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import streamlit as st
import pandas as pd
import numpy as np
import io
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import seaborn as sns
import base64
def show_general_data_statistics():
if "data" in st.session_state:
data = st.session_state["data"]
num_var = len(data.columns)
num_rows = len(data)
missing_cells = data.isnull().sum().sum()
missing_cells_percent = (missing_cells / (data.size)) * 100
duplicate_rows = data.duplicated().sum()
duplicate_rows_percent = (duplicate_rows / num_rows) * 100
var_types = data.dtypes.value_counts()
st.write("### General Data Statistics:")
st.write(f"- **Number of Variables:** {num_var}")
st.write(f"- **Number of Rows:** {num_rows}")
st.write(f"- **Missing Cells:** {missing_cells}")
st.write(f"- **Missing Cells (%):** {missing_cells_percent:.2f}%")
st.write(f"- **Duplicate Rows:** {duplicate_rows}")
st.write(f"- **Duplicate Rows (%):** {duplicate_rows_percent:.2f}%")
st.write("#### Variable Types:")
st.write(var_types)
else:
st.warning("Please upload a dataset first.")
def describe_data():
st.title("Describe Data")
if "data" in st.session_state:
data = st.session_state["data"]
st.write("Dataset Description:")
st.write(data.describe())
else:
st.warning("Please upload a dataset first.")
def info_data():
st.title("Dataset Info")
if "data" in st.session_state:
data = st.session_state["data"]
buffer = io.StringIO()
data.info(buf=buffer)
info = buffer.getvalue()
st.text(info)
else:
st.warning("Please upload a dataset first.")
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