proportion.

Example Usage

# Example code demonstrating usage
df, all_columns, proportions_df = generate_subcategories_with_proportions(df, columns=['col1', 'col2'], solo_columns=['col3'])

---

9. bin_continuous

Description

Bins continuous data in a specified column of a dataframe.

Parameters

Parameter	Type	Description
dataframe	pd.DataFrame	The input dataframe.
column_name	str	The name of the column containing continuous data to be binned.
bin_size	int	The size of each bin. Default is 10.
range_start	int	The starting value of the range for binning. Default is 0.

Returns

Type	Description
pd.DataFrame	The dataframe with an additional column for binned data.

Example Usage

# Example code demonstrating usage
binned_df = bin_continuous(df, column_name='age', bin_size=5)

---

10. load_latest_yyyymmdd_file

Description

Load the latest file from the specified directory based on its date.

Parameters

Parameter	Type	Description
directory	str	Path to the directory containing the files.
base_filename	str	Base name of the file.
file_extension	str	File extension including the dot (e.g., '.csv').
na_values	list or dict	Additional strings to recognize as NA/NaN.

Returns

Type	Description
pd.DataFrame	The loaded data.

Example Usage

# Example code demonstrating usage
df = load_latest_yyyymmdd_file("/path/to/directory", "data_", ".csv")
print(df.head())

---

11. remove_rows_with_na_threshold

Description

Removes rows from the dataframe that have a fraction of NA values greater than the specified threshold.

Parameters

Parameter	Type	Description
df	pd.DataFrame	The input dataframe.
stringified_ids	list	List of column names to consider for NA value calculation.
threshold	float	The fraction of NA values for a row to be removed. Default is 0.5.
save_starting_df	bool	Whether to save the initial dataframe to a CSV file. Default is False.

Returns

Type	Description
pd.DataFrame	The dataframe with rows removed based on the threshold.

Example Usage

# Example code demonstrating usage
cleaned_df = remove_rows_with_na_threshold(df, stringified_ids=['col1', 'col2'], threshold=0.3)

---

12. impute_na_in_columns

Description

Imputes NA values in columns with either the minimum or median value of the column.

Parameters

Parameter	Type	Description
df	pd.DataFrame	The input dataframe.
method	str	Method for imputation. Either 'min' or 'median'. Default is 'median'.

Returns

Type	Description
pd.DataFrame	The dataframe with NA values imputed.

Example Usage

# Example code demonstrating usage
imputed_df = impute_na_in_columns(df, method='median')

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Each function is meticulously crafted to handle specific tasks related to data manipulation, transformation, and analysis. This documentation provides a comprehensive understanding of the capabilities and usage of each function within the main.py script. Happy coding! 🎉

# ph_utils

ph_utils is a Python package that provides utility functions for data manipulation and analysis, particularly focused on working with pandas DataFrames.

Features

• Sum and sort DataFrame columns
• Generate binary threshold matrices
• Concatenate CSV files from directories
• Aggregate duplicates in DataFrames
• Generate subcategories from DataFrame columns
• Bin continuous data
• Load latest files based on date in filename
• Remove rows with NA values above a threshold
• Impute NA values in DataFrame columns

Installation

You can install ph_utils using pip:

pip install ph_utils

Usage

Here are some examples of how to use ph_utils:

import pandas as pd
from ph_utils import sum_and_sort_columns, binary_threshold_matrix_by_col, aggregate_duplicates

# Example 1: Sum and sort columns
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [0, 0, 0],
    'C': [4, 5, 6],
    'D': ['x', 'y', 'z']
})
result = sum_and_sort_columns(df)
print(result)

# Example 2: Create a binary threshold matrix
binary_df = binary_threshold_matrix_by_col(df, lower_threshold=25, upper_threshold=75)
print(binary_df)

# Example 3: Aggregate duplicates
aggregated_df = aggregate_duplicates(df, group_columns=['D'], numeric_method='mean')
print(aggregated_df)

For more detailed information on each function, please refer to the function docstrings in the source code.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.