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78c5ddd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import gradio as gr
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load dataset
URL = "https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/master/employees.csv"
df = pd.read_csv(URL)
# Pre-processing / cleaning
df = df.copy()
# convert HIRE_DATE to datetime
df["HIRE_DATE"] = pd.to_datetime(df["HIRE_DATE"], errors="coerce", dayfirst=True)
# convert SALARY to numeric (if needed)
df["SALARY"] = pd.to_numeric(df["SALARY"], errors="coerce")
# drop rows with missing crucial fields
df = df.dropna(subset=["EMPLOYEE_ID","FIRST_NAME","LAST_NAME","HIRE_DATE","SALARY"])
# Visualization functions
def preview_data(n_rows: int):
return df.head(n_rows)
def plot_vis(choice: str):
plt.figure(figsize=(8,5))
if choice == "Salary Distribution":
sns.histplot(df["SALARY"], bins=30, kde=True)
plt.title("Distribution of Salaries")
elif choice == "Hires Over Time":
hires = df.set_index("HIRE_DATE").resample("Y").size()
sns.lineplot(x=hires.index.year, y=hires.values)
plt.title("Number of Hires Over Years")
plt.xlabel("Year")
plt.ylabel("Count")
elif choice == "Top Job IDs by Count":
top_jobs = df["JOB_ID"].value_counts().head(10)
sns.barplot(x=top_jobs.values, y=top_jobs.index)
plt.title("Top 10 JOB_IDs by Employee Count")
plt.xlabel("Count")
plt.ylabel("JOB_ID")
elif choice == "Salary vs Department":
dept_salary = df.groupby("DEPARTMENT_ID")["SALARY"].mean().sort_values(ascending=False).head(10)
sns.barplot(x=dept_salary.values, y=dept_salary.index)
plt.title("Average Salary by Department (Top10)")
plt.xlabel("Avg Salary")
plt.ylabel("Department ID")
plt.tight_layout()
return plt.gcf()
# Build Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# 👥 Employee Data Explorer")
gr.Markdown("Visualize and explore the employees dataset.")
with gr.Tab("📋 Data Preview"):
n = gr.Slider(5, 100, value=10, step=5, label="Number of rows to show")
table = gr.Dataframe(label="Dataset Preview")
n.change(preview_data, inputs=n, outputs=table)
with gr.Tab("📊 Visualizations"):
vis_choice = gr.Radio(
["Salary Distribution", "Hires Over Time", "Top Job IDs by Count", "Salary vs Department"],
label="Select a visualization"
)
plot = gr.Plot(label="Visualization")
vis_choice.change(plot_vis, inputs=vis_choice, outputs=plot)
gr.Markdown("Built with Gradio + seaborn + matplotlib")
demo.launch()
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