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app.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from wordcloud import WordCloud
+
+# Set page title
+st.set_page_config(page_title='Layoffs Data Visualization')
+
+# Load the CSV file
+@st.cache_data  # Caching the data for improved performance
+def load_data(file_path):
+    data = pd.read_csv(file_path)
+    return data
+
+# Main function
+def main():
+    # Page title and description
+    st.title("Layoffs Data Visualization")
+    st.write("Explore and visualize layoffs data")
+
+    # Load the data
+    data = load_data("Data/layoffs_data.csv")
+
+    # Sidebar options
+    st.sidebar.title("Options")
+    selected_option = st.sidebar.selectbox("Select an option", ("Data Preview", "Unique Industries",
+                                                                "Layoffs Count by Company",
+                                                                "Layoffs Count by Country",
+                                                                "Bar plot of layoffs by industry",
+                                                                "Line plot of layoffs over time",
+                                                                "Word Cloud of Industries",
+                                                                "Histogram of Laid Off Counts",
+                                                                "Scatter Plot",
+                                                                "List of Companies and Laid Off Employees"))
+
+    # Data exploration
+    if selected_option == "Data Preview":
+        st.subheader("Data Preview")
+        num_rows = st.number_input("Number of rows to display", min_value=1, value=len(data))
+        st.dataframe(data.head(num_rows))
+
+    elif selected_option == "Unique Industries":
+        st.subheader("Unique Industries")
+        unique_industries = data['Industry'].unique()
+        st.write(unique_industries)
+
+    elif selected_option == "Layoffs Count by Company":
+        st.subheader("Layoffs Count by Company")
+        company_counts = data['Company'].value_counts()
+        st.write(company_counts)
+
+    elif selected_option == "Layoffs Count by Country":
+        st.subheader("Layoffs Count by Country")
+        country_counts = data['Country'].value_counts()
+        st.write(country_counts)
+
+    # Data visualization
+    elif selected_option == "Bar plot of layoffs by industry":
+        st.subheader("Bar plot of layoffs by industry")
+        industry_counts = data['Industry'].value_counts()
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.barplot(x=industry_counts.index, y=industry_counts.values, ax=ax)
+        ax.set_xticklabels(industry_counts.index, rotation=90)
+        st.pyplot(fig)
+
+    elif selected_option == "Line plot of layoffs over time":
+        st.subheader("Line plot of layoffs over time")
+        data['Date'] = pd.to_datetime(data['Date'])
+        layoffs_over_time = data.groupby('Date').sum()['Laid_Off_Count']
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.lineplot(x=layoffs_over_time.index, y=layoffs_over_time.values, ax=ax)
+        ax.set_xlabel("Date")
+        ax.set_ylabel("Layoffs Count")
+        st.pyplot(fig)
+
+    elif selected_option == "Word Cloud of Industries":
+        st.subheader("Word Cloud of Industries")
+        industry_text = ' '.join(data['Industry'].dropna().values.tolist())
+        wordcloud = WordCloud(width=800, height=400).generate(industry_text)
+        plt.figure(figsize=(10, 6))
+        plt.imshow(wordcloud, interpolation='bilinear')
+        plt.axis("off")
+        st.pyplot(plt)
+
+    elif selected_option == "Histogram of Laid Off Counts":
+        st.subheader("Histogram of Laid Off Counts")
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.histplot(data['Laid_Off_Count'], kde=True, ax=ax)
+        st.pyplot(fig)
+
+    elif selected_option == "Scatter Plot":
+        st.subheader("Scatter Plot")
+        fig = px.scatter(data, x='Funds_Raised', y='Laid_Off_Count', color='Stage')
+        st.plotly_chart(fig)
+
+    elif selected_option == "List of Companies and Laid Off Employees":
+        st.subheader("List of Companies and Laid Off Employees")
+        company_employees = data.groupby('Company').sum()['Laid_Off_Count'].reset_index()
+        st.dataframe(company_employees)
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+altair==5.0.1
+attrs==23.1.0
+blinker==1.6.2
+branca==0.6.0
+cachetools==5.3.1
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+colorama==0.4.6
+contourpy==1.0.7
+cycler==0.11.0
+decorator==5.1.1
+folium==0.14.0
+fonttools==4.39.4
+gitdb==4.0.10
+GitPython==3.1.31
+idna==3.4
+importlib-metadata==6.6.0
+Jinja2==3.1.2
+joblib==1.2.0
+jsonschema==4.17.3
+kiwisolver==1.4.4
+markdown-it-py==2.2.0
+MarkupSafe==2.1.3
+matplotlib==3.7.1
+mdurl==0.1.2
+nltk==3.8.1
+numpy==1.24.3
+packaging==23.1
+pandas==2.0.2
+Pillow==9.5.0
+plotly==5.15.0
+protobuf==4.23.2
+pyarrow==12.0.0
+pydeck==0.8.1b0
+Pygments==2.15.1
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+pytz==2023.3
+pytz-deprecation-shim==0.1.0.post0
+regex==2023.6.3
+requests==2.31.0
+rich==13.4.1
+seaborn==0.12.2
+six==1.16.0
+smmap==5.0.0
+streamlit==1.23.1
+streamlit-folium==0.12.0
+tenacity==8.2.2
+textblob==0.17.1
+toml==0.10.2
+toolz==0.12.0
+tornado==6.3.2
+tqdm==4.65.0
+typing_extensions==4.6.3
+tzdata==2023.3
+tzlocal==4.3
+urllib3==2.0.3
+validators==0.20.0
+watchdog==3.0.0
+wordcloud==1.9.2
+zipp==3.15.0