{"id":21974827,"url":"https://github.com/wakolivotes/data-processing-and-preparation","last_synced_at":"2026-04-13T23:31:06.372Z","repository":{"id":114875646,"uuid":"400146299","full_name":"wakoliVotes/Data-Processing-and-Preparation","owner":"wakoliVotes","description":"In this tutorial, we use the Titanic Data (obtained from Kaggle) to illustrate key aspects of Data Processing and Preparation by relying on useful Python Libraries","archived":false,"fork":false,"pushed_at":"2021-10-31T19:34:26.000Z","size":195,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-22T23:41:39.885Z","etag":null,"topics":["data-science","datacleaning","jupyter-notebook","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wakoliVotes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-08-26T11:35:32.000Z","updated_at":"2023-05-01T02:26:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"8cae19a8-22cf-4130-98ed-1cf70c268b63","html_url":"https://github.com/wakoliVotes/Data-Processing-and-Preparation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/wakoliVotes/Data-Processing-and-Preparation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wakoliVotes%2FData-Processing-and-Preparation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wakoliVotes%2FData-Processing-and-Preparation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wakoliVotes%2FData-Processing-and-Preparation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wakoliVotes%2FData-Processing-and-Preparation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wakoliVotes","download_url":"https://codeload.github.com/wakoliVotes/Data-Processing-and-Preparation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wakoliVotes%2FData-Processing-and-Preparation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31775692,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-13T20:17:16.280Z","status":"ssl_error","status_checked_at":"2026-04-13T20:17:08.216Z","response_time":93,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","datacleaning","jupyter-notebook","python"],"created_at":"2024-11-29T15:48:06.173Z","updated_at":"2026-04-13T23:31:06.349Z","avatar_url":"https://github.com/wakoliVotes.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"### Illustrating Data Processing and Preparation with Python Libraries\n- In this tutorial, Titanic Data is used\n-  This is obtained from Kaggle (Link: https://www.kaggle.com/c/titanic) to illustrate aspects of Data Processing and Preparation\n- To complete this exercise, there is relying on useful Python Libraries\n\n#### Steps and Workings\n- First, there is need to import numpy and pandas, and then import the dataset for using\n\n\u003ccode\u003e\n\u003cpre\u003e\n        import numpy as np\n        import pandas as pd\n\n          TitanicData = pd.read_csv('D:/github/Python/train.csv')  # In this section, one replaces their specific path to read the data\n          TitanicData.head()                              # To visualize the data, the .head() is used to view the first five elements\n\u003c/pre\u003e\n\u003c/code\u003e\nThe image below shows the dataset's outlook\n\u003cimg src=\"https://github.com/danny-votez/Illustration-of-Data-Processing-and-Preparation-with-Python-Libraries/blob/main/datasetview.jpg\"\u003e\n\n- As seen from above, the head() keyword gives the first 5 elements. However, if you want to get a specific number of items, you can adjust by adding \"n=\"number of rows you need\"\". \n- This is illustrated below, where, to show 10 items, inside the head() function, n=10 is added, to specify the desired rows of data to show;\n\n\u003cpre\u003e\n          import numpy as np\n          import pandas as pd\n          \n          TitanicData = pd.read_csv('D:/github/Python/train.csv')\n          \n          # To get varied display rows, specify the value using the n item, e.g., n=8, or n=10, or n=30\n          \n          TitanicData.head(n=10)                        # Gives 10 rows of dataset\n\u003c/pre\u003e\n- The below image shows the visualization with 10 rows;\n\n![image](https://user-images.githubusercontent.com/77758884/130964763-26eef94b-93e8-4ceb-923a-981f7e590484.png)\n\n\n- Next, we can get more information about the Titanic data, using the info() tool, which will be coded as;\n\u003ccode\u003e\n\u003cpre\u003e\n    TitanicData.info()                                  # Gives a summarized information about our dataset\n\u003c/pre\u003e\n\u003c/code\u003e\n\n- As shown, the info gives the data types of the respective columns and number of entries.\n- Based on this representation, it is evident some columns have missing items, that can be examined later\n\n\u003cimg src=\"https://github.com/danny-votez/Illustration-of-Data-Processing-and-Preparation-with-Python-Libraries/blob/main/datasetinfo.jpg\"\u003e\n\n- We can use the describe function to get additional information on the dataset, including its mean, mode, standard deviation, percentiles, etc. This is achieved with the code below {remember to replace the file name, i.e., TitanicData, with your respective data file name}\n\n\u003ccode\u003e\n\u003cpre\u003e\n                TitanicData.describe()   # Gives additional descriptive statistics information about our dataset\n\u003c/pre\u003e\n\u003c/code\u003e\nBelow is the describe output for the used dataset\n\n![image](https://user-images.githubusercontent.com/77758884/130960566-77c5685f-e2a0-4c46-8d3a-74a6dd42fd5b.png)\n\n\n- As shown from above, we can see that for the TitanicData, basic descriptive statistics can help us understand the data more.\n- For example, from the data, the mean values are evident. In the sample, we can see the means of;\n\u003cpre\u003e\n        - Passengers, i.e., PassengerId (446.00),\n        - Survived (0.383838),\n        - Pclass (2.308642),\n        - Age (29.699118),\n        - SibSp (0.523008),\n        - Parch (0.381594) and,\n        - Fare (32.204208).\n\u003c/pre\u003e\n- NOTE: The above describe() function only gives columns that have continuous data elements, while there is excluding caterorical data.\n- However, if you need to include everything, you can add the \"(include='all')\" after writing the describe() function.\n- This is as illustrated below;\n\u003cpre\u003e\n            TitanicData.describe(include='all')        # Gives descriptive statistics for all columns\n\u003c/pre\u003e\nBelow is now the outlook for all columns in the dataset;\n![image](https://user-images.githubusercontent.com/77758884/130963069-7c442688-764b-4ee1-aa00-778bc5f6ff75.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwakolivotes%2Fdata-processing-and-preparation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwakolivotes%2Fdata-processing-and-preparation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwakolivotes%2Fdata-processing-and-preparation/lists"}