EconKit - Function Descriptions

econkit is a Python library that provides various statistical and econometric analysis tools, including descriptive statistics, correlation matrices, and tests for stationarity and autocorrelation.

Functions

Descriptive Statistics

`descriptives(data)`

Computes descriptive statistics for each numeric column in a DataFrame.

Parameters:

data: pandas.DataFrame containing the data to be analyzed.

Returns:

None. Prints a summary table of the descriptive statistics.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df = pd.read_csv('your_data.csv')
ec.descriptives(df)

Correlation Matrix

`correlation(df, method='Pearson', p=False)`

Calculates and prints the correlation matrix and p-values for numeric columns in the provided DataFrame. Supports Pearson, Spearman, and Kendall correlation methods.

Parameters:

df: pandas.DataFrame containing the data to be analyzed.
method: str (optional). Method of correlation ('Pearson', 'Spearman', or 'Kendall'). Default is 'Pearson'.
p: bool (optional). If True, p-value matrix is also printed; if False, only the correlation matrix is printed.

Returns:

None. Prints the correlation matrix and optionally the p-value matrix.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df = pd.read_csv('your_data.csv')
ec.correlation(df, method='Spearman', p=True)

Augmented Dickey-Fuller (ADF) Test

`adf(dataframe, maxlag=None, regression='c', autolag='AIC', handle_na='drop')`

Performs the ADF test on each column in the DataFrame and returns a summary table.

Parameters:

dataframe: pandas.DataFrame containing the data to be tested.
maxlag: int (optional). Maximum number of lags to use.
regression: str (optional). Type of regression trend ('c', 'ct', 'ctt', 'nc'). Default is 'c'.
autolag: str (optional). Method for lag length selection ('AIC', 'BIC', 't-stat').
handle_na: str (optional). How to handle missing values ('drop' or 'fill').

Returns:

None. Prints a summary table of the ADF test results.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df = pd.read_csv('your_data.csv')
ec.adf(df)

KPSS Test

`kpss(dataframe, regression='c', nlags='auto', handle_na='drop')`

Performs the KPSS test on each column in the DataFrame and returns a summary table.

Parameters:

dataframe: pandas.DataFrame containing the data to be tested.
regression: str (optional). Type of regression trend ('c' or 'ct'). Default is 'c'.
nlags: str or int (optional). Number of lags to use. Default is 'auto'.
handle_na: str (optional). How to handle missing values ('drop' or 'fill').

Returns:

None. Prints a summary table of the KPSS test results.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df = pd.read_csv('your_data.csv')
ec.kpss(df)

Durbin-Watson Test

`dw(data)`

Performs the Durbin-Watson autocorrelation test and Ljung-Box test for each column of the dataset.

Parameters:

data: pandas.DataFrame containing the time series data.

Returns:

None. Prints a summary table of the Durbin-Watson test results.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df = pd.read_csv('your_data.csv')
ec.dw(df)

Normality Tests

`normality_tests(data, pvalue=False)`

Performs normality tests (Shapiro-Wilk and Kolmogorov-Smirnov) on each column of a DataFrame and displays results as a formatted table.

Parameters:

data: pandas.DataFrame containing the numerical data.
pvalue: bool (optional). If True, displays the p-value matrix along with the test results. Default is False.

Returns:

None. Prints the results matrix and optionally the p-value matrix.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df = pd.read_csv('your_data.csv')
ec.normality_tests(df, pvalue=True)

Random Number Generator

`random_number_generator(num_vars, num_random, distribution, mean=0, std_dev=1, seed=None, lower=None, upper=None, p=None, n_trials=None, lambda_param=None, start=None, stop=None, step=None, repeat_each=None, repeat_sequence=None)`

Generates random numbers based on the specified distribution and parameters.

