Inspiration

The analysis never over. Life is about huge data and manipulation.

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Context

I have use Pokymon dataset to analyze further through pandas, This dataset and jupyter notebook can be use to understand how pandas work, all steps are fairly described in markdown sections.

Importing data

POKYMON data which is available publically on kaggle, you can use any dataset to do further analysis or practice.

Acknowledgements

I would like to thanks keith galli to initiate such lovely opportunity for beginners, to understand python and its libraries in a very simple.

Inspiration

Further analysis can be done on various outcomes, as data is everchanging category.

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Libraries Import:

Importing necessary libraries such as pandas, seaborn, matplotlib, scikit-learn's KMeans, and warnings. Data Loading and Exploration:

Reading a dataset named "Mall_Customers.csv" into a pandas DataFrame (df). Displaying the first few rows of the dataset using df.head(). Conducting univariate analysis by calculating descriptive statistics with df.describe(). Univariate Analysis:

Visualizing the distribution of the 'Annual Income (k$)' column using sns.distplot. Looping through selected columns ('Age', 'Annual Income (k$)', 'Spending Score (1-100)') and plotting individual distribution plots. Bivariate Analysis:

Creating a scatter plot for 'Annual Income (k$)' vs 'Spending Score (1-100)' using sns.scatterplot. Generating a pair plot for selected columns with gender differentiation using sns.pairplot. Gender-Based Analysis:

Grouping the data by 'Gender' and calculating the mean for selected columns. Computing the correlation matrix for the grouped data and visualizing it using a heatmap. Univariate Clustering:

Applying KMeans clustering with 3 clusters based on 'Annual Income (k$)' and adding the 'Income Cluster' column to the DataFrame. Plotting the elbow method to determine the optimal number of clusters. Bivariate Clustering:

Applying KMeans clustering with 5 clusters based on 'Annual Income (k$)' and 'Spending Score (1-100)' and adding the 'Spending and Income Cluster' column. Plotting the elbow method for bivariate clustering and visualizing the cluster centers on a scatter plot. Displaying a normalized cross-tabulation between 'Spending and Income Cluster' and 'Gender'. Multivariate Clustering:

Performing multivariate clustering by creating dummy variables, scaling selected columns, and applying KMeans clustering. Plotting the elbow method for multivariate clustering. Result Saving:

Saving the modified DataFrame with cluster information to a CSV file named "Result.csv". Saving the multivariate clustering plot as an image file ("Multivariate_figure.png").

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Project Description:

Title: Pandas Data Manipulation and File Conversion

Overview: This project aims to demonstrate the basic functionalities of Pandas, a powerful data manipulation library in Python. In this project, we will create a DataFrame, perform some data manipulation operations using Pandas, and then convert the DataFrame into both Excel and CSV formats.

Key Objectives:

1. DataFrame Creation: Utilize Pandas to create a DataFrame with sample data.
2. Data Manipulation: Perform basic data manipulation tasks such as adding columns, filtering data, and performing calculations.
3. File Conversion: Convert the DataFrame into Excel (.xlsx) and CSV (.csv) file formats.

Tools and Libraries Used:

• Python
• Pandas

Project Implementation:

1. DataFrame Creation:

   • Import the Pandas library.
   • Create a DataFrame using either a dictionary, a list of dictionaries, or by reading data from an external source like a CSV file.
   • Populate the DataFrame with sample data representing various data types (e.g., integer, float, string, datetime).

2. Data Manipulation:

   • Add new columns to the DataFrame representing derived data or computations based on existing columns.
   • Filter the DataFrame to include only specific rows based on certain conditions.
   • Perform basic calculations or transformations on the data, such as aggregation functions or arithmetic operations.

3. File Conversion:

   • Utilize Pandas to convert the DataFrame into an Excel (.xlsx) file using the to_excel() function.
   • Convert the DataFrame into a CSV (.csv) file using the to_csv() function.
   • Save the generated files to the local file system for further analysis or sharing.

