yz

Includes various adwords elements diagnosis functions

To install: pip install yz

Overview

The yz package provides a collection of functions designed to assist in diagnosing and analyzing data related to AdWords and other similar datasets. The functions in this package help in understanding the structure and characteristics of data frames, performing statistical analysis, and formatting output for reporting and inspection.

Features

• Data Diagnostics: Analyze data frames to extract information about columns, such as data types, unique values, and non-null counts.
• Comparison and Filtering: Functions to filter and compare data based on specified conditions.
• Statistical Analysis: Aggregate and normalize data for detailed statistical insights.

Functions

diag_df(df)

Analyzes a DataFrame to provide a summary of each column including data type, a non-null example value, count of unique values, non-zero values, and non-NaN values.

Usage Example:

import pandas as pd
df = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': ['a', 'b', 'b', 'c']
})
summary = diag_df(df)
print(summary)

numof(logical_series)

Counts the number of True values in a logical series.

Usage Example:

import pandas as pd
data = pd.Series([True, False, True, True])
count = numof(data)
print(count)  # Output: 3

pr_numof(data, column=None, op=ge, comp_val=0, str_format='sparse', op2str=None)

Prints the number of elements in a specified column of a DataFrame that meet a comparison condition.

Usage Example:

import pandas as pd
df = pd.DataFrame({'Age': [25, 30, 35, 40]})
pr_numof(df, column='Age', op=ge, comp_val=30)

cols_that_are_of_the_type(df, type_spec)

Returns a list of columns where the type of the first element matches the specified type or meets a condition defined by a function.

Usage Example:

import pandas as pd
df = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [25, 30]
})
cols = cols_that_are_of_the_type(df, int)
print(cols)  # Output: ['age']

get_unique(d, cols=None)

Returns a DataFrame with unique rows based on specified columns.

Usage Example:

import pandas as pd
df = pd.DataFrame({
    'A': [1, 1, 2],
    'B': [2, 2, 2]
})
unique_df = get_unique(df)
print(unique_df)

print_unique_counts(d)

Prints the count of unique values for each column in the DataFrame.

Usage Example:

import pandas as pd
df = pd.DataFrame({
    'A': [1, 1, 2],
    'B': ['x', 'y', 'x']
})
print_unique_counts(df)

mk_fanout_score_df(df, fromVars, toVars, statVars=None, keep_statVars=False)

Creates a DataFrame that scores fan-out relationships between variables.

Usage Example:

import pandas as pd
df = pd.DataFrame({
    'Company': ['A', 'A', 'B'],
    'Product': ['X', 'Y', 'X'],
    'Sales': [100, 150, 200]
})
score_df = mk_fanout_score_df(df, fromVars=['Company'], toVars=['Product'])
print(score_df)

Installation

To install the yz package, run the following command:

pip install yz

This package is essential for data scientists and analysts working with complex datasets, providing tools to simplify data diagnostics and analysis.