Parameters:

num_vars: int. Number of variables (columns) to generate.
num_random: int. Number of random values per variable.
distribution: str. The distribution type ('normal', 'uniform', 'binomial', 'bernoulli', 'poisson', 'patterned').
mean: float. Mean for the normal distribution. Default is 0.
std_dev: float. Standard deviation for the normal distribution. Default is 1.
lower: float. Lower bound for the uniform distribution.
upper: float. Upper bound for the uniform distribution.
p: float. Probability for Bernoulli or Binomial distributions.
n_trials: int. Number of trials for the Binomial distribution.
lambda_param: float. Lambda parameter for the Poisson distribution.
start: float. Start value for the patterned distribution.
stop: float. Stop value for the patterned distribution.
step: float. Step size for the patterned distribution.
repeat_each: int. Number of times to repeat each number in the patterned distribution.
repeat_sequence: int. Number of times to repeat the entire sequence.

Returns:

pandas.DataFrame. A DataFrame containing the generated random numbers.

Example Usage:

from econkit import econometrics as ec

random_data = ec.random_number_generator(num_vars=2, num_random=10, distribution='normal', mean=0, std_dev=1, seed=42)
print(random_data)

Combine DataFrames

`combine_dataframes(column_name, *dataframes)`

Combines multiple DataFrames based on a specified column into a new DataFrame, aligning by the index.

Parameters:

column_name: str. The column to extract from each DataFrame.
*dataframes: Multiple pandas DataFrames passed as positional arguments.

Returns:

pandas.DataFrame. A new DataFrame combining the specified columns from all input DataFrames.

Example Usage:

import pandas as pd
from econkit import econometrics as ec

df1 = pd.read_csv('data1.csv')
df2 = pd.read_csv('data2.csv')

combined_df = ec.combine_dataframes('Close', df1, df2)
print(combined_df)

Financial Data Retrieval

`data(ticker_symbol, start_date, end_date, interval)`

Downloads financial data from Yahoo Finance and calculates daily returns.

Parameters:

ticker_symbol: str. The stock ticker symbol.
start_date: str. Start date in 'dd-mm-yyyy' format.
end_date: str. End date in 'dd-mm-yyyy' format.
interval: str. Data interval (e.g., '1d', '1wk', '1mo').

Returns:

pandas.DataFrame containing the stock data and calculated returns.

Example Usage:

from econkit import finance as f

start = '01-06-2024'
end = '07-06-2024'
interval = '1d'

df = f.data('AAPL', start, end, interval)
print(df.head())

Usage Notes

Ensure your data is clean and properly formatted before using these functions.
Some functions handle missing values; specify your preferred method using the handle_na parameter.
For time series analysis, ensure your data is indexed by date.

For further details, refer to the function docstrings in the source code or the examples provided above.

Metadata

Release files for econkit 0.5.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for econkit 0.5.8
File	Size	Uploaded	Provenance
econkit-0.5.8.tar.gz	13.4 kB	Feb 26, 2025	Without provenance	Details

Built distribution (wheel)

Table of built distributions (wheels) for econkit 0.5.8
File	Interpreter	ABI	Platform	Reset
econkit-0.5.8-py3-none-any.whl 12.8 kB Feb 26, 2025 Without provenance	Python 3	none	any	Details

Total release size: 26.2 kB

Release files / econkit-0.5.8.tar.gz

Download URL	econkit-0.5.8.tar.gz
Size	13.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`7d5b3f9113a27eed59dd4793e0c6c80fd52575e517c5dbd2b2605a11d08a502d`
BLAKE2b-256 checksum How to use checksums	`b7c877f4b78a99e2e5339ec9aec45e9fa2d093b26c7094f9cfaf76b353ae936d`
Upload date	Feb 26, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.2`

Release files / econkit-0.5.8-py3-none-any.whl

Download URL	econkit-0.5.8-py3-none-any.whl
Size	12.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`0f6bcf8e369cceba4290cba3ef45a3b48e2378e17c5344720908385de7a56c8a`
BLAKE2b-256 checksum How to use checksums	`18fbc9ba8a4eddfd0f02f46c823b0d4c00a83717c5d6d44f12497b28038163ec`
Upload date	Feb 26, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.2`

Security for econkit 0.5.8

Report project as malware

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.