Metadata-Version: 2.1
Name: pretty_data
Version: 0.1
Author: Hayati YURTOĞLU
Author-email: hayatiyurtoglu71@gmail.com
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Description-Content-Type: text/markdown
License-File: LICENSE.txt


---

# General Info and Manipulator Classes

## Description
This Python module provides two classes, `GeneralInfo` and `Manipulator`, for data preprocessing tasks using the Pandas library. These classes offer functionalities to obtain general information about a DataFrame and perform various manipulations on it.

## GeneralInfo Class
The `GeneralInfo` class contains methods to retrieve general information about a DataFrame such as head and tail, descriptive statistics, information about duplicates and null values, and processing object data types.

### Methods
- `head_and_tail()`: Displays the head and tail of the DataFrame.
- `describe_and_info()`: Provides information using the `describe()` and `info()` methods of Pandas.
- `duplicated_and_null()`: Identifies duplicated data and null values in the DataFrame.
- `object_processor()`: Processes object data types, including finding index max and min values, unique values, and value counts.
- `report_general_info()`: Executes all the above methods to provide a comprehensive report on the DataFrame.

## Manipulator Class
The `Manipulator` class inherits from the `GeneralInfo` class and extends its functionalities by including operations for data manipulation such as regex-based selection and datetime parsing.

### Methods
- `select_w_regex(only_numbers, only_strings, specific, s_parameter, value)`: Selects either strings or numbers from the DataFrame using regular expressions. Can also perform specific regex-based replacements.
- `datetime_parser(column_name, as_index, parse_and_sep)`: Parses datetime values in a specified column and optionally sets them as the index or separates them into year, month, and day columns.
- `search_string(column_name, keywords, method)`: Searches for strings containing specified keywords in a given column using the `str.contains` method.

---

Now, let's create the Turkish version:

---

# General Info ve Manipulator SÄ±nÄ±flarÄ±

## AÃ§Ä±klama
Bu Python modÃ¼lÃ¼, Pandas kÃ¼tÃ¼phanesini kullanarak veri Ã¶n iÅŸleme gÃ¶revleri iÃ§in iki sÄ±nÄ±f saÄŸlar: `GeneralInfo` ve `Manipulator`. Bu sÄ±nÄ±flar, bir DataFrame hakkÄ±nda genel bilgileri almak ve Ã§eÅŸitli manipÃ¼lasyonlar yapmak iÃ§in iÅŸlevsellikler sunar.

## GeneralInfo SÄ±nÄ±fÄ±
`GeneralInfo` sÄ±nÄ±fÄ±, DataFrame hakkÄ±nda genel bilgileri almak iÃ§in baÅŸ ve kuyruk, aÃ§Ä±klayÄ±cÄ± istatistikler, Ã§oÄŸaltÄ±lmÄ±ÅŸ veriler ve boÅŸ deÄŸerler hakkÄ±nda bilgileri alma ve nesne veri tiplerini iÅŸleme gibi yÃ¶ntemler iÃ§erir.

### YÃ¶ntemler
- `head_and_tail()`: DataFrame'in baÅŸÄ±nÄ± ve kuyruÄŸunu gÃ¶rÃ¼ntÃ¼ler.
- `describe_and_info()`: Pandas'Ä±n `describe()` ve `info()` yÃ¶ntemlerini kullanarak bilgi saÄŸlar.
- `duplicated_and_null()`: DataFrame'deki Ã§oÄŸaltÄ±lmÄ±ÅŸ verileri ve boÅŸ deÄŸerleri belirler.
- `object_processor()`: Nesne veri tiplerini iÅŸler, en bÃ¼yÃ¼k ve en kÃ¼Ã§Ã¼k dizin deÄŸerlerini, benzersiz deÄŸerleri ve deÄŸer sayÄ±larÄ±nÄ± bulur.
- `report_general_info()`: YukarÄ±daki tÃ¼m yÃ¶ntemleri yÃ¼rÃ¼terek DataFrame hakkÄ±nda kapsamlÄ± bir rapor saÄŸlar.

## Manipulator SÄ±nÄ±fÄ±
`Manipulator` sÄ±nÄ±fÄ±, `GeneralInfo` sÄ±nÄ±fÄ±ndan miras alÄ±r ve regex tabanlÄ± seÃ§im ve tarih zamanÄ± ayrÄ±ÅŸtÄ±rma gibi veri manipÃ¼lasyonlarÄ± iÃ§in iÅŸlevselliÄŸini geniÅŸletir.

