data_analysis (1).py

# -*- coding: utf-8 -*-
"""Data analysis.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1rgZ1KKaswBXKXYF_m5svCLsk6eBzLBT9
"""

!pip install streamlit

import pandas as pd
import streamlit as st
import seaborn as sns
import matplotlib.pyplot as plt

def load_and_scatterplot(year):

    file_path = f"Survey_results_sample_{year}.csv"

    try:
        data = pd.read_csv(file_path)
    except FileNotFoundError:
        st.error(f"File for the year {year} not found!")
        return None



    cols = ['Country', 'YearsCodePro', 'ConvertedCompYearly', 'DevType']
    filtered_data = data[cols].dropna()

    filtered_data['YearsCodePro'] = pd.to_numeric(filtered_data['YearsCodePro'], errors='coerce')
    filtered_data = filtered_data.dropna(subset=['YearsCodePro'])
    # Create a scatter plot for YearsCodePro vs ConvertedCompYearly, color-coded by DevType
    st.write(f"Scatter Plot: Years of Professional Coding Experience vs Yearly Compensation for {year}")

    plt.figure(figsize=(14, 8))

    scatter = sns.scatterplot(
        data=filtered_data,
        x='YearsCodePro',
        y='ConvertedCompYearly',
        hue='DevType',
        style='Country',
        palette='deep',
        s=100,
        alpha=0.6
    )

    scatter.legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Developer Type')

    plt.title(f'YearsCodePro vs ConvertedCompYearly ({year}), colored by DevType', fontsize=16)
    plt.xlabel('Years of Professional Coding Experience', fontsize=14)
    plt.ylabel('Yearly Compensation (USD)', fontsize=14)

    st.pyplot(plt)