Expected Outcome:

Upon completion of this project, you will have gained a fundamental understanding of how to work with Pandas DataFrames, perform basic data manipulation tasks, and convert DataFrames into different file formats. This knowledge will be valuable for data analysis, preprocessing, and data export tasks in various data science and analytics projects.

Conclusion:

The Pandas library offers powerful tools for data manipulation and file conversion in Python. By completing this project, you will have acquired essential skills that are widely applicable in the field of data science and analytics. You can further extend this project by exploring more advanced Pandas functionalities or integrating it into larger data processing pipelines.in this data we add number of data and make that data a data frame.and save in single excel file as different sheet name and then convert that excel file in csv file .

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LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

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Vezora/Tested-188k-Python-Alpaca: Functional Python Code Dataset

188k Functional Python Code Samples

By Vezora (From Huggingface) [source]

About this dataset

The Vezora/Tested-188k-Python-Alpaca dataset is a comprehensive collection of functional Python code samples, specifically designed for training and analysis purposes. With 188,000 samples, this dataset offers an extensive range of examples that cater to the research needs of Python programming enthusiasts.

This valuable resource consists of various columns, including input, which represents the input or parameters required for executing the Python code sample. The instruction column describes the task or objective that the Python code sample aims to solve. Additionally, there is an output column that showcases the resulting output generated by running the respective Python code.

By utilizing this dataset, researchers can effectively study and analyze real-world scenarios and applications of Python programming. Whether for educational purposes or development projects, this dataset serves as a reliable reference for individuals seeking practical examples and solutions using Python

How to use the dataset

The Vezora/Tested-188k-Python-Alpaca dataset is a comprehensive collection of functional Python code samples, containing 188,000 samples in total. This dataset can be a valuable resource for researchers and programmers interested in exploring various aspects of Python programming.

Contents of the Dataset

The dataset consists of several columns:

• output: This column represents the expected output or result that is obtained when executing the corresponding Python code sample.
• instruction: It provides information about the task or instruction that each Python code sample is intended to solve.
• input: The input parameters or values required to execute each Python code sample.

Exploring the Dataset

To make effective use of this dataset, it is essential to understand its structure and content properly. Here are some steps you can follow:

• Importing Data: Load the dataset into your preferred environment for data analysis using appropriate tools like pandas in Python.

import pandas as pd

# Load the dataset
df = pd.read_csv('train.csv')

• Understanding Column Names: Familiarize yourself with the column names and their meanings by referring to the provided description.

# Display column names
print(df.columns)

• Sample Exploration: Get an initial understanding of the data structure by examining a few random samples from different columns.

# Display random samples from 'output' column
print(df['output'].sample(5))

• Analyzing Instructions: Analyze different instructions or tasks present in the 'instruction' column to identify specific areas you are interested in studying or learning about.

# Count unique instructions and display top ones with highest occurrences
instruction_counts = df['instruction'].value_counts()
print(instruction_counts.head(10))

Potential Use Cases

The Vezora/Tested-188k-Python-Alpaca dataset can be utilized in various ways:

• Code Analysis: Analyze the code samples to understand common programming patterns and best practices.
• Code Debugging: Use code samples with known outputs to test and debug your own Python programs.
• Educational Purposes: Utilize the dataset as a teaching tool for Python programming classes or tutorials.
• Machine Learning Applications: Train machine learning models to predict outputs based on given inputs.

Remember that this dataset provides a plethora of diverse Python coding examples, allowing you to explore different

Research Ideas

• Code analysis: Researchers and developers can use this dataset to analyze various Python code samples and identify patterns, best practices, and common mistakes. This can help in improving code quality and optimizing performance.
• Language understanding: Natural language processing techniques can be applied to the instruction column of this dataset to develop models that can understand and interpret natural language instructions for programming tasks.
• Code generation: The input column of this dataset contains the required inputs for executing each Python code sample. Researchers can build models that generate Python code based on specific inputs or task requirements using the examples provided in this dataset. This can be useful in automating repetitive programming tasks o...

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