### YÃ¶ntemler
- `select_w_regex(only_numbers, only_strings, specific, s_parameter, value)`: DataFrame'den yalnÄ±zca dize veya sayÄ±larÄ± seÃ§er ve dÃ¼zenli ifadeler kullanÄ±r. Belirli bir regex tabanlÄ± deÄŸiÅŸtirme yapabilir.
- `datetime_parser(column_name, as_index, parse_and_sep)`: Belirtilen sÃ¼tundaki tarih saat deÄŸerlerini ayrÄ±ÅŸtÄ±rÄ±r ve isteÄŸe baÄŸlÄ± olarak bunlarÄ± dizin olarak ayarlar veya yÄ±l, ay ve gÃ¼n sÃ¼tunlarÄ±na ayÄ±rÄ±r.
- `search_string(column_name, keywords, method)`: Belirtilen sÃ¼tunda belirtilen anahtar kelimeleri iÃ§eren dizeleri arar, `str.contains` yÃ¶ntemini kullanÄ±r.

---


---


#### EDA Class

The EDA (Exploratory Data Analysis) class is designed to facilitate the initial exploration of your data. This class is equipped with various methods to perform one-by-one analysis, bivariate analysis, and plotting operations.

#### Features

1. **Desktop Finder**
   - Method: `find_system_desktop()`
   - Description: This method finds the desktop path of the system where the code is executed.

2. **One-by-One Countplots**
   - Method: `one_by_one_countplots()`
   - Description: Generates countplots for each column in the DataFrame and saves the plots on the desktop.

3. **One-by-One Histplots**
   - Method: `one_by_one_histplot()`
   - Description: Generates histogram plots for each column in the DataFrame and saves the plots on the desktop.

4. **Bivariate Analysis**
   - Method: `bivariate_analysis(x: str)`
   - Description: Performs bivariate analysis including bar plots and regression plots for correlation analysis.

5. **Correlation Analyzer**
   - Method: `corr_analyzer()`
   - Description: Analyzes correlation among numeric columns in the DataFrame and generates a correlation heatmap.

6. **General EDA Report**
   - Method: `give_eda_report(x: str)`
   - Description: Runs all the methods in the class to generate a comprehensive EDA report including countplots, histplots, bivariate analysis, and correlation analysis.

#### Usage

Instantiate the `Eda` class with your DataFrame, and then call the desired method(s) to perform exploratory data analysis.


------

#### EDA SÄ±nÄ±fÄ±

EDA (KeÅŸifsel Veri Analizi) sÄ±nÄ±fÄ±, verilerinizin baÅŸlangÄ±Ã§ keÅŸfini kolaylaÅŸtÄ±rmak iÃ§in tasarlanmÄ±ÅŸtÄ±r. Bu sÄ±nÄ±f, tek tek analiz, iki deÄŸiÅŸkenli analiz ve Ã§izim iÅŸlemleri gerÃ§ekleÅŸtirmek iÃ§in Ã§eÅŸitli yÃ¶ntemlerle donatÄ±lmÄ±ÅŸtÄ±r.

#### Ã–zellikler

1. **MasaÃ¼stÃ¼ Bulucu**
   - Metod: `find_system_desktop()`
   - AÃ§Ä±klama: Bu metod, kodun Ã§alÄ±ÅŸtÄ±ÄŸÄ± sistemin masaÃ¼stÃ¼ yolunu bulur.

2. **Tek Tek SayÄ±m Grafikleri**
   - Metod: `one_by_one_countplots()`
   - AÃ§Ä±klama: DataFrame'deki her sÃ¼tun iÃ§in sayÄ±m grafikleri oluÅŸturur ve grafikleri masaÃ¼stÃ¼ne kaydeder.

3. **Tek Tek Histogram Grafikleri**
   - Metod: `one_by_one_histplot()`
   - AÃ§Ä±klama: DataFrame'deki her sÃ¼tun iÃ§in histogram grafikleri oluÅŸturur ve grafikleri masaÃ¼stÃ¼ne kaydeder.

4. **Ä°ki DeÄŸiÅŸkenli Analiz**
   - Metod: `bivariate_analysis(x: str)`
   - AÃ§Ä±klama: Korelasyon analizi iÃ§in Ã§ubuk grafikler ve regresyon grafikleri de dahil olmak Ã¼zere iki deÄŸiÅŸkenli analiz yapar.

5. **Korelasyon Analizcisi**
   - Metod: `corr_analyzer()`
   - AÃ§Ä±klama: DataFrame'deki sayÄ±sal sÃ¼tunlar arasÄ±ndaki korelasyonu analiz eder ve bir korelasyon Ä±sÄ± haritasÄ± oluÅŸturur.

6. **Genel EDA Raporu**
   - Metod: `give_eda_report(x: str)`
   - AÃ§Ä±klama: SÄ±nÄ±ftaki tÃ¼m metodlarÄ± Ã§alÄ±ÅŸtÄ±rarak, sayÄ±m grafikleri, histogram grafikleri, iki deÄŸiÅŸkenli analiz ve korelasyon analizi iÃ§eren kapsamlÄ± bir EDA raporu oluÅŸturur.

#### KullanÄ±m

`Eda` sÄ±nÄ±fÄ±nÄ± DataFrame'inizle birlikte baÅŸlatÄ±n ve ardÄ±ndan keÅŸifsel veri analizi yapmak iÃ§in istenen metod(lar)Ä± Ã§aÄŸÄ±rÄ±